From 66a0e8b0d9f3dd821bb315c9a7ffb8a9b5264103 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Fri, 10 Oct 2025 14:32:27 +1000 Subject: [PATCH 01/41] Implemented dataset with a summariser to connect expert-layman terms and finished make_datasets --- recognition/Project13-TristanGreen/dataset.py | 207 ++++++++++++++++++ 1 file changed, 207 insertions(+) create mode 100644 recognition/Project13-TristanGreen/dataset.py diff --git a/recognition/Project13-TristanGreen/dataset.py b/recognition/Project13-TristanGreen/dataset.py new file mode 100644 index 000000000..1f249392d --- /dev/null +++ b/recognition/Project13-TristanGreen/dataset.py @@ -0,0 +1,207 @@ +# dataset.py +from __future__ import annotations +import csv, json, os +from typing import Dict, List, Optional, Tuple + +import torch +from torch.utils.data import Dataset +try: + from datasets import load_dataset # optional + HF_AVAILABLE = True +except Exception: + HF_AVAILABLE = False + +from transformers import AutoTokenizer, DataCollatorForSeq2Seq + + +class SummarisationDataset(Dataset): + """ + Flexible dataset: supports HF datasets, CSV, or JSONL. + Expects two text fields: `input_col` (expert report) and `target_col` (lay summary). + """ + def __init__( + self, + records: List[Dict[str, str]], + tokenizer: AutoTokenizer, + max_input_len: int = 1024, + max_target_len: int = 256, + add_prefix: bool = True, + prefix_text: str = "summarize: ", + strip_empty: bool = True, + ): + self.records = [] + for r in records: + src = (r.get("input") or r.get("report") or r.get("source") or r.get("text") or "").strip() + tgt = (r.get("target") or r.get("summary") or r.get("lay_summary") or "").strip() + if strip_empty and (not src or not tgt): + continue + self.records.append({"input": src, "target": tgt}) + + if len(self.records) == 0: + raise ValueError("No usable records found (empty inputs/targets).") + + self.tok = tokenizer + self.max_in = max_input_len + self.max_tgt = max_target_len + self.add_prefix = add_prefix + self.prefix_text = prefix_text + + def __len__(self) -> int: + return len(self.records) + + def __getitem__(self, idx: int): + ex = self.records[idx] + src = (self.prefix_text + ex["input"]) if self.add_prefix else ex["input"] + tgt = ex["target"] + + model_inputs = self.tok( + src, + max_length=self.max_in, + truncation=True, + padding=False, + ) + labels = self.tok( + text_target=tgt, # <-- modern API + max_length=self.max_tgt, + truncation=True, + padding=False, + ) + model_inputs["labels"] = labels["input_ids"] + return {k: torch.tensor(v) for k, v in model_inputs.items()} + + +def load_local_csv( + path: str, + input_col: str = "report", + target_col: str = "summary", + delimiter: str = ",", +) -> List[Dict[str, str]]: + rows: List[Dict[str, str]] = [] + with open(path, "r", encoding="utf-8") as f: + reader = csv.DictReader(f, delimiter=delimiter) + for r in reader: + rows.append({"input": r.get(input_col, ""), "target": r.get(target_col, "")}) + return rows + + +def load_local_jsonl( + path: str, + input_col: str = "report", + target_col: str = "summary", +) -> List[Dict[str, str]]: + rows: List[Dict[str, str]] = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + obj = json.loads(line) + rows.append({"input": obj.get(input_col, ""), "target": obj.get(target_col, "")}) + return rows + + +def load_biolaysumm_hf( + dataset_name: str = "BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track", + split: str = "train", + input_col: str = "report", + target_col: str = "summary", +) -> List[Dict[str, str]]: + if not HF_AVAILABLE: + raise RuntimeError("`datasets` not installed. Use local CSV/JSONL or install `datasets`.") + ds = load_dataset(dataset_name, split=split) + rows = [] + for r in ds: + rows.append({"input": r.get(input_col, ""), "target": r.get(target_col, "")}) + return rows + + +def make_datasets( + tokenizer_name: str = "google/flan-t5-base", + train_source: Tuple[str, str] = ("local_jsonl", "train.jsonl"), + val_source: Optional[Tuple[str, str]] = ("local_jsonl", "val.jsonl"), + input_col: str = "report", + target_col: str = "summary", + max_input_len: int = 1024, + max_target_len: int = 256, + add_prefix: bool = True, + prefix_text: str = "summarize: ", +): + tok = AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True) + + def _load(kind: str, arg: str) -> List[Dict[str, str]]: + if kind == "local_csv": + return load_local_csv(arg, input_col, target_col) + elif kind == "local_jsonl": + return load_local_jsonl(arg, input_col, target_col) + elif kind == "hf": + # arg should be split name if using HF + return load_biolaysumm_hf(split=arg, input_col=input_col, target_col=target_col) + else: + raise ValueError(f"Unknown source kind: {kind}") + + train_kind, train_arg = train_source + train_rows = _load(train_kind, train_arg) + + val_rows = [] + if val_source: + val_kind, val_arg = val_source + val_rows = _load(val_kind, val_arg) + + train_ds = SummarisationDataset( + train_rows, tok, max_input_len, max_target_len, add_prefix, prefix_text + ) + val_ds = SummarisationDataset( + val_rows, tok, max_input_len, max_target_len, add_prefix, prefix_text + ) if val_rows else None + + collator = Seq2SeqCollatorFast(tok, label_pad_token_id=-100, pad_to_multiple_of=None) # or 8/16 if you want alignment + return tok, train_ds, val_ds, collator + + +from torch.nn.utils.rnn import pad_sequence + +class Seq2SeqCollatorFast: + """ + Fast collator that pads inputs/labels with pure torch ops. + Avoids the slow path in HF's DataCollatorForSeq2Seq that triggers + """ + def __init__(self, tokenizer, label_pad_token_id=-100, pad_to_multiple_of=None): + self.tok = tokenizer + self.label_pad_token_id = label_pad_token_id + self.pad_to_multiple_of = pad_to_multiple_of + + def _maybe_pad_to_multiple(self, tensor, pad_value): + if self.pad_to_multiple_of is None: + return tensor + seq_len = tensor.size(1) + if seq_len % self.pad_to_multiple_of == 0: + return tensor + pad_len = self.pad_to_multiple_of - (seq_len % self.pad_to_multiple_of) + pad = (0, pad_len) # pad on the right + return torch.nn.functional.pad(tensor, pad, value=pad_value) + + def __call__(self, features): + # features: list of dicts with torch tensors (from our Dataset) + input_ids = [f["input_ids"] if isinstance(f["input_ids"], torch.Tensor) else torch.tensor(f["input_ids"]) for f in features] + attn_masks = [f["attention_mask"] if isinstance(f["attention_mask"], torch.Tensor) else torch.tensor(f["attention_mask"]) for f in features] + labels = [f["labels"] if isinstance(f["labels"], torch.Tensor) else torch.tensor(f["labels"]) for f in features] + + # Pad inputs + pad_id = self.tok.pad_token_id + input_ids = pad_sequence(input_ids, batch_first=True, padding_value=pad_id) + attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) + + # Pad labels with pad_token_id, then convert to -100 + labels = pad_sequence(labels, batch_first=True, padding_value=pad_id) + labels_mask = labels.eq(pad_id) + labels = labels.masked_fill(labels_mask, self.label_pad_token_id) + + # Optional: align to 8/16/32 for kernel efficiency + input_ids = self._maybe_pad_to_multiple(input_ids, pad_id) + attn_masks = self._maybe_pad_to_multiple(attn_masks, 0) + labels = self._maybe_pad_to_multiple(labels, self.label_pad_token_id) + + return { + "input_ids": input_ids, + "attention_mask": attn_masks, + "labels": labels, + } From 8c5ba3442185f8246bdf73fb6d1cea42b73845cd Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Fri, 10 Oct 2025 14:35:39 +1000 Subject: [PATCH 02/41] added training data and rouge values to README.md --- recognition/Project13-TristanGreen/README.md | 23 ++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 recognition/Project13-TristanGreen/README.md diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md new file mode 100644 index 000000000..93123d9ff --- /dev/null +++ b/recognition/Project13-TristanGreen/README.md @@ -0,0 +1,23 @@ + + + + + + +Train e1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 150454/150454 [5:20:10<00:00, 7.83batch/s, loss=1.3393, sps=7.8] +[epoch 1] train_loss=1.3393 +Eval: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1250/1250 [1:01:16<00:00, 2.94s/batch] +[epoch 1] ROUGE: {'rouge1': 0.639758940949706, 'rouge2': 0.4262667182806449, 'rougeL': 0.5793631565756041, 'rougeLsum': 0.5795225947980385} +[epoch 1] ✓ saved best adapters to runs/flan_t5_base_lora_biolaysumm_debug +done. + + + + + + + + + + + From 9e42f22e5388ef5881930b88434b5944f86da92a Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 13 Oct 2025 13:55:21 +1000 Subject: [PATCH 03/41] Train working for seq2seq on the flan-t5 model. Currently lacks scheduler and warmup but produces results. --- recognition/Project13-TristanGreen/train.py | 159 ++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 recognition/Project13-TristanGreen/train.py diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py new file mode 100644 index 000000000..abf046ed7 --- /dev/null +++ b/recognition/Project13-TristanGreen/train.py @@ -0,0 +1,159 @@ +import argparse, os, random +from typing import List, Dict, Tuple +import torch +from torch.utils.data import Dataset, DataLoader +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM + +# ---------------------------- +# Dumb data loader (TSV: src \t tgt) +# ---------------------------- +def read_tsv(path: str) -> List[Dict[str, str]]: + pairs = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + # Expect "source\t target" + parts = line.split("\t") + if len(parts) < 2: + # skip trash lines + continue + src, tgt = parts[0], parts[1] + pairs.append({"src": src, "tgt": tgt}) + if not pairs: + raise ValueError(f"No usable lines found in {path}. Expect TSV with 'src\\t tgt'.") + return pairs + +# Tiny fallback toy data if you don't pass --train_file +def toy_pairs() -> List[Dict[str, str]]: + return [ + {"src": "summarize: The cat sat on the mat.", "tgt": "Cat on mat."}, + {"src": "summarize: Transformers are powerful models for NLP.", "tgt": "Transformers are powerful."}, + {"src": "summarize: The sky is blue and the sun is bright.", "tgt": "Blue sky, bright sun."}, + ] + +# ---------------------------- +# Dataset + Collate +# ---------------------------- +class PairDataset(Dataset): + def __init__(self, pairs: List[Dict[str, str]]): + self.pairs = pairs + + def __len__(self): + return len(self.pairs) + + def __getitem__(self, idx): + return self.pairs[idx] + +def make_collate_fn(tokenizer, src_max_len: int, tgt_max_len: int): + pad_id = tokenizer.pad_token_id + if pad_id is None: + raise ValueError("Tokenizer has no pad_token_id. Set one before training.") + + def collate(batch: List[Dict[str, str]]): + sources = [ex["src"] for ex in batch] + targets = [ex["tgt"] for ex in batch] + + model_inputs = tokenizer( + sources, + max_length=src_max_len, + truncation=True, + padding=True, + return_tensors="pt", + ) + with torch.no_grad(): + labels = tokenizer( + text_target=targets, + max_length=tgt_max_len, + truncation=True, + padding=True, + return_tensors="pt", + )["input_ids"] + labels[labels == pad_id] = -100 # ignore pad in loss + model_inputs["labels"] = labels + return model_inputs + return collate + +# ---------------------------- +# Training +# ---------------------------- +def set_seed(seed: int): + random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + +def train(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + set_seed(args.seed) + + tokenizer = AutoTokenizer.from_pretrained(args.model) + if tokenizer.pad_token_id is None: + # For some tokenizers you need to define this; T5 already has it. + tokenizer.pad_token = tokenizer.eos_token or tokenizer.unk_token + + model = AutoModelForSeq2SeqLM.from_pretrained(args.model) + model.to(device) + model.train() + + # Data + if args.train_file: + pairs = read_tsv(args.train_file) + else: + pairs = toy_pairs() + ds = PairDataset(pairs) + collate_fn = make_collate_fn(tokenizer, args.src_max_len, args.tgt_max_len) + + dl = DataLoader( + ds, + batch_size=args.batch_size, + shuffle=True, + collate_fn=collate_fn, + pin_memory=(device.type == "cuda"), + drop_last=False, + ) + + # Optimizer + optim = torch.optim.AdamW(model.parameters(), lr=args.lr) + + global_step = 0 + for epoch in range(1, args.epochs + 1): + for batch in dl: + batch = {k: v.to(device) for k, v in batch.items()} + out = model(**batch) # uses labels -> returns loss + loss = out.loss + + loss.backward() + optim.step() + optim.zero_grad() + + global_step += 1 + if global_step % args.log_every == 0: + print(f"[epoch {epoch}] step {global_step} loss={loss.item():.4f}") + + # Save minimal artifacts + os.makedirs(args.output_dir, exist_ok=True) + model.save_pretrained(args.output_dir) + tokenizer.save_pretrained(args.output_dir) + print(f"Saved to {args.output_dir}") + +# ---------------------------- +# CLI +# ---------------------------- +def parse_args(): + p = argparse.ArgumentParser(description="Bare-bones seq2seq trainer (no LoRA, no AMP, no eval).") + p.add_argument("--model", type=str, default="google/flan-t5-small") + p.add_argument("--train_file", type=str, default=None, help="TSV with 'src\\t tgt'. If omitted, uses tiny toy set.") + p.add_argument("--output_dir", type=str, default="./out_basic") + p.add_argument("--epochs", type=int, default=1) + p.add_argument("--batch_size", type=int, default=8) + p.add_argument("--lr", type=float, default=5e-5) + p.add_argument("--src_max_len", type=int, default=256) + p.add_argument("--tgt_max_len", type=int, default=64) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--log_every", type=int, default=20) + return p.parse_args() + +if __name__ == "__main__": + args = parse_args() + train(args) \ No newline at end of file From cf9bc452b26c29ec37a85c5ea5bd9823f8249b01 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 13 Oct 2025 14:08:41 +1000 Subject: [PATCH 04/41] Implemented tokenizer and pretrained flan-t5 from HF in load_base_model. --- recognition/Project13-TristanGreen/modules.py | 42 ++ recognition/Project13-TristanGreen/train.py | 377 +++++++++++------- 2 files changed, 275 insertions(+), 144 deletions(-) create mode 100644 recognition/Project13-TristanGreen/modules.py diff --git a/recognition/Project13-TristanGreen/modules.py b/recognition/Project13-TristanGreen/modules.py new file mode 100644 index 000000000..7cb18150d --- /dev/null +++ b/recognition/Project13-TristanGreen/modules.py @@ -0,0 +1,42 @@ +#modules.py +""" +General design ideas are that the datasets are defensively imported and are not +taken for granted since this is public software. All imports have guardrails. +""" +from __future__ import annotations +from typing import Optional, Dict, Any, List + +import torch +from transformers import ( + AutoModelForSeq2SeqLM, + AutoTokenizer, +) + +try: + from peft import LoraConfig, get_peft_model, PeftModel + PEFT_AVAILABLE = True +except Exception: + PEFT_AVAILABLE = False + + +def get_tokenizer(name: str = "google/flan-t5-base"): + tok = AutoTokenizer.from_pretrained(name, use_fast=True) + if tok.pad_token is None: + tok.pad_token = tok.eos_token + return tok + + +def load_base_model( + name: str = "google/flan-t5-base", + dtype: Optional[torch.dtype] = torch.float16, # <-- use 'dtype', not 'torch_dtype' + device_map: Optional[str] = None, +): + model = AutoModelForSeq2SeqLM.from_pretrained( + name, + dtype=dtype, # <-- fixes deprecation + device_map=device_map, + ) + if getattr(model.config, "decoder_start_token_id", None) is None: + model.config.decoder_start_token_id = model.config.pad_token_id + return model + diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index abf046ed7..7ce9a7bf5 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -1,159 +1,248 @@ -import argparse, os, random -from typing import List, Dict, Tuple +import os, json, math, argparse, random, csv, time +from typing import Dict, List, Optional +import numpy as np import torch -from torch.utils.data import Dataset, DataLoader -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM - -# ---------------------------- -# Dumb data loader (TSV: src \t tgt) -# ---------------------------- -def read_tsv(path: str) -> List[Dict[str, str]]: - pairs = [] - with open(path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - # Expect "source\t target" - parts = line.split("\t") - if len(parts) < 2: - # skip trash lines - continue - src, tgt = parts[0], parts[1] - pairs.append({"src": src, "tgt": tgt}) - if not pairs: - raise ValueError(f"No usable lines found in {path}. Expect TSV with 'src\\t tgt'.") - return pairs - -# Tiny fallback toy data if you don't pass --train_file -def toy_pairs() -> List[Dict[str, str]]: - return [ - {"src": "summarize: The cat sat on the mat.", "tgt": "Cat on mat."}, - {"src": "summarize: Transformers are powerful models for NLP.", "tgt": "Transformers are powerful."}, - {"src": "summarize: The sky is blue and the sun is bright.", "tgt": "Blue sky, bright sun."}, - ] - -# ---------------------------- -# Dataset + Collate -# ---------------------------- -class PairDataset(Dataset): - def __init__(self, pairs: List[Dict[str, str]]): - self.pairs = pairs - - def __len__(self): - return len(self.pairs) - - def __getitem__(self, idx): - return self.pairs[idx] - -def make_collate_fn(tokenizer, src_max_len: int, tgt_max_len: int): - pad_id = tokenizer.pad_token_id - if pad_id is None: - raise ValueError("Tokenizer has no pad_token_id. Set one before training.") - - def collate(batch: List[Dict[str, str]]): - sources = [ex["src"] for ex in batch] - targets = [ex["tgt"] for ex in batch] - - model_inputs = tokenizer( - sources, - max_length=src_max_len, - truncation=True, - padding=True, - return_tensors="pt", - ) - with torch.no_grad(): - labels = tokenizer( - text_target=targets, - max_length=tgt_max_len, - truncation=True, - padding=True, - return_tensors="pt", - )["input_ids"] - labels[labels == pad_id] = -100 # ignore pad in loss - model_inputs["labels"] = labels - return model_inputs - return collate - -# ---------------------------- -# Training -# ---------------------------- -def set_seed(seed: int): - random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - -def train(args): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +from torch.optim import AdamW +from torch.utils.data import DataLoader, Subset +from transformers import get_cosine_schedule_with_warmup +from tqdm.auto import tqdm + +# our modules +from dataset import make_datasets +from modules import load_base_model, attach_lora + +import evaluate # HF evaluate -> ROUGE + + +def csv_logger(path: str): + f = open(path, "a", newline="", encoding="utf-8") + w = csv.writer(f) + if f.tell() == 0: + w.writerow(["timestamp", "epoch", "global_step", "loss"]) + return f, w + + +def set_seed(seed: int = 1337): + random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + +def decode_labels(tokenizer, labels: torch.Tensor) -> List[str]: + lab = labels.clone() + lab[lab == -100] = tokenizer.pad_token_id + return tokenizer.batch_decode(lab, skip_special_tokens=True) + + +def run_eval(model, tokenizer, val_loader: Optional[DataLoader], device, args, rouge_metric): + if val_loader is None: + return None + model.eval() + + # speed hint for generation + use_cache_was = getattr(model.config, "use_cache", True) + model.config.use_cache = True + + preds, refs = [], [] + with torch.inference_mode(): + val_pbar = tqdm(val_loader, desc="Eval", unit="batch", dynamic_ncols=True) + for vb in val_pbar: + vb = {k: v.to(device) for k, v in vb.items()} + gen_out = model.generate( + input_ids=vb["input_ids"], + attention_mask=vb["attention_mask"], + max_new_tokens=args.eval_max_new_tokens, + num_beams=args.eval_beams, + no_repeat_ngram_size=3, + length_penalty=1.0, + early_stopping=True, + ) + pred_txt = tokenizer.batch_decode(gen_out, skip_special_tokens=True) + tgt = vb["labels"].clone() + tgt[tgt == -100] = tokenizer.pad_token_id + ref_txt = tokenizer.batch_decode(tgt, skip_special_tokens=True) + preds.extend(pred_txt) + refs.extend(ref_txt) + + scores = rouge_metric.compute(predictions=preds, references=refs, use_stemmer=True) + keep = {k: float(v) for k, v in scores.items() if k in {"rouge1", "rouge2", "rougeL", "rougeLsum"}} + model.config.use_cache = use_cache_was + return keep + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--model_name", default="google/flan-t5-base") + p.add_argument("--train_source", default="local_jsonl") + p.add_argument("--train_path", default="train.jsonl") + p.add_argument("--val_source", default="local_jsonl") + p.add_argument("--val_path", default="val.jsonl") + p.add_argument("--input_col", default="report") + p.add_argument("--target_col", default="summary") + p.add_argument("--max_input_len", type=int, default=1024) + p.add_argument("--max_target_len", type=int, default=256) + p.add_argument("--prefix", default="summarize: ") + p.add_argument("--epochs", type=int, default=3) + p.add_argument("--lr", type=float, default=2e-4) + p.add_argument("--weight_decay", type=float, default=0.01) + p.add_argument("--batch_size", type=int, default=1) + p.add_argument("--accum", type=int, default=16) + p.add_argument("--warmup_steps", type=int, default=1000) + p.add_argument("--clip", type=float, default=1.0) + p.add_argument("--lora_r", type=int, default=8) + p.add_argument("--lora_alpha", type=int, default=16) + p.add_argument("--lora_dropout", type=float, default=0.05) + p.add_argument("--output_dir", default="runs/flan_t5_base_lora") + p.add_argument("--seed", type=int, default=1337) + p.add_argument("--fp16", action="store_true") + p.add_argument("--eval_max_new_tokens", type=int, default=128) + p.add_argument("--eval_beams", type=int, default=4) + + # dev/fast-run controls + p.add_argument("--max_train_samples", type=int, default=None, + help="Limit training examples for quick dev runs") + p.add_argument("--max_eval_samples", type=int, default=None, + help="Limit validation examples during dev") + p.add_argument("--eval_batch_size", type=int, default=8, + help="Batch size used for generation during eval") + p.add_argument("--eval_every_steps", type=int, default=None, + help="If set, run eval every N optimizer steps (in addition to end-of-epoch)") + + args = p.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) set_seed(args.seed) - tokenizer = AutoTokenizer.from_pretrained(args.model) - if tokenizer.pad_token_id is None: - # For some tokenizers you need to define this; T5 already has it. - tokenizer.pad_token = tokenizer.eos_token or tokenizer.unk_token + # tokenizer + datasets + tokenizer, train_ds, val_ds, collator = make_datasets( + tokenizer_name=args.model_name, + train_source=(args.train_source, args.train_path), + val_source=(args.val_source, args.val_path) if args.val_path else None, + input_col=args.input_col, + target_col=args.target_col, + max_input_len=args.max_input_len, + max_target_len=args.max_target_len, + add_prefix=True, + prefix_text=args.prefix, + ) - model = AutoModelForSeq2SeqLM.from_pretrained(args.model) - model.to(device) - model.train() - - # Data - if args.train_file: - pairs = read_tsv(args.train_file) - else: - pairs = toy_pairs() - ds = PairDataset(pairs) - collate_fn = make_collate_fn(tokenizer, args.src_max_len, args.tgt_max_len) - - dl = DataLoader( - ds, - batch_size=args.batch_size, - shuffle=True, - collate_fn=collate_fn, - pin_memory=(device.type == "cuda"), - drop_last=False, + # Subset for dev speed + if args.max_train_samples is not None: + n = min(args.max_train_samples, len(train_ds)) + print(f"[INFO] Using only first {n} training samples (of {len(train_ds)})") + train_ds = Subset(train_ds, range(n)) + if (val_ds is not None) and (args.max_eval_samples is not None): + m = min(args.max_eval_samples, len(val_ds)) + print(f"[INFO] Using only first {m} validation samples (of {len(val_ds)})") + val_ds = Subset(val_ds, range(m)) + + # DataLoaders (eval uses larger batch) + train_loader = DataLoader( + train_ds, batch_size=args.batch_size, shuffle=True, + collate_fn=collator, pin_memory=True, num_workers=0 ) + val_loader: Optional[DataLoader] = None + if val_ds is not None: + val_loader = DataLoader( + val_ds, batch_size=args.eval_batch_size, shuffle=False, + collate_fn=collator, pin_memory=True, num_workers=0 + ) - # Optimizer - optim = torch.optim.AdamW(model.parameters(), lr=args.lr) + # model + LoRA + dtype = torch.float16 if (args.fp16 and torch.cuda.is_available()) else torch.float32 + model = load_base_model(args.model_name, dtype=dtype, device_map=None) + model = attach_lora(model, r=args.lora_r, alpha=args.lora_alpha, + dropout=args.lora_dropout, target_modules=["q", "k", "v", "o"]) + device = "cuda" if torch.cuda.is_available() else "cpu" + model.to(device) + # Logging + log_file, log_writer = csv_logger(os.path.join(args.output_dir, "train_log.csv")) global_step = 0 + optimizer_steps = 0 + + # Optim + sched + optim = AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + total_steps = math.ceil(len(train_loader) / args.accum) * args.epochs + warmup = min(args.warmup_steps, int(0.06 * total_steps)) + sched = get_cosine_schedule_with_warmup(optim, warmup, total_steps) + scaler = torch.amp.GradScaler("cuda", enabled=(args.fp16 and device == "cuda")) + + # Load ROUGE once + rouge_metric = evaluate.load("rouge") + + best_rougeLsum = -1.0 for epoch in range(1, args.epochs + 1): - for batch in dl: - batch = {k: v.to(device) for k, v in batch.items()} - out = model(**batch) # uses labels -> returns loss - loss = out.loss + model.train() + running = 0.0 + optim.zero_grad(set_to_none=True) - loss.backward() - optim.step() - optim.zero_grad() + pbar = tqdm(train_loader, desc=f"Train e{epoch}", unit="batch", dynamic_ncols=True) + start_time = time.time() + step_in_epoch = 0 + for batch in pbar: + step_in_epoch += 1 global_step += 1 - if global_step % args.log_every == 0: - print(f"[epoch {epoch}] step {global_step} loss={loss.item():.4f}") + batch = {k: v.to(device) for k, v in batch.items()} + + with torch.amp.autocast("cuda", enabled=(args.fp16 and device == "cuda")): + out = model(**batch) + loss = out.loss / args.accum + + # guard against NaN/Inf + if not torch.isfinite(loss): + print(f"[WARN] non-finite loss at global_step {global_step}: {float(loss)}. Skipping batch.") + optim.zero_grad(set_to_none=True) + continue + + scaler.scale(loss).backward() + running += loss.item() + + if (global_step % args.accum) == 0: + scaler.unscale_(optim) + torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) + scaler.step(optim) + scaler.update() + sched.step() + optim.zero_grad(set_to_none=True) + optimizer_steps += 1 + + # mid-epoch eval hook + if (args.eval_every_steps is not None) and (optimizer_steps % args.eval_every_steps == 0): + scores = run_eval(model, tokenizer, val_loader, device, args, rouge_metric) + if scores is not None: + print(f"[step {optimizer_steps}] ROUGE: {scores}") + + # live progress + avg_loss = running / max(1, (step_in_epoch // args.accum)) + elapsed = time.time() - start_time + sps = (step_in_epoch * args.batch_size) / max(1e-6, elapsed) + pbar.set_postfix({"loss": f"{avg_loss:.4f}", "sps": f"{sps:.1f}"}) + + # CSV log + if (global_step % args.accum) == 0: + log_writer.writerow([time.time(), epoch, global_step, avg_loss]) + log_file.flush() + + print(f"[epoch {epoch}] train_loss={avg_loss:.4f}") + + # end-of-epoch eval + scores = run_eval(model, tokenizer, val_loader, device, args, rouge_metric) + if scores is not None: + print(f"[epoch {epoch}] ROUGE: {scores}") + rougeLsum = float(scores.get("rougeLsum", 0.0)) + if rougeLsum > best_rougeLsum: + best_rougeLsum = rougeLsum + model.save_pretrained(args.output_dir) # saves LoRA adapters + tokenizer.save_pretrained(args.output_dir) + with open(os.path.join(args.output_dir, "metrics.json"), "w") as f: + json.dump({"best_rougeLsum": best_rougeLsum, "epoch": epoch, "scores": scores}, f, indent=2) + print(f"[epoch {epoch}] ✓ saved best adapters to {args.output_dir}") + + log_file.close() + print("done.") - # Save minimal artifacts - os.makedirs(args.output_dir, exist_ok=True) - model.save_pretrained(args.output_dir) - tokenizer.save_pretrained(args.output_dir) - print(f"Saved to {args.output_dir}") - -# ---------------------------- -# CLI -# ---------------------------- -def parse_args(): - p = argparse.ArgumentParser(description="Bare-bones seq2seq trainer (no LoRA, no AMP, no eval).") - p.add_argument("--model", type=str, default="google/flan-t5-small") - p.add_argument("--train_file", type=str, default=None, help="TSV with 'src\\t tgt'. If omitted, uses tiny toy set.") - p.add_argument("--output_dir", type=str, default="./out_basic") - p.add_argument("--epochs", type=int, default=1) - p.add_argument("--batch_size", type=int, default=8) - p.add_argument("--lr", type=float, default=5e-5) - p.add_argument("--src_max_len", type=int, default=256) - p.add_argument("--tgt_max_len", type=int, default=64) - p.add_argument("--seed", type=int, default=42) - p.add_argument("--log_every", type=int, default=20) - return p.parse_args() if __name__ == "__main__": - args = parse_args() - train(args) \ No newline at end of file + main() \ No newline at end of file From cd5a99c0391da3323f31285d176398ad353476cf Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 13 Oct 2025 14:11:46 +1000 Subject: [PATCH 05/41] Implemented basic predict.py with ability to use weights to parse jargon and summarise. --- recognition/Project13-TristanGreen/predict.py | 76 +++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 recognition/Project13-TristanGreen/predict.py diff --git a/recognition/Project13-TristanGreen/predict.py b/recognition/Project13-TristanGreen/predict.py new file mode 100644 index 000000000..6b2b130d4 --- /dev/null +++ b/recognition/Project13-TristanGreen/predict.py @@ -0,0 +1,76 @@ +# predict.py +import os, argparse, json +from typing import List +import torch +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer +from peft import PeftModel + +def load_model(adapter_dir: str, base_model: str, fp16: bool): + tok = AutoTokenizer.from_pretrained(adapter_dir) + dtype = torch.float16 if (fp16 and torch.cuda.is_available()) else torch.float32 + base = AutoModelForSeq2SeqLM.from_pretrained(base_model, dtype=dtype) + model = PeftModel.from_pretrained(base, adapter_dir) + device = "cuda" if torch.cuda.is_available() else "cpu" + model.to(device).eval() + if getattr(model.config, "decoder_start_token_id", None) is None: + model.config.decoder_start_token_id = model.config.pad_token_id + return tok, model, device + +def chunk(lst: List[str], n: int): + for i in range(0, len(lst), n): + yield lst[i:i+n] + +def generate_batch(model, tok, device, texts: List[str], max_in: int, max_new: int, beams: int, prefix: str): + batch = [prefix + t for t in texts] + enc = tok(batch, return_tensors="pt", truncation=True, max_length=max_in, padding=True).to(device) + with torch.inference_mode(): + out = model.generate( + **enc, + max_new_tokens=max_new, + num_beams=beams, + no_repeat_ngram_size=3, + length_penalty=1.0, + early_stopping=True, + use_cache=True, + ) + return tok.batch_decode(out, skip_special_tokens=True) + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--adapter_dir", required=True, help="Path to saved LoRA adapters") + ap.add_argument("--base_model", default="google/flan-t5-base") + ap.add_argument("--text", default=None, help="Single input string to summarize") + ap.add_argument("--input_col", default="report") + ap.add_argument("--out_path", default="predictions.jsonl") + ap.add_argument("--batch_size", type=int, default=8) + ap.add_argument("--max_input_len", type=int, default=1024) + ap.add_argument("--max_new_tokens", type=int, default=128) + ap.add_argument("--beams", type=int, default=4) + ap.add_argument("--prefix", default="summarize: ") + ap.add_argument("--fp16", action="store_true") + args = ap.parse_args() + + tok, model, device = load_model(args.adapter_dir, args.base_model, args.fp16) + + # single text mode + if args.text is not None: + outs = generate_batch(model, tok, device, [args.text], args.max_input_len, args.max_new_tokens, args.beams, args.prefix) + print(outs[0]) + return + + + inputs = [r.get(args.input_col, "") for r in rows] + preds = [] + for block in chunk(inputs, args.batch_size): + preds.extend(generate_batch(model, tok, device, block, args.max_input_len, args.max_new_tokens, args.beams, args.prefix)) + + # write JSONL with predictions + with open(args.out_path, "w", encoding="utf-8") as w: + for r, p in zip(rows, preds): + out = dict(r) + out["prediction"] = p + w.write(json.dumps(out, ensure_ascii=False) + "\n") + print(f"wrote {len(preds)} predictions to {args.out_path}") + +if __name__ == "__main__": + main() \ No newline at end of file From 74ff2a31e3f19fc41a4494ee4ebf7010235ab225 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Wed, 15 Oct 2025 15:47:46 +1000 Subject: [PATCH 06/41] Implemented text generation helper fully, finalised modules for now. --- recognition/Project13-TristanGreen/modules.py | 49 +++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/recognition/Project13-TristanGreen/modules.py b/recognition/Project13-TristanGreen/modules.py index 7cb18150d..ffb293ff7 100644 --- a/recognition/Project13-TristanGreen/modules.py +++ b/recognition/Project13-TristanGreen/modules.py @@ -40,3 +40,52 @@ def load_base_model( model.config.decoder_start_token_id = model.config.pad_token_id return model + +def attach_lora(model, r: int = 8, alpha: int = 16, dropout: float = 0.05, target_modules: Optional[List[str]] = None): + if not PEFT_AVAILABLE: + raise RuntimeError("peft not installed. `pip install peft` to use LoRA.") + if target_modules is None: + target_modules = ["q", "k", "v", "o"] + cfg = LoraConfig( + r=r, lora_alpha=alpha, lora_dropout=dropout, + target_modules=target_modules, bias="none", task_type="SEQ_2_SEQ_LM", + ) + return get_peft_model(model, cfg) + + +@torch.no_grad() +def generate( + model, + tokenizer, + inputs: List[str], + max_input_len: int = 1024, + max_new_tokens: int = 256, + num_beams: int = 4, + no_repeat_ngram_size: int = 3, + length_penalty: float = 1.0, + add_prefix: bool = True, + prefix_text: str = "summarize: ", + device: Optional[str] = None, +) -> List[str]: + model.eval() + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + batch = [(prefix_text + x) if add_prefix else x for x in inputs] + enc = tokenizer( + batch, + max_length=max_input_len, + truncation=True, + padding=True, + return_tensors="pt", + ).to(device) + + out = model.generate( + **enc, + max_new_tokens=max_new_tokens, + num_beams=num_beams, + no_repeat_ngram_size=no_repeat_ngram_size, + length_penalty=length_penalty, + early_stopping=True, + ) + return tokenizer.batch_decode(out, skip_special_tokens=True) From a8cffeef8d2aeeee5677c92ac47990ebb16aebdc Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Wed, 15 Oct 2025 15:48:57 +1000 Subject: [PATCH 07/41] Finished prediction to support single and multi-line json file inputs. --- recognition/Project13-TristanGreen/predict.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/recognition/Project13-TristanGreen/predict.py b/recognition/Project13-TristanGreen/predict.py index 6b2b130d4..22a7ceae3 100644 --- a/recognition/Project13-TristanGreen/predict.py +++ b/recognition/Project13-TristanGreen/predict.py @@ -40,6 +40,7 @@ def main(): ap.add_argument("--adapter_dir", required=True, help="Path to saved LoRA adapters") ap.add_argument("--base_model", default="google/flan-t5-base") ap.add_argument("--text", default=None, help="Single input string to summarize") + ap.add_argument("--jsonl", default=None, help="Path to JSONL with an input column") ap.add_argument("--input_col", default="report") ap.add_argument("--out_path", default="predictions.jsonl") ap.add_argument("--batch_size", type=int, default=8) @@ -58,6 +59,14 @@ def main(): print(outs[0]) return + # file mode + if args.jsonl is None: + raise SystemExit("Provide --text or --jsonl") + rows = [] + with open(args.jsonl, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + rows.append(json.loads(line)) inputs = [r.get(args.input_col, "") for r in rows] preds = [] From fc0117af0da1e2f6d268736d5349bcac8af3ab27 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Wed, 15 Oct 2025 15:50:01 +1000 Subject: [PATCH 08/41] Implemented a chatbot functionality for testing the predict.py and weights --- recognition/Project13-TristanGreen/chat.py | 37 ++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 recognition/Project13-TristanGreen/chat.py diff --git a/recognition/Project13-TristanGreen/chat.py b/recognition/Project13-TristanGreen/chat.py new file mode 100644 index 000000000..245e6128e --- /dev/null +++ b/recognition/Project13-TristanGreen/chat.py @@ -0,0 +1,37 @@ +import torch +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM +from peft import PeftModel + +# --- config --- +ADAPTER_DIR = "runs/flan_t5_base_lora_biolaysumm_debug" +BASE_MODEL = "google/flan-t5-base" +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +PREFIX = "summarize: " # change to something else if you fine-tuned for a different task +MAX_INPUT_LEN = 1024 +MAX_NEW_TOKENS = 256 +NUM_BEAMS = 4 + +# --- load model --- +print("Loading model...") +tok = AutoTokenizer.from_pretrained(ADAPTER_DIR) +base = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL) +model = PeftModel.from_pretrained(base, ADAPTER_DIR).to(DEVICE).eval() +print("Ready.") + +# --- chat loop --- +while True: + user = input("\n🧠 You: ").strip() + if not user: + continue + if user.lower() in {"exit", "quit", "q"}: + print("Bye.") + break + + enc = tok(PREFIX + user, return_tensors="pt", truncation=True, max_length=MAX_INPUT_LEN).to(DEVICE) + with torch.inference_mode(): + out = model.generate(**enc, + max_new_tokens=MAX_NEW_TOKENS, + num_beams=NUM_BEAMS, + no_repeat_ngram_size=3, + early_stopping=True) + print("\n🤖 Model:", tok.decode(out[0], skip_special_tokens=True)) From 1c6f8e3607cfb60098d260c046aefed566273040 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Wed, 15 Oct 2025 16:01:24 +1000 Subject: [PATCH 09/41] added gitignore --- recognition/Project13-TristanGreen/.gitignore | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 recognition/Project13-TristanGreen/.gitignore diff --git a/recognition/Project13-TristanGreen/.gitignore b/recognition/Project13-TristanGreen/.gitignore new file mode 100644 index 000000000..4096bf271 --- /dev/null +++ b/recognition/Project13-TristanGreen/.gitignore @@ -0,0 +1,2 @@ +/runs +/__pycache__ \ No newline at end of file From 6b9515b7714fa3be6b97f0743dc77c9acea425c8 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Thu, 16 Oct 2025 16:27:16 +1000 Subject: [PATCH 10/41] Edited README to add title --- recognition/Project13-TristanGreen/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 93123d9ff..1076c89cf 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -1,4 +1,5 @@ +> Brain-T5: A lightweight model fine-tuned for simplifying medical jargon using FLAN-T5 and LoRA. From 6c25f2942b87159b2383b3b20eb59baba197c6aa Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Thu, 16 Oct 2025 16:35:44 +1000 Subject: [PATCH 11/41] README: added headings for topic but yet to fill. --- recognition/Project13-TristanGreen/README.md | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 1076c89cf..25c68884a 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -1,8 +1,26 @@ +

+

Brain-T5: A lightweight model fine-tuned for simplifying medical jargon using FLAN-T5 and LoRA.

+

+ +## Project Motivation: -> Brain-T5: A lightweight model fine-tuned for simplifying medical jargon using FLAN-T5 and LoRA. +## Demo Examples: +## Features: +## Installation: +## Training Usage: + +## Chat Usage: + +## Training Resuts: + + + + + +README structure heavily inspired by HF transformers page. Train e1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 150454/150454 [5:20:10<00:00, 7.83batch/s, loss=1.3393, sps=7.8] From 1062c94c23aef6fd64d890355623ea2669620f7d Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Thu, 16 Oct 2025 17:33:41 +1000 Subject: [PATCH 12/41] work on README. added overview, motivation, features and codebase layout. --- recognition/Project13-TristanGreen/README.md | 34 +++++++++++++++++--- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 25c68884a..a5bce522d 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -1,12 +1,39 @@

Brain-T5: A lightweight model fine-tuned for simplifying medical jargon using FLAN-T5 and LoRA.

- + +Brain-T5 is a lightweight language model designed to translate technical clinical and biomedical text into layperson summaries so non-experts can understand them. Built on top of FLAN-T5 using LoRA fine-tuning, it is deployable on consumer grade GPUs and acts to assist research into medical fields from outer disciplines and acts as an assistant for patient communication. This repository includes full training, evaluation and inference pipelines, from dataset intake to an interactive chat mode. + ## Project Motivation: +Between medical professionals and the average person or researcher in an outer discipline, the scope of what "standard language" is does not cross over very well. Jargon is used excessively inside the medical world which may cause outer folk to struggle to understand basic summaries, research abstracts/results, or diagnostic reports. The only tools that exist that fit this use case effectively are large language models such as OpenAI's GPT-3+, Google's Gemini, Anthropic's Sonnet and others, however they cannot be localised easily on consumer grade hardware and use inputted conversational data to train their models. Many medical institutions may not want their data to cross borders, making a local option preferrable. -## Demo Examples: +Brain-T5 aims to close this gap by: + +- Using a fine-tuning approach with LoRA to an existing reliable text model +- Using the lightweight T5 model from Hugging Face trained on reliable medical summarisations. +- Using an open source, easy-to-install model that can be used on average consumer-grade hardware. + +Brain-T5 is a major step toward bridging the gap between the average person and medical knowledge and aims to enhance both clinical practices and interdisciplinary research around the world. ## Features: +- **LoRA-based fine-tuning** - train large models on consumer-grade GPUs. +- **Supports HuggingFace datasets, CSV, JSON** - flexible with data types. +- **Built-in ROUGE evaluation** - automatic scoring after each training epoch. +- **Interactive Chat CLI(`chat.py`)** - real-time inference like a medical assistant. +- **Modular codebase** - easy to extend or adapt to alternative domains (legal, finance, etc.) + +## Project Structure +``` +├── train.py # Full training pipeline with metrics and logging +├── predict.py # Batch inference on JSONL or single text +├── chat.py # Interactive CLI for conversational testing +├── modules.py # Tokenizer/model loaders and LoRA attachment +├── dataset.py # Dataset wrapper and fast collator +├── runs/ # LoRA adapters & metrics saved here +└── README.md +``` + +## Demo Examples: ## Installation: @@ -20,8 +47,7 @@ -README structure heavily inspired by HF transformers page. - +README structure heavily inspired by HF transformers pag Train e1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 150454/150454 [5:20:10<00:00, 7.83batch/s, loss=1.3393, sps=7.8] [epoch 1] train_loss=1.3393 From 3b713ee35e2dd3c11a8d7d107ad6de8b3468f2f1 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Thu, 16 Oct 2025 17:38:25 +1000 Subject: [PATCH 13/41] added requirements.txt for training model. --- recognition/Project13-TristanGreen/requirements.txt | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 recognition/Project13-TristanGreen/requirements.txt diff --git a/recognition/Project13-TristanGreen/requirements.txt b/recognition/Project13-TristanGreen/requirements.txt new file mode 100644 index 000000000..09bd267d7 --- /dev/null +++ b/recognition/Project13-TristanGreen/requirements.txt @@ -0,0 +1,8 @@ +torch +transformers +peft +evaluate +datasets +tqdm +numpy + From b57e1f7c0fad69f2a2a4982de92233065f3b7af6 Mon Sep 17 00:00:00 2001 From: Tristan Green <66065263+TPGCIG@users.noreply.github.com> Date: Fri, 17 Oct 2025 17:33:06 +1000 Subject: [PATCH 14/41] uploaded icon image for readme --- recognition/Project13-TristanGreen/brain t5.png | Bin 0 -> 82826 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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+ Logo +

+ +

Brain-T5: A lightweight model fine-tuned for simplifying medical jargon using FLAN-T5 and LoRA.

From db5d0a220be16ddddf2865864c8e493e84d86ac8 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Sun, 19 Oct 2025 18:14:10 +1000 Subject: [PATCH 19/41] Added training usage to README --- recognition/Project13-TristanGreen/README.md | 103 +++++++++++++++++-- 1 file changed, 95 insertions(+), 8 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 06c46d97c..ccdaa2ce9 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -32,7 +32,7 @@ Brain-T5 is a major step toward bridging the gap between the average person and ├── train.py # Full training pipeline with metrics and logging ├── predict.py # Batch inference on JSONL or single text ├── chat.py # Interactive CLI for conversational testing -├── modules.py # Tokenizer/model loaders and LoRA attachment +├── modules.py # Tokeniser/model loaders and LoRA attachment ├── dataset.py # Dataset wrapper and fast collator ├── runs/ # LoRA adapters & metrics saved here └── README.md @@ -40,26 +40,113 @@ Brain-T5 is a major step toward bridging the gap between the average person and ## Demo Examples: + ## Installation: +``` +# First, clone the repository and at the same time, checkout the topic-recognition branch. +git clone -b topic-recognition https://github.com/TPGCIG/PatternAnalysis-2025/ + +# Change directory to the Brain-T5 one. +cd recognition/Project13-TristanGreen + +# Install the dependencies. +pip install -r requirements.txt +``` + +You're now ready to go! + ## Training Usage: +The user has complete control over the training parameters, model used (in this circumstance, the user may want to train on `flan-t5-small`, `flan-t5-base`, `flan-t5-large`, `flan-t5-xl`, and `flan-t5-xxl`, however the default is set to `flan-t5-base` as it nets reliable results on consumer grade GPUs. -## Chat Usage: +## Training Usage -## Training Resuts: +### 1) Prepare data +Supports **JSONL**, **CSV**, or the **BioLaySumm HF dataset**. +- **JSONL** (one object per line) — default columns: `report` (input), `summary` (target) + ```json + {"report": "CT scan shows...", "summary": "The scan shows..."} + {"report": "Patient presents with...", "summary": "In plain English..."} + ``` +- **CSV** (has headers): `report,summary,...` + +### 2) Quick-start commands +Pick ONE of these, then iterate. + +**A. Local JSONL** +```bash +python train.py --train_source local_jsonl --train_path train.jsonl --val_source local_jsonl --val_path val.jsonl --output_dir runs/flan_t5_base_lora_myexp --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 +``` + +**B. Local CSV** +```bash +python train.py --train_source local_csv --train_path train.csv --val_source local_csv --val_path val.csv --input_col report --target_col summary --output_dir runs/flan_t5_base_lora_csv --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 +``` +**C. Hugging Face (BioLaySumm)** +> Requires `pip install datasets`. Uses the built-in dataset loader. +> `--train_path`/`--val_path` are **split names** (e.g., `train`, `validation`, `test`). +```bash +python train.py --train_source hf --train_path train --val_source hf --val_path validation --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 +``` + +### 3) What the script actually does +- Builds tokenizer + datasets via `make_datasets(...)` with your chosen **source kind** (`local_jsonl`, `local_csv`, or `hf`) and columns (`--input_col`, `--target_col`). +- Attaches **LoRA** adapters to FLAN‑T5 and trains with AdamW + cosine schedule. +- Evaluates with **ROUGE** at epoch end (and optionally mid‑epoch with `--eval_every_steps`). +- Saves best adapters + tokenizer to `--output_dir`, along with `metrics.json` and `train_log.csv`. +If you don’t see these files, you didn’t train anything meaningful. + +### 4) Arguments +- **Data**: `--train_source/--train_path`, `--val_source/--val_path`, `--input_col`, `--target_col` +- **Sequence lengths**: `--max_input_len`, `--max_target_len` (truncate aggressively if OOM) +- **Batching**: `--batch_size`, `--accum` (effective batch = batch_size × accum) +- **Optim**: `--lr`, `--weight_decay`, `--warmup_steps`, `--clip` +- **LoRA**: `--lora_r`, `--lora_alpha`, `--lora_dropout` +- **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams`, `--eval_every_steps` +- **Misc**: `--epochs`, `--seed`, `--fp16` + +### 5) Outputs (verify or it didn’t happen) +Inside your `--output_dir`: +``` +runs// +├── adapter_config.json +├── adapter_model.bin # LoRA weights +├── tokenizer.json +├── metrics.json # best ROUGE +└── train_log.csv # step-wise loss +``` +You should also see console logs like: +``` +[epoch 1] train_loss=... +[epoch 1] ROUGE: {'rouge1': ..., 'rouge2': ..., 'rougeL': ..., 'rougeLsum': ...} +``` + +### 6) Use the trained adapters +Single text: +```bash +python predict.py --adapter_dir runs/ --text "Put clinical text here" --fp16 +``` +Batch JSONL: +```bash +python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report --out_path predictions.jsonl +``` +### 7) General usage tips +- If CUDA OOM: lower `--max_input_len`/`--max_target_len`, increase `--accum`, or drop `--fp16` if your GPU cannot handle the defaults. +- If ROUGE is flat, your data columns are probably wrong. Print a few samples. +- If `runs/` is empty, you never beat your previous best—check learning rate and dataset. -README structure heavily inspired by HF transformers pag +## Chat Usage: + + + +## Training Resuts: -Train e1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 150454/150454 [5:20:10<00:00, 7.83batch/s, loss=1.3393, sps=7.8] [epoch 1] train_loss=1.3393 -Eval: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1250/1250 [1:01:16<00:00, 2.94s/batch] [epoch 1] ROUGE: {'rouge1': 0.639758940949706, 'rouge2': 0.4262667182806449, 'rougeL': 0.5793631565756041, 'rougeLsum': 0.5795225947980385} -[epoch 1] ✓ saved best adapters to runs/flan_t5_base_lora_biolaysumm_debug -done. From 3c177a0c4d58a4049669c01f5fc26d6c9e551001 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Sun, 19 Oct 2025 18:18:52 +1000 Subject: [PATCH 20/41] fixed small typo in README --- recognition/Project13-TristanGreen/README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index ccdaa2ce9..b7e9a5964 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -56,9 +56,6 @@ pip install -r requirements.txt You're now ready to go! -## Training Usage: -The user has complete control over the training parameters, model used (in this circumstance, the user may want to train on `flan-t5-small`, `flan-t5-base`, `flan-t5-large`, `flan-t5-xl`, and `flan-t5-xxl`, however the default is set to `flan-t5-base` as it nets reliable results on consumer grade GPUs. - ## Training Usage ### 1) Prepare data From 4e353686e6cd64992abe51ecd64c898d5383a7a3 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Sun, 19 Oct 2025 18:25:15 +1000 Subject: [PATCH 21/41] Added table of contents and touched up README --- recognition/Project13-TristanGreen/README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index b7e9a5964..849a753eb 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -9,6 +9,15 @@ Brain-T5 is a lightweight language model designed to translate technical clinical and biomedical text into layperson summaries so non-experts can understand them. Built on top of FLAN-T5 using LoRA fine-tuning, it is deployable on consumer grade GPUs and acts to assist research into medical fields from outer disciplines and acts as an assistant for patient communication. This repository includes full training, evaluation and inference pipelines, from dataset intake to an interactive chat mode. +## Table of Contents +- [Project Motivation](#project-motivation) +- [Features](#features) +- [Project Structure](#project-structure) +- [Installation](#installation) +- [Training Usage](#training-usage) +- [Chat Usage](#chat-usage) +- [Training Results](#training-results) + ## Project Motivation: Between medical professionals and the average person or researcher in an outer discipline, the scope of what "standard language" is does not cross over very well. Jargon is used excessively inside the medical world which may cause outer folk to struggle to understand basic summaries, research abstracts/results, or diagnostic reports. The only tools that exist that fit this use case effectively are large language models such as OpenAI's GPT-3+, Google's Gemini, Anthropic's Sonnet and others, however they cannot be localised easily on consumer grade hardware and use inputted conversational data to train their models. Many medical institutions may not want their data to cross borders, making a local option preferrable. @@ -38,9 +47,6 @@ Brain-T5 is a major step toward bridging the gap between the average person and └── README.md ``` -## Demo Examples: - - ## Installation: ``` @@ -134,15 +140,13 @@ python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report - If ROUGE is flat, your data columns are probably wrong. Print a few samples. - If `runs/` is empty, you never beat your previous best—check learning rate and dataset. - - ## Chat Usage: ## Training Resuts: -[epoch 1] train_loss=1.3393 +[epoch 1] train_loss=1.3393
[epoch 1] ROUGE: {'rouge1': 0.639758940949706, 'rouge2': 0.4262667182806449, 'rougeL': 0.5793631565756041, 'rougeLsum': 0.5795225947980385} From 3f03b7d767c767dae2bbfecde22084d00c3f829a Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Mon, 20 Oct 2025 15:37:31 +1000 Subject: [PATCH 22/41] added sample usage to README.md --- recognition/Project13-TristanGreen/README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 849a753eb..2c48a4284 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -142,7 +142,19 @@ python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report ## Chat Usage: +You: “CT angiography shows ~70% stenosis of the proximal LAD; plan for PCI if symptoms persist despite maximal medical therapy.” +Model: “There’s a notable blockage in a main heart artery. The team suggests a procedure if medicines don’t help enough.” +You: “MRI brain: multiple periventricular T2/FLAIR hyperintensities suggestive of demyelination; correlate clinically for MS.” +Model: “Scans show several spots in brain areas often linked to nerve coating problems. Doctors will compare this with symptoms.” + +You: “HbA1c 9.2% despite metformin 1g BID; initiate basal insulin 10u qHS and titrate to fasting 5–7 mmol/L.” +Model: “Blood sugar control isn’t good with current meds. Start a nightly insulin dose and adjust to get morning sugars into a better range.” + +You: “Echo: LVEF 35%, global hypokinesis, mild MR; optimize GDMT—ACEi/ARB, beta-blocker, MRA.” +Model: “Heart pumping is weaker than normal with some valve leak. Use standard heart medicines to improve function.” + +> This sample was trained on 3 epochs over a 16 hour period, better results can come as result of longer training and adjustable parameters. 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z!Me0vqXL2AN=w`$5Oa1#^ftQqlbddJdoJ}aAP;QqsJhx#pk})zz+?Z1f>6+aH0Z zZAK|%bYtzB-$0QO$YN<%mCNVwX%a_>N$Z^d4Zfz1@iZ$rf)p3|ilNX7c0 zJT0~^fJgP&)<{<77!I(f11r46+S-+I7~t|JIz}ZkmV}O@(%v@G^O7XlVPRL{H3XC6 z9v88(5uFxyv$^y|g-4Dxosi#&uDE!kNe=o{+;N%=ADHL+r!UB4k(NI&1 zPnx`@>q5uB^L2B3L literal 0 HcmV?d00001 From a2286c1e634214d7b8b93ed016fe853d3ff02280 Mon Sep 17 00:00:00 2001 From: Tristan Green Date: Mon, 20 Oct 2025 17:14:27 +1000 Subject: [PATCH 24/41] added lots of detail to README --- recognition/Project13-TristanGreen/README.md | 70 ++++++++++++++++---- 1 file changed, 56 insertions(+), 14 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 2c48a4284..31461415b 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -10,13 +10,17 @@ Brain-T5 is a lightweight language model designed to translate technical clinical and biomedical text into layperson summaries so non-experts can understand them. Built on top of FLAN-T5 using LoRA fine-tuning, it is deployable on consumer grade GPUs and acts to assist research into medical fields from outer disciplines and acts as an assistant for patient communication. This repository includes full training, evaluation and inference pipelines, from dataset intake to an interactive chat mode. ## Table of Contents -- [Project Motivation](#project-motivation) -- [Features](#features) -- [Project Structure](#project-structure) -- [Installation](#installation) -- [Training Usage](#training-usage) -- [Chat Usage](#chat-usage) -- [Training Results](#training-results) +- Brain-T5 + - [Project Motivation](#project-motivation) + - [Features](#features) + - [Project Structure](#project-structure) + - [Installation](#installation) + - [Training Usage](#training-usage) + - [Chat Usage](#chat-usage) + - [Training Results](#training-results) +- The FLAN-T5 Model + - [What is FLAN-T5?](#what-is-flan-t5) + - [Why not other models?](#why-not-other-models) ## Project Motivation: Between medical professionals and the average person or researcher in an outer discipline, the scope of what "standard language" is does not cross over very well. Jargon is used excessively inside the medical world which may cause outer folk to struggle to understand basic summaries, research abstracts/results, or diagnostic reports. The only tools that exist that fit this use case effectively are large language models such as OpenAI's GPT-3+, Google's Gemini, Anthropic's Sonnet and others, however they cannot be localised easily on consumer grade hardware and use inputted conversational data to train their models. Many medical institutions may not want their data to cross borders, making a local option preferrable. @@ -30,11 +34,11 @@ Brain-T5 aims to close this gap by: Brain-T5 is a major step toward bridging the gap between the average person and medical knowledge and aims to enhance both clinical practices and interdisciplinary research around the world. ## Features: -- **LoRA-based fine-tuning** - train large models on consumer-grade GPUs. -- **Supports HuggingFace datasets, CSV, JSON** - flexible with data types. -- **Built-in ROUGE evaluation** - automatic scoring after each training epoch. -- **Interactive Chat CLI(`chat.py`)** - real-time inference like a medical assistant. -- **Modular codebase** - easy to extend or adapt to alternative domains (legal, finance, etc.) +* **LoRA-based fine-tuning** - train large models on consumer-grade GPUs. +* **Supports HuggingFace datasets, CSV, JSON** - flexible with data types. +* **Built-in ROUGE evaluation** - automatic scoring after each training epoch. +* **Interactive Chat CLI(`chat.py`)** - real-time inference like a medical assistant. +* **Modular codebase** - easy to extend or adapt to alternative domains (legal, finance, etc.) ## Project Structure ``` @@ -142,6 +146,9 @@ python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report ## Chat Usage: +### Examples +> This sample was trained on 3 epochs over a 16 hour period, better results can come as result of longer training and adjustable parameters. The rouge scores for this training are seen in [Training Usage](#training-usage) + You: “CT angiography shows ~70% stenosis of the proximal LAD; plan for PCI if symptoms persist despite maximal medical therapy.” Model: “There’s a notable blockage in a main heart artery. The team suggests a procedure if medicines don’t help enough.” @@ -152,22 +159,57 @@ You: “HbA1c 9.2% despite metformin 1g BID; initiate basal insulin 10u qHS and Model: “Blood sugar control isn’t good with current meds. Start a nightly insulin dose and adjust to get morning sugars into a better range.” You: “Echo: LVEF 35%, global hypokinesis, mild MR; optimize GDMT—ACEi/ARB, beta-blocker, MRA.” -Model: “Heart pumping is weaker than normal with some valve leak. Use standard heart medicines to improve function.” +Model: “Heart pumping is weaker than normal with some valve leak. Use standard lung medicines to improve function.” + +### Error Analysis +The model consistently drops critical figures (example 1: ~70%, example 3: 9.2%) and summarises the responses with shallow descriptions that lose a lot of meaning. This is likely since, while T5 uses attention out-of-the-box, it doesn't learn to prioritise clinically critical details like these ones. This is likely since we are fine-tuning the model that is fundamentally trained on the [Common Crawl](https://commoncrawl.org/) dataset which does not prioritise numeric values or risk markers. In downstream fine-tuning, the model should be explicitly taught that figures have substantial meaning and should be valued more than other tokens that the model processes which the Common Crawl dataset is trained to do. + +The model also hallucinates (rarely though) (example 4: *use standard lung medicines* in heart context) and can forget the context loosely. This is obviously problematic but in downstream fine-tuning, the model should be explicitly over-attentive to the context as mistakes of this nature can cause problems in the medical field. +These limitations, while problematic, can be overcome via training on consumer grade hardware. Also, under proper supervision from a medical professional, these bugs can be quickly identified and flagged. -> This sample was trained on 3 epochs over a 16 hour period, better results can come as result of longer training and adjustable parameters. The rouge scores for this training are seen in [Training Usage](#training-usage) ## Training Resuts: +After the first epoch, the following results were achieved: + [epoch 1] train_loss=1.3393
[epoch 1] ROUGE: {'rouge1': 0.639758940949706, 'rouge2': 0.4262667182806449, 'rougeL': 0.5793631565756041, 'rougeLsum': 0.5795225947980385} +# The FLAN-T5 Model +## What is T5? +T5 (Text-to-Text Transfer Transformer) is a transformer model built completely on a text-to-text framework. This framework treats every task in Natural Language Processing (NLP), whether it be machine translation, summarisation, or question-answering, as a process of taking text as input and producing text as output. This unification allows the same model architecture, objective function, and training procedure to be applied across all tasks, massively simplifying the entire NLP training pipeline. + + +The super summarised explanation on how the model works (provided by OpenAI's ChatGPT) is: + +> This is the quoted text from ChatGPT. + +1. Tokenise → Embed → + Position. Words become vectors, add position info so the model knows order. +2. Encoder (repeated N×): + - Self-attention: each word looks at all other words to decide what matters. + - Feed-forward: a per-token mini-MLP to transform features. + - Add & Norm: residual skip + layer norm to keep training stable.
Output = contextual vectors for every input token. +3. Decoder input: start with / and previously generated tokens shifted right. +4. Decoder block (repeated N×): + - Masked self-attention: looks only at past output tokens (mask stops peeking at the future). + - Cross-attention: queries the encoder outputs so the decoder can “look up” relevant parts of the input. + - Feed-forward and Add & Norm again. +5. Linear → Softmax: turn the decoder’s last vector into a probability over the vocabulary; pick the next token; loop 4–5 until done. +> End quote. + +This is a representation of how T5 unifies all forms of text-to-text input/output to heavily generalise its use case and simply learning. +## What is FLAN-T5? +FLAN-T5 (Fine-tuned LAnguate Net T5) is an enhanced version of the original [T5](https://medium.com/analytics-vidhya/t5-a-detailed-explanation-a0ac9bc53e51) but fine-tuned using a technique called instruction tuning. +During training, FLAN-T5 is exposed to a massive number of tasks that are all formatted as natural language instructions (e.g. "Answer the following question: ..."). This training paradigm significantly improves the model's ability to: +1. **Follow instructions** since it is built on user prompts instead of general text data. +2, **Generalise** since the training prompts may map a new, prompted task out for the model to answer which can help it understand how to answer newer tasks it previously couldnt. From 5c106cf0dadf7df7938827b92642849db8f8df91 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 20 Oct 2025 20:53:52 +1000 Subject: [PATCH 25/41] removed a command line argument from train.py which was unnecessary --- recognition/Project13-TristanGreen/train.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index 7ce9a7bf5..669829571 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -105,8 +105,6 @@ def main(): help="Limit validation examples during dev") p.add_argument("--eval_batch_size", type=int, default=8, help="Batch size used for generation during eval") - p.add_argument("--eval_every_steps", type=int, default=None, - help="If set, run eval every N optimizer steps (in addition to end-of-epoch)") args = p.parse_args() @@ -208,11 +206,6 @@ def main(): optim.zero_grad(set_to_none=True) optimizer_steps += 1 - # mid-epoch eval hook - if (args.eval_every_steps is not None) and (optimizer_steps % args.eval_every_steps == 0): - scores = run_eval(model, tokenizer, val_loader, device, args, rouge_metric) - if scores is not None: - print(f"[step {optimizer_steps}] ROUGE: {scores}") # live progress avg_loss = running / max(1, (step_in_epoch // args.accum)) @@ -238,7 +231,7 @@ def main(): tokenizer.save_pretrained(args.output_dir) with open(os.path.join(args.output_dir, "metrics.json"), "w") as f: json.dump({"best_rougeLsum": best_rougeLsum, "epoch": epoch, "scores": scores}, f, indent=2) - print(f"[epoch {epoch}] ✓ saved best adapters to {args.output_dir}") + print(f"[epoch {epoch}] saved best adapters to {args.output_dir}") log_file.close() print("done.") From db252029cc875c9166dd45a7f0d53d565ce547e9 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 18:57:24 +1000 Subject: [PATCH 26/41] added command line arg option to choose which weights you want to use. --- recognition/Project13-TristanGreen/chat.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/recognition/Project13-TristanGreen/chat.py b/recognition/Project13-TristanGreen/chat.py index 245e6128e..23830de9d 100644 --- a/recognition/Project13-TristanGreen/chat.py +++ b/recognition/Project13-TristanGreen/chat.py @@ -1,16 +1,22 @@ import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from peft import PeftModel +import argparse + +p = argparse.ArgumentParser() + +p.add_argument("--model_dir", required=True) # --- config --- -ADAPTER_DIR = "runs/flan_t5_base_lora_biolaysumm_debug" +ADAPTER_DIR = p.parse_args().model_dir BASE_MODEL = "google/flan-t5-base" DEVICE = "cuda" if torch.cuda.is_available() else "cpu" -PREFIX = "summarize: " # change to something else if you fine-tuned for a different task +PREFIX = "summarize: " MAX_INPUT_LEN = 1024 MAX_NEW_TOKENS = 256 NUM_BEAMS = 4 + # --- load model --- print("Loading model...") tok = AutoTokenizer.from_pretrained(ADAPTER_DIR) From b04e1fb94dd1ca1434d30846551175341ed87d20 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 18:58:21 +1000 Subject: [PATCH 27/41] robustness changes and editing tool usage a bit as some functions have been updated. --- recognition/Project13-TristanGreen/README.md | 127 ++++++++++++------- 1 file changed, 83 insertions(+), 44 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 31461415b..4b33f5346 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -68,62 +68,55 @@ You're now ready to go! ## Training Usage -### 1) Prepare data -Supports **JSONL**, **CSV**, or the **BioLaySumm HF dataset**. -- **JSONL** (one object per line) — default columns: `report` (input), `summary` (target) - ```json - {"report": "CT scan shows...", "summary": "The scan shows..."} - {"report": "Patient presents with...", "summary": "In plain English..."} - ``` -- **CSV** (has headers): `report,summary,...` - -### 2) Quick-start commands -Pick ONE of these, then iterate. - -**A. Local JSONL** -```bash -python train.py --train_source local_jsonl --train_path train.jsonl --val_source local_jsonl --val_path val.jsonl --output_dir runs/flan_t5_base_lora_myexp --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 -``` -**B. Local CSV** +### 1) Quick-start commands +**Hugging Face (BioLaySumm)** +> Requires `pip install datasets`. Uses the built-in dataset loader. ```bash -python train.py --train_source local_csv --train_path train.csv --val_source local_csv --val_path val.csv --input_col report --target_col summary --output_dir runs/flan_t5_base_lora_csv --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 +python train.py --output_dir [dir_name] ``` -**C. Hugging Face (BioLaySumm)** -> Requires `pip install datasets`. Uses the built-in dataset loader. -> `--train_path`/`--val_path` are **split names** (e.g., `train`, `validation`, `test`). +### 2) For fine-grain training and control over parameters ```bash python train.py --train_source hf --train_path train --val_source hf --val_path validation --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 ``` ### 3) What the script actually does -- Builds tokenizer + datasets via `make_datasets(...)` with your chosen **source kind** (`local_jsonl`, `local_csv`, or `hf`) and columns (`--input_col`, `--target_col`). +- Builds tokenizer + datasets via `make_datasets(...)` with `hf` and columns (`--input_col`, `--target_col`). - Attaches **LoRA** adapters to FLAN‑T5 and trains with AdamW + cosine schedule. -- Evaluates with **ROUGE** at epoch end (and optionally mid‑epoch with `--eval_every_steps`). -- Saves best adapters + tokenizer to `--output_dir`, along with `metrics.json` and `train_log.csv`. -If you don’t see these files, you didn’t train anything meaningful. +- Evaluates with **ROUGE** at epoch. +- Saves best adapters + tokenizer to `--output_dir`, along with `metrics.json`, `train_log.csv` and graphs for `loss` and `ROUGE` scores per-epoch. ### 4) Arguments -- **Data**: `--train_source/--train_path`, `--val_source/--val_path`, `--input_col`, `--target_col` -- **Sequence lengths**: `--max_input_len`, `--max_target_len` (truncate aggressively if OOM) - **Batching**: `--batch_size`, `--accum` (effective batch = batch_size × accum) - **Optim**: `--lr`, `--weight_decay`, `--warmup_steps`, `--clip` - **LoRA**: `--lora_r`, `--lora_alpha`, `--lora_dropout` -- **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams`, `--eval_every_steps` +- **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams` - **Misc**: `--epochs`, `--seed`, `--fp16` ### 5) Outputs (verify or it didn’t happen) Inside your `--output_dir`: ``` runs// -├── adapter_config.json -├── adapter_model.bin # LoRA weights -├── tokenizer.json -├── metrics.json # best ROUGE -└── train_log.csv # step-wise loss +├── adapter_config.json # LoRA adapter setup (rank, alpha, target modules) +├── adapter_model.safetensors # Actual trained LoRA weight deltas +├── hardware.json # GPU name, VRAM, and compute capability info +├── history_val.csv # Validation ROUGE scores per epoch (for plotting) +├── metrics_test.json # Final held-out test ROUGE scores +├── metrics_val.json # Best validation epoch and its ROUGE metrics +├── special_tokens_map.json # Token IDs for , , etc. (auto from tokenizer) +├── README.md # Auto-generated PEFT model card (safe to delete) +├── tokenizer.json # Full tokenizer vocab + merges +├── tokenizer_config.json # Tokenizer settings (truncation, padding, etc.) +├── time.json # Total training time and epochs elapsed +├── rouge_val_curve.png # Line plot — validation ROUGE vs epoch +├── rouge_test_bar.png # Bar chart — test ROUGE metrics +├── loss_curve.png # Smoothed training loss vs steps curve +├── params.json # Total vs trainable parameter counts (LoRA ratio) +├── metrics.json # Duplicate or summary of best ROUGE metrics +└── train_log.csv # Step-wise loss log for generating loss_curve ``` -You should also see console logs like: +You should also see console logs during training like: ``` [epoch 1] train_loss=... [epoch 1] ROUGE: {'rouge1': ..., 'rouge2': ..., 'rougeL': ..., 'rougeLsum': ...} @@ -140,25 +133,25 @@ python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report ``` ### 7) General usage tips -- If CUDA OOM: lower `--max_input_len`/`--max_target_len`, increase `--accum`, or drop `--fp16` if your GPU cannot handle the defaults. +- If CUDA OOM: increase `--accum`, or drop `--fp16` if your GPU cannot handle the defaults. - If ROUGE is flat, your data columns are probably wrong. Print a few samples. - If `runs/` is empty, you never beat your previous best—check learning rate and dataset. ## Chat Usage: ### Examples -> This sample was trained on 3 epochs over a 16 hour period, better results can come as result of longer training and adjustable parameters. The rouge scores for this training are seen in [Training Usage](#training-usage) +> This sample was trained on 7 epochs over a 39 hour period, better results can come as result of longer training and adjustable parameters. The rouge scores for this training are seen in [Training Usage](#training-usage) -You: “CT angiography shows ~70% stenosis of the proximal LAD; plan for PCI if symptoms persist despite maximal medical therapy.” +You: “CT angiography shows ~70% stenosis of the proximal LAD; plan for PCI if symptoms persist despite maximal medical therapy.”
Model: “There’s a notable blockage in a main heart artery. The team suggests a procedure if medicines don’t help enough.” -You: “MRI brain: multiple periventricular T2/FLAIR hyperintensities suggestive of demyelination; correlate clinically for MS.” +You: “MRI brain: multiple periventricular T2/FLAIR hyperintensities suggestive of demyelination; correlate clinically for MS.”
Model: “Scans show several spots in brain areas often linked to nerve coating problems. Doctors will compare this with symptoms.” -You: “HbA1c 9.2% despite metformin 1g BID; initiate basal insulin 10u qHS and titrate to fasting 5–7 mmol/L.” +You: “HbA1c 9.2% despite metformin 1g BID; initiate basal insulin 10u qHS and titrate to fasting 5–7 mmol/L.”
Model: “Blood sugar control isn’t good with current meds. Start a nightly insulin dose and adjust to get morning sugars into a better range.” -You: “Echo: LVEF 35%, global hypokinesis, mild MR; optimize GDMT—ACEi/ARB, beta-blocker, MRA.” +You: “Echo: LVEF 35%, global hypokinesis, mild MR; optimize GDMT—ACEi/ARB, beta-blocker, MRA.”
Model: “Heart pumping is weaker than normal with some valve leak. Use standard lung medicines to improve function.” ### Error Analysis @@ -169,11 +162,54 @@ These limitations, while problematic, can be overcome via training on consumer g ## Training Resuts: +Training was performed on the BioLaySumm 2025 - LaymanRRG opensource track, using FLAN-T5-Base with LoRA fine-tuning for 3 epochs. +The model was trained with AdamW + cosine schedule, batch size 1 × gradient accumulation 16 (effective batch = 16), and evaluated with ROUGE-1/2/L/Lsum per epoch. + +1. Training Loss (full run) + + +This plot shows the training loss vs optimizer steps over the entire fine-tuning run. +The curve steadily declines and stabilises, showing smooth convergence without major oscillation — indicating that: + +* The learning rate and warm-up schedule were well-tuned. + +* Gradient accumulation was effective in maintaining numerical stability under mixed-precision (--fp16) training. + +* No gradient explosions or plateaus occurred (loss range ≈ 1.9 → 1.2). + +2. Training Loss (medium zoom) + + +This is a zoomed-in view of the mid-training regime, showing finer granularity of step-wise noise. +Loss fluctuations at small scale are expected from single-sample batches, but the general slope continues downward, confirming consistent optimization rather than overfitting spikes. + +3. Validation ROUGE Progress (full run) + + +This figure tracks ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum per epoch. + +Interpretation: + +* ROUGE-1 and ROUGE-L steadily improve and plateau by the third epoch, showing that lexical and long-span coherence both increased. + +* ROUGE-2 remains noisier, which is typical for summarization tasks where exact bigram matches are less frequent. + +* The consistent upward trajectory across all four metrics indicates learning stability and effective LoRA adaptation. + +4. Validation ROUGE (medium zoom) + + +This mid-range view highlights the epoch-to-epoch change more clearly: + +* Rapid early gains in the first epoch. + +* Smaller, diminishing returns after epoch 2, suggesting convergence. + +* No regression in ROUGE-Lsum, evidence that the checkpoint selected (highest ROUGE-Lsum) indeed corresponds to the global optimum seen during training. -After the first epoch, the following results were achieved: +Overall, Brain-T5 demonstrates reliable convergence and solid generalisation across validation and test splits. +The model maintains smooth training dynamics and rising ROUGE performance without evidence of overfitting or divergence — validating the correctness of the pipeline in train.py and the dataset tokenization logic in dataset.py -[epoch 1] train_loss=1.3393
-[epoch 1] ROUGE: {'rouge1': 0.639758940949706, 'rouge2': 0.4262667182806449, 'rougeL': 0.5793631565756041, 'rougeLsum': 0.5795225947980385} # The FLAN-T5 Model ## What is T5? @@ -209,7 +245,10 @@ FLAN-T5 (Fine-tuned LAnguate Net T5) is an enhanced version of the original [T5] During training, FLAN-T5 is exposed to a massive number of tasks that are all formatted as natural language instructions (e.g. "Answer the following question: ..."). This training paradigm significantly improves the model's ability to: 1. **Follow instructions** since it is built on user prompts instead of general text data. -2, **Generalise** since the training prompts may map a new, prompted task out for the model to answer which can help it understand how to answer newer tasks it previously couldnt. +2. **Generalise** since the training prompts may map a new, prompted task out for the model to answer which can help it understand how to answer newer tasks it previously couldnt. +3. **Transfer knowledge efficiently** because FLAN-T5 was trained on diverse, instruction-formatted datasets, it can quickly adapt to unseen downstream tasks (like layperson medical summarization) with relatively few gradient updates. +4. **Reduce hallucination and bias** as tuning encourages models to anchor their responses to explicit prompts, producing more deterministic and context-aware outputs compared to raw pretrained T5 models. +In essence, FLAN-T5 represents a major leap in making large-scale text-to-text models usable out of the box for a wide range of natural language tasks. Its combination of instructional alignment, broad coverage, and generalization ability makes it a strong backbone for fine-tuning in specialized domains, such as Brain-T5, where the goal is translating complex biomedical text into accessible language without requiring massive compute resources. From d7e91920d6cec416b09dc7ec29475c6aa01d0ec8 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 18:58:54 +1000 Subject: [PATCH 28/41] truncating a few parameter options that are unintuitive and arent critical to functionality, makes use of tool easier. --- recognition/Project13-TristanGreen/train.py | 439 +++++++++++++++----- 1 file changed, 336 insertions(+), 103 deletions(-) diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index 669829571..9ad8b67d8 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -1,52 +1,48 @@ -import os, json, math, argparse, random, csv, time -from typing import Dict, List, Optional +# train.py — locked to BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track +import os, json, math, argparse, random, time, uuid, csv +from typing import Optional import numpy as np import torch from torch.optim import AdamW -from torch.utils.data import DataLoader, Subset +from torch.utils.data import DataLoader from transformers import get_cosine_schedule_with_warmup from tqdm.auto import tqdm -# our modules -from dataset import make_datasets -from modules import load_base_model, attach_lora +import matplotlib +matplotlib.use("Agg") # headless safe +import matplotlib.pyplot as plt -import evaluate # HF evaluate -> ROUGE +from dataset import make_datasets # locked dataset helper +from modules import load_base_model, attach_lora +import evaluate +# ----------------------- +# Utils: logging & eval +# ----------------------- def csv_logger(path: str): + """Append-mode CSV logger for training steps; writes header if file is empty.""" f = open(path, "a", newline="", encoding="utf-8") w = csv.writer(f) if f.tell() == 0: w.writerow(["timestamp", "epoch", "global_step", "loss"]) return f, w - def set_seed(seed: int = 1337): random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) - -def decode_labels(tokenizer, labels: torch.Tensor) -> List[str]: - lab = labels.clone() - lab[lab == -100] = tokenizer.pad_token_id - return tokenizer.batch_decode(lab, skip_special_tokens=True) - - -def run_eval(model, tokenizer, val_loader: Optional[DataLoader], device, args, rouge_metric): - if val_loader is None: +def run_eval(model, tokenizer, loader: Optional[DataLoader], device, args, rouge_metric): + if loader is None: return None model.eval() - - # speed hint for generation use_cache_was = getattr(model.config, "use_cache", True) model.config.use_cache = True preds, refs = [], [] with torch.inference_mode(): - val_pbar = tqdm(val_loader, desc="Eval", unit="batch", dynamic_ncols=True) - for vb in val_pbar: + for vb in tqdm(loader, desc="Eval", unit="batch", dynamic_ncols=True): vb = {k: v.to(device) for k, v in vb.items()} gen_out = model.generate( input_ids=vb["input_ids"], @@ -61,28 +57,236 @@ def run_eval(model, tokenizer, val_loader: Optional[DataLoader], device, args, r tgt = vb["labels"].clone() tgt[tgt == -100] = tokenizer.pad_token_id ref_txt = tokenizer.batch_decode(tgt, skip_special_tokens=True) - preds.extend(pred_txt) - refs.extend(ref_txt) + preds.extend(pred_txt); refs.extend(ref_txt) scores = rouge_metric.compute(predictions=preds, references=refs, use_stemmer=True) - keep = {k: float(v) for k, v in scores.items() if k in {"rouge1", "rouge2", "rougeL", "rougeLsum"}} + keep = {k: float(v) for k, v in scores.items() if k in {"rouge1","rouge2","rougeL","rougeLsum"}} model.config.use_cache = use_cache_was return keep +# ----------------------- +# Per-epoch ROUGE logging +# ----------------------- + +RUN_ID = os.environ.get("RUN_ID", str(uuid.uuid4())[:8]) + +def log_val_rouge_row(run_dir, epoch, scores): + """ + Append one row per epoch: + run_id, timestamp, epoch, rouge1, rouge2, rougeL, rougeLsum + Writes header if file is empty. + """ + path = os.path.join(run_dir, "history_val.csv") + new_file = (not os.path.exists(path)) or (os.path.getsize(path) == 0) + with open(path, "a", newline="", encoding="utf-8") as f: + w = csv.writer(f) + if new_file: + w.writerow(["run_id","timestamp","epoch","rouge1","rouge2","rougeL","rougeLsum"]) + w.writerow([ + RUN_ID, + int(time.time()), + int(epoch), + float(scores.get("rouge1", 0.0)), + float(scores.get("rouge2", 0.0)), + float(scores.get("rougeL", 0.0)), + float(scores.get("rougeLsum", 0.0)), + ]) + +# ----------------------- +# Plotting helpers +# ----------------------- + +def plot_val_rouge_curve(run_dir): + """ + Plot Validation ROUGE vs Epoch (robust): + - reads history_val.csv + - keeps only the latest row per epoch (by timestamp if present) + - sorts epochs ascending + - falls back to metrics_val.json if no rows + """ + import json + path = os.path.join(run_dir, "history_val.csv") + rows = [] + if os.path.exists(path) and os.path.getsize(path) > 0: + with open(path, newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + + def _fallback_from_metrics(): + mv = os.path.join(run_dir, "metrics_val.json") + if not os.path.exists(mv): + print("[plot] no history_val.csv rows and no metrics_val.json; skipping val ROUGE plot") + return None + obj = json.load(open(mv, "r", encoding="utf-8")) + ep = int(obj.get("epoch", 1)) + s = obj.get("scores", obj) + return [ep], [float(s.get("rouge1", 0.0))], [float(s.get("rouge2", 0.0))], \ + [float(s.get("rougeL", 0.0))], [float(s.get("rougeLsum", 0.0))] + + if not rows: + vals = _fallback_from_metrics() + if vals is None: return + epochs, r1, r2, rL, rS = vals + else: + # latest row per epoch by timestamp if present; else by order + last_by_epoch = {} + for r in rows: + if "epoch" not in r: # malformed row + continue + try: + ep = int(r["epoch"]) + except Exception: + continue + ts = int(r.get("timestamp", 0)) if r.get("timestamp") else 0 + if (ep not in last_by_epoch) or (ts >= int(last_by_epoch[ep].get("timestamp", 0) or 0)): + last_by_epoch[ep] = r + + if not last_by_epoch: + vals = _fallback_from_metrics() + if vals is None: return + epochs, r1, r2, rL, rS = vals + else: + epochs = sorted(last_by_epoch.keys()) + def _f(e, k): + v = last_by_epoch[e].get(k, None) + return float(v) if v not in (None, "",) else 0.0 + r1 = [_f(e, "rouge1") for e in epochs] + r2 = [_f(e, "rouge2") for e in epochs] + rL = [_f(e, "rougeL") for e in epochs] + rS = [_f(e, "rougeLsum") for e in epochs] + + plt.figure() + plt.plot(epochs, r1, label="ROUGE-1") + plt.plot(epochs, r2, label="ROUGE-2") + plt.plot(epochs, rL, label="ROUGE-L") + plt.plot(epochs, rS, label="ROUGE-Lsum") + plt.xlabel("Epoch"); plt.ylabel("ROUGE") + plt.title("Validation ROUGE vs Epoch") + plt.grid(True, alpha=0.3) + plt.legend() + plt.tight_layout() + out = os.path.join(run_dir, "rouge_val_curve.png") + plt.savefig(out, dpi=160); plt.close() + print(f"[plot] wrote {out}") + +def plot_loss_curve(run_dir): + """ + Plot a single clean Training Loss vs Steps line even if train_log.csv + contains multiple runs or step resets. + Strategy: + - read train_log.csv + - split into segments whenever global_step decreases (new run appended) + - keep ONLY the last segment (latest run) + - sort by step, clip outlier spikes (1..99p), smooth with small moving average + """ + import numpy as np + + path = os.path.join(run_dir, "train_log.csv") + if not os.path.exists(path) or os.path.getsize(path) == 0: + print("[plot] no train_log.csv; skipping loss plot"); return + + rows = [] + with open(path, newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + if not rows: + print("[plot] empty train_log.csv; skipping loss plot"); return + + # parse numeric + raw_steps, raw_losses = [], [] + for r in rows: + try: + raw_steps.append(int(float(r["global_step"]))) + raw_losses.append(float(r["loss"])) + except Exception: + continue + if not raw_steps: + print("[plot] no numeric rows in train_log.csv; skipping"); return + + # split into segments whenever step decreases (step reset = new run) + segs = [] + seg_s, seg_l = [raw_steps[0]], [raw_losses[0]] + for s, l in zip(raw_steps[1:], raw_losses[1:]): + if s < seg_s[-1]: # reset + segs.append((seg_s, seg_l)) + seg_s, seg_l = [s], [l] + else: + seg_s.append(s); seg_l.append(l) + segs.append((seg_s, seg_l)) + + # pick the LAST segment (most recent run) + steps, losses = segs[-1] + + # sort by step + order = np.argsort(steps) + steps = [steps[i] for i in order] + losses = [losses[i] for i in order] + + # clip extreme spikes for visualization (1..99 percentile) + lo, hi = np.percentile(losses, [1, 99]) + keep = [(lo <= v <= hi) for v in losses] + steps = [s for s, m in zip(steps, keep) if m] + losses = [v for v, m in zip(losses, keep) if m] + + # moving average smoothing + from collections import deque + def movavg(x, k=None): + if len(x) == 0: return x + if k is None: + k = max(5, min(25, len(x)//20)) # gentle default + out, q, s = [], deque(), 0.0 + for v in x: + q.append(v); s += v + if len(q) > k: s -= q.popleft() + out.append(s / len(q)) + return out + + sm = movavg(losses) + + plt.figure() + plt.plot(steps, sm) + plt.xlabel("Global step (optimizer)") + plt.ylabel("Loss") + plt.title("Training Loss vs Steps") + plt.grid(True, alpha=0.3) + plt.tight_layout() + out = os.path.join(run_dir, "loss_curve.png") + plt.savefig(out, dpi=160); plt.close() + print(f"[plot] wrote {out}") + +def plot_test_rouge_bar(run_dir): + path = os.path.join(run_dir, "metrics_test.json") + if not os.path.exists(path): + print("[plot] no metrics_test.json; skipping test bar"); return + m = json.load(open(path, "r", encoding="utf-8")) + if isinstance(m, dict) and "note" in m: + print(f"[plot] {m['note']} — skipping test bar"); return + labels = ["ROUGE-1","ROUGE-2","ROUGE-L","ROUGE-Lsum"] + vals = [float(m.get("rouge1",0.0)), float(m.get("rouge2",0.0)), + float(m.get("rougeL",0.0)), float(m.get("rougeLsum",0.0))] + plt.figure() + plt.bar(labels, vals) + plt.ylabel("Score"); plt.title("Test ROUGE (Held-out)") + plt.tight_layout() + out = os.path.join(run_dir, "rouge_test_bar.png") + plt.savefig(out, dpi=160); plt.close() + print(f"[plot] wrote {out}") + +# ----------------------- +# Main +# ----------------------- def main(): p = argparse.ArgumentParser() + # No dataset args — we're locked to the RRG opensource track via make_datasets p.add_argument("--model_name", default="google/flan-t5-base") - p.add_argument("--train_source", default="local_jsonl") - p.add_argument("--train_path", default="train.jsonl") - p.add_argument("--val_source", default="local_jsonl") - p.add_argument("--val_path", default="val.jsonl") - p.add_argument("--input_col", default="report") - p.add_argument("--target_col", default="summary") - p.add_argument("--max_input_len", type=int, default=1024) - p.add_argument("--max_target_len", type=int, default=256) - p.add_argument("--prefix", default="summarize: ") - p.add_argument("--epochs", type=int, default=3) + p.add_argument("--train_split", default="train") + p.add_argument("--val_split", default="validation") + p.add_argument("--test_split", default="test") + + # seq lengths + prefix + p.add_argument("--prefix", default="summarize: ") + + # training + p.add_argument("--epochs", type=int, default=5) p.add_argument("--lr", type=float, default=2e-4) p.add_argument("--weight_decay", type=float, default=0.01) p.add_argument("--batch_size", type=int, default=1) @@ -92,72 +296,91 @@ def main(): p.add_argument("--lora_r", type=int, default=8) p.add_argument("--lora_alpha", type=int, default=16) p.add_argument("--lora_dropout", type=float, default=0.05) - p.add_argument("--output_dir", default="runs/flan_t5_base_lora") + p.add_argument("--output_dir", default="runs/flan_t5_base_lora_rrg") p.add_argument("--seed", type=int, default=1337) p.add_argument("--fp16", action="store_true") + + # eval/generation p.add_argument("--eval_max_new_tokens", type=int, default=128) p.add_argument("--eval_beams", type=int, default=4) + p.add_argument("--eval_batch_size", type=int, default=8) + + # dev-speed controls + p.add_argument("--max_train_samples", type=int, default=None) + p.add_argument("--max_eval_samples", type=int, default=None) + p.add_argument("--max_test_samples", type=int, default=None) - # dev/fast-run controls - p.add_argument("--max_train_samples", type=int, default=None, - help="Limit training examples for quick dev runs") - p.add_argument("--max_eval_samples", type=int, default=None, - help="Limit validation examples during dev") - p.add_argument("--eval_batch_size", type=int, default=8, - help="Batch size used for generation during eval") + # optional self-split (because official opensource test has empty refs) + p.add_argument("--self_split", action="store_true", + help="If set, create custom 80/10/10 train/val/test from training data.") + p.add_argument("--self_split_val", type=float, default=0.1, + help="Proportion of data to use as validation if self_split.") + p.add_argument("--self_split_test", type=float, default=0.1, + help="Proportion of data to use as test if self_split.") args = p.parse_args() os.makedirs(args.output_dir, exist_ok=True) set_seed(args.seed) - # tokenizer + datasets - tokenizer, train_ds, val_ds, collator = make_datasets( + # Start fresh logs (truncate; do NOT pre-write mismatched headers) + open(os.path.join(args.output_dir, "train_log.csv"), "w").close() + open(os.path.join(args.output_dir, "history_val.csv"), "w").close() + + # tokenizer + datasets (locked to RRG via make_datasets) + tokenizer, train_ds, val_ds, test_ds, collator = make_datasets( tokenizer_name=args.model_name, - train_source=(args.train_source, args.train_path), - val_source=(args.val_source, args.val_path) if args.val_path else None, - input_col=args.input_col, - target_col=args.target_col, - max_input_len=args.max_input_len, - max_target_len=args.max_target_len, - add_prefix=True, + train_split=args.train_split, + val_split=args.val_split, + test_split=args.test_split, + max_input_len=1024, + max_target_len=256, prefix_text=args.prefix, + self_split=args.self_split, + self_split_val=args.self_split_val, + self_split_test=args.self_split_test, ) - # Subset for dev speed + # Optional subsetting if args.max_train_samples is not None: - n = min(args.max_train_samples, len(train_ds)) - print(f"[INFO] Using only first {n} training samples (of {len(train_ds)})") - train_ds = Subset(train_ds, range(n)) - if (val_ds is not None) and (args.max_eval_samples is not None): - m = min(args.max_eval_samples, len(val_ds)) - print(f"[INFO] Using only first {m} validation samples (of {len(val_ds)})") - val_ds = Subset(val_ds, range(m)) - - # DataLoaders (eval uses larger batch) - train_loader = DataLoader( - train_ds, batch_size=args.batch_size, shuffle=True, - collate_fn=collator, pin_memory=True, num_workers=0 - ) - val_loader: Optional[DataLoader] = None + train_ds = train_ds.select(range(min(args.max_train_samples, len(train_ds)))) + if val_ds is not None and args.max_eval_samples is not None: + val_ds = val_ds.select(range(min(args.max_eval_samples, len(val_ds)))) + if test_ds is not None and args.max_test_samples is not None: + test_ds = test_ds.select(range(min(args.max_test_samples, len(test_ds)))) + + # DataLoaders + train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, + collate_fn=collator, pin_memory=True, num_workers=0) + val_loader = None if val_ds is not None: - val_loader = DataLoader( - val_ds, batch_size=args.eval_batch_size, shuffle=False, - collate_fn=collator, pin_memory=True, num_workers=0 - ) + val_loader = DataLoader(val_ds, batch_size=args.eval_batch_size, shuffle=False, + collate_fn=collator, pin_memory=True, num_workers=0) # model + LoRA dtype = torch.float16 if (args.fp16 and torch.cuda.is_available()) else torch.float32 model = load_base_model(args.model_name, dtype=dtype, device_map=None) model = attach_lora(model, r=args.lora_r, alpha=args.lora_alpha, - dropout=args.lora_dropout, target_modules=["q", "k", "v", "o"]) + dropout=args.lora_dropout, target_modules=["q","k","v","o"]) device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) + # Log params & hardware (artifact for report) + total = sum(p.numel() for p in model.parameters()) + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + with open(os.path.join(args.output_dir, "params.json"), "w") as f: + json.dump({"total": total, "trainable": trainable, "ratio": trainable/total}, f, indent=2) + if torch.cuda.is_available(): + props = torch.cuda.get_device_properties(0) + with open(os.path.join(args.output_dir, "hardware.json"), "w") as f: + json.dump({"gpu_name": torch.cuda.get_device_name(0), + "total_vram_gb": round(props.total_memory/(1024**3),2), + "compute_capability": f"{props.major}.{props.minor}"}, + f, indent=2) + # Logging log_file, log_writer = csv_logger(os.path.join(args.output_dir, "train_log.csv")) global_step = 0 - optimizer_steps = 0 # Optim + sched optim = AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) @@ -165,77 +388,87 @@ def main(): warmup = min(args.warmup_steps, int(0.06 * total_steps)) sched = get_cosine_schedule_with_warmup(optim, warmup, total_steps) scaler = torch.amp.GradScaler("cuda", enabled=(args.fp16 and device == "cuda")) - - # Load ROUGE once rouge_metric = evaluate.load("rouge") + t0 = time.time() best_rougeLsum = -1.0 for epoch in range(1, args.epochs + 1): model.train() running = 0.0 optim.zero_grad(set_to_none=True) - pbar = tqdm(train_loader, desc=f"Train e{epoch}", unit="batch", dynamic_ncols=True) start_time = time.time() step_in_epoch = 0 for batch in pbar: - step_in_epoch += 1 - global_step += 1 + step_in_epoch += 1; global_step += 1 batch = {k: v.to(device) for k, v in batch.items()} - with torch.amp.autocast("cuda", enabled=(args.fp16 and device == "cuda")): out = model(**batch) loss = out.loss / args.accum - - # guard against NaN/Inf if not torch.isfinite(loss): - print(f"[WARN] non-finite loss at global_step {global_step}: {float(loss)}. Skipping batch.") - optim.zero_grad(set_to_none=True) - continue - - scaler.scale(loss).backward() - running += loss.item() + print(f"[WARN] non-finite loss at step {global_step}: {float(loss)} — skipping.") + optim.zero_grad(set_to_none=True); continue + scaler.scale(loss).backward(); running += loss.item() if (global_step % args.accum) == 0: scaler.unscale_(optim) torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) - scaler.step(optim) - scaler.update() - sched.step() + scaler.step(optim); scaler.update(); sched.step() optim.zero_grad(set_to_none=True) - optimizer_steps += 1 + # CSV log (per optimizer step) + avg_loss = running / max(1, (step_in_epoch // args.accum)) + log_writer.writerow([time.time(), epoch, global_step, avg_loss]); log_file.flush() - # live progress + # live status avg_loss = running / max(1, (step_in_epoch // args.accum)) elapsed = time.time() - start_time sps = (step_in_epoch * args.batch_size) / max(1e-6, elapsed) pbar.set_postfix({"loss": f"{avg_loss:.4f}", "sps": f"{sps:.1f}"}) - # CSV log - if (global_step % args.accum) == 0: - log_writer.writerow([time.time(), epoch, global_step, avg_loss]) - log_file.flush() - print(f"[epoch {epoch}] train_loss={avg_loss:.4f}") - # end-of-epoch eval + # validation at epoch end scores = run_eval(model, tokenizer, val_loader, device, args, rouge_metric) if scores is not None: - print(f"[epoch {epoch}] ROUGE: {scores}") + print(f"[epoch {epoch}] ROUGE (val): {scores}") + + # persist per-epoch history for plotting + log_val_rouge_row(args.output_dir, epoch, scores) + rougeLsum = float(scores.get("rougeLsum", 0.0)) if rougeLsum > best_rougeLsum: best_rougeLsum = rougeLsum - model.save_pretrained(args.output_dir) # saves LoRA adapters + model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) - with open(os.path.join(args.output_dir, "metrics.json"), "w") as f: + with open(os.path.join(args.output_dir, "metrics_val.json"), "w") as f: json.dump({"best_rougeLsum": best_rougeLsum, "epoch": epoch, "scores": scores}, f, indent=2) print(f"[epoch {epoch}] saved best adapters to {args.output_dir}") - log_file.close() - print("done.") + # live plot after each epoch + plot_val_rouge_curve(args.output_dir) + # timing + minutes = (time.time() - t0) / 60.0 + with open(os.path.join(args.output_dir, "time.json"), "w") as f: + json.dump({"minutes": minutes, "epochs": args.epochs}, f, indent=2) + + # test evaluation (held-out; for opensource we self-split or skip if refs missing) + if test_ds is not None: + test_loader = DataLoader(test_ds, batch_size=args.eval_batch_size, shuffle=False, + collate_fn=collator, pin_memory=True, num_workers=0) + test_scores = run_eval(model, tokenizer, test_loader, device, args, rouge_metric) + with open(os.path.join(args.output_dir, "metrics_test.json"), "w") as f: + json.dump(test_scores, f, indent=2) + print(f"[test] ROUGE: {test_scores}") + + # Always generate plots at the end for the report + plot_loss_curve(args.output_dir) + plot_val_rouge_curve(args.output_dir) + plot_test_rouge_bar(args.output_dir) + + print("done.") if __name__ == "__main__": - main() \ No newline at end of file + main() From d5fbddf060fe7acaadea8dcb550cf206df118d8a Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 19:00:33 +1000 Subject: [PATCH 29/41] small changes to align functionality of database tools with new train.py parameters. --- recognition/Project13-TristanGreen/dataset.py | 281 ++++++------------ 1 file changed, 91 insertions(+), 190 deletions(-) diff --git a/recognition/Project13-TristanGreen/dataset.py b/recognition/Project13-TristanGreen/dataset.py index 1f249392d..fab2f09c2 100644 --- a/recognition/Project13-TristanGreen/dataset.py +++ b/recognition/Project13-TristanGreen/dataset.py @@ -1,169 +1,17 @@ -# dataset.py -from __future__ import annotations -import csv, json, os -from typing import Dict, List, Optional, Tuple +# dataset.py — locked to BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track +from __future__ import annotations +from typing import Optional, List, Dict import torch -from torch.utils.data import Dataset -try: - from datasets import load_dataset # optional - HF_AVAILABLE = True -except Exception: - HF_AVAILABLE = False - -from transformers import AutoTokenizer, DataCollatorForSeq2Seq - - -class SummarisationDataset(Dataset): - """ - Flexible dataset: supports HF datasets, CSV, or JSONL. - Expects two text fields: `input_col` (expert report) and `target_col` (lay summary). - """ - def __init__( - self, - records: List[Dict[str, str]], - tokenizer: AutoTokenizer, - max_input_len: int = 1024, - max_target_len: int = 256, - add_prefix: bool = True, - prefix_text: str = "summarize: ", - strip_empty: bool = True, - ): - self.records = [] - for r in records: - src = (r.get("input") or r.get("report") or r.get("source") or r.get("text") or "").strip() - tgt = (r.get("target") or r.get("summary") or r.get("lay_summary") or "").strip() - if strip_empty and (not src or not tgt): - continue - self.records.append({"input": src, "target": tgt}) - - if len(self.records) == 0: - raise ValueError("No usable records found (empty inputs/targets).") - - self.tok = tokenizer - self.max_in = max_input_len - self.max_tgt = max_target_len - self.add_prefix = add_prefix - self.prefix_text = prefix_text - - def __len__(self) -> int: - return len(self.records) - - def __getitem__(self, idx: int): - ex = self.records[idx] - src = (self.prefix_text + ex["input"]) if self.add_prefix else ex["input"] - tgt = ex["target"] - - model_inputs = self.tok( - src, - max_length=self.max_in, - truncation=True, - padding=False, - ) - labels = self.tok( - text_target=tgt, # <-- modern API - max_length=self.max_tgt, - truncation=True, - padding=False, - ) - model_inputs["labels"] = labels["input_ids"] - return {k: torch.tensor(v) for k, v in model_inputs.items()} - - -def load_local_csv( - path: str, - input_col: str = "report", - target_col: str = "summary", - delimiter: str = ",", -) -> List[Dict[str, str]]: - rows: List[Dict[str, str]] = [] - with open(path, "r", encoding="utf-8") as f: - reader = csv.DictReader(f, delimiter=delimiter) - for r in reader: - rows.append({"input": r.get(input_col, ""), "target": r.get(target_col, "")}) - return rows - - -def load_local_jsonl( - path: str, - input_col: str = "report", - target_col: str = "summary", -) -> List[Dict[str, str]]: - rows: List[Dict[str, str]] = [] - with open(path, "r", encoding="utf-8") as f: - for line in f: - if not line.strip(): - continue - obj = json.loads(line) - rows.append({"input": obj.get(input_col, ""), "target": obj.get(target_col, "")}) - return rows - - -def load_biolaysumm_hf( - dataset_name: str = "BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track", - split: str = "train", - input_col: str = "report", - target_col: str = "summary", -) -> List[Dict[str, str]]: - if not HF_AVAILABLE: - raise RuntimeError("`datasets` not installed. Use local CSV/JSONL or install `datasets`.") - ds = load_dataset(dataset_name, split=split) - rows = [] - for r in ds: - rows.append({"input": r.get(input_col, ""), "target": r.get(target_col, "")}) - return rows - - -def make_datasets( - tokenizer_name: str = "google/flan-t5-base", - train_source: Tuple[str, str] = ("local_jsonl", "train.jsonl"), - val_source: Optional[Tuple[str, str]] = ("local_jsonl", "val.jsonl"), - input_col: str = "report", - target_col: str = "summary", - max_input_len: int = 1024, - max_target_len: int = 256, - add_prefix: bool = True, - prefix_text: str = "summarize: ", -): - tok = AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True) - - def _load(kind: str, arg: str) -> List[Dict[str, str]]: - if kind == "local_csv": - return load_local_csv(arg, input_col, target_col) - elif kind == "local_jsonl": - return load_local_jsonl(arg, input_col, target_col) - elif kind == "hf": - # arg should be split name if using HF - return load_biolaysumm_hf(split=arg, input_col=input_col, target_col=target_col) - else: - raise ValueError(f"Unknown source kind: {kind}") - - train_kind, train_arg = train_source - train_rows = _load(train_kind, train_arg) - - val_rows = [] - if val_source: - val_kind, val_arg = val_source - val_rows = _load(val_kind, val_arg) - - train_ds = SummarisationDataset( - train_rows, tok, max_input_len, max_target_len, add_prefix, prefix_text - ) - val_ds = SummarisationDataset( - val_rows, tok, max_input_len, max_target_len, add_prefix, prefix_text - ) if val_rows else None - - collator = Seq2SeqCollatorFast(tok, label_pad_token_id=-100, pad_to_multiple_of=None) # or 8/16 if you want alignment - return tok, train_ds, val_ds, collator - - +from datasets import load_dataset +from transformers import AutoTokenizer from torch.nn.utils.rnn import pad_sequence +DATASET_ID = "BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track" +INPUT_COL = "radiology_report" +TARGET_COL = "layman_report" + class Seq2SeqCollatorFast: - """ - Fast collator that pads inputs/labels with pure torch ops. - Avoids the slow path in HF's DataCollatorForSeq2Seq that triggers - """ def __init__(self, tokenizer, label_pad_token_id=-100, pad_to_multiple_of=None): self.tok = tokenizer self.label_pad_token_id = label_pad_token_id @@ -172,36 +20,89 @@ def __init__(self, tokenizer, label_pad_token_id=-100, pad_to_multiple_of=None): def _maybe_pad_to_multiple(self, tensor, pad_value): if self.pad_to_multiple_of is None: return tensor - seq_len = tensor.size(1) - if seq_len % self.pad_to_multiple_of == 0: + L = tensor.size(1) + if L % self.pad_to_multiple_of == 0: return tensor - pad_len = self.pad_to_multiple_of - (seq_len % self.pad_to_multiple_of) - pad = (0, pad_len) # pad on the right - return torch.nn.functional.pad(tensor, pad, value=pad_value) + add = self.pad_to_multiple_of - (L % self.pad_to_multiple_of) + return torch.nn.functional.pad(tensor, (0, add), value=pad_value) - def __call__(self, features): - # features: list of dicts with torch tensors (from our Dataset) - input_ids = [f["input_ids"] if isinstance(f["input_ids"], torch.Tensor) else torch.tensor(f["input_ids"]) for f in features] - attn_masks = [f["attention_mask"] if isinstance(f["attention_mask"], torch.Tensor) else torch.tensor(f["attention_mask"]) for f in features] - labels = [f["labels"] if isinstance(f["labels"], torch.Tensor) else torch.tensor(f["labels"]) for f in features] + def __call__(self, feats: List[Dict[str, torch.Tensor]]): + ids = [f["input_ids"] if isinstance(f["input_ids"], torch.Tensor) else torch.tensor(f["input_ids"]) for f in feats] + am = [f["attention_mask"] if isinstance(f["attention_mask"], torch.Tensor) else torch.tensor(f["attention_mask"]) for f in feats] + labs = [f["labels"] if isinstance(f["labels"], torch.Tensor) else torch.tensor(f["labels"]) for f in feats] - # Pad inputs pad_id = self.tok.pad_token_id - input_ids = pad_sequence(input_ids, batch_first=True, padding_value=pad_id) - attn_masks = pad_sequence(attn_masks, batch_first=True, padding_value=0) - - # Pad labels with pad_token_id, then convert to -100 - labels = pad_sequence(labels, batch_first=True, padding_value=pad_id) - labels_mask = labels.eq(pad_id) - labels = labels.masked_fill(labels_mask, self.label_pad_token_id) - - # Optional: align to 8/16/32 for kernel efficiency - input_ids = self._maybe_pad_to_multiple(input_ids, pad_id) - attn_masks = self._maybe_pad_to_multiple(attn_masks, 0) - labels = self._maybe_pad_to_multiple(labels, self.label_pad_token_id) - - return { - "input_ids": input_ids, - "attention_mask": attn_masks, - "labels": labels, - } + ids = pad_sequence(ids, batch_first=True, padding_value=pad_id) + am = pad_sequence(am, batch_first=True, padding_value=0) + labs = pad_sequence(labs, batch_first=True, padding_value=pad_id) + labs = labs.masked_fill(labs.eq(pad_id), self.label_pad_token_id) + + ids = self._maybe_pad_to_multiple(ids, pad_id) + am = self._maybe_pad_to_multiple(am, 0) + labs = self._maybe_pad_to_multiple(labs, self.label_pad_token_id) + return {"input_ids": ids, "attention_mask": am, "labels": labs} + + +def make_datasets( + tokenizer_name: str = "google/flan-t5-base", + train_split: str = "train", + val_split: Optional[str] = "validation", + test_split: Optional[str] = "test", + max_input_len: int = 1024, + max_target_len: int = 256, + prefix_text: str = "summarize: ", + *, + self_split: bool = False, + self_split_seed: int = 1337, + self_split_val: float = 0.1, + self_split_test: float = 0.1, +): + + tok = AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True) + if tok.pad_token is None: + tok.pad_token = tok.eos_token + + ds = load_dataset(DATASET_ID) + + from datasets import DatasetDict + + if self_split: + base = ds["train"].train_test_split(test_size=self_split_test, seed=self_split_seed) + train_part = base["train"] + test_part = base["test"] + vt = train_part.train_test_split( + test_size=self_split_val / (1.0 - self_split_test), seed=self_split_seed) + ds = DatasetDict({ + "train": vt["train"], + "validation": vt["test"], + "test": test_part, + }) + + # Validate required columns exist + for split in [s for s in [train_split, val_split, test_split] if s and s in ds]: + cols = ds[split].column_names + if INPUT_COL not in cols or TARGET_COL not in cols: + raise KeyError(f"Expected columns '{INPUT_COL}', '{TARGET_COL}' in split '{split}', found {cols}") + + def encode_batch(batch): + srcs = [prefix_text + s for s in batch[INPUT_COL]] + enc = tok(srcs, max_length=max_input_len, truncation=True) + tgt = tok(text_target=batch[TARGET_COL], max_length=max_target_len, truncation=True) + enc["labels"] = tgt["input_ids"] + return enc + + remove_cols = ds[train_split].column_names + train_proc = ds[train_split].map(encode_batch, batched=True, remove_columns=remove_cols, desc="Tokenizing train") + + val_proc = None + if val_split and val_split in ds: + remove_cols_val = ds[val_split].column_names + val_proc = ds[val_split].map(encode_batch, batched=True, remove_columns=remove_cols_val, desc="Tokenizing val") + + test_proc = None + if test_split and test_split in ds: + remove_cols_test = ds[test_split].column_names + test_proc = ds[test_split].map(encode_batch, batched=True, remove_columns=remove_cols_test, desc="Tokenizing test") + + collator = Seq2SeqCollatorFast(tok, label_pad_token_id=-100, pad_to_multiple_of=None) + return tok, train_proc, val_proc, test_proc, collator From 3edc34686f4789881f87309fd30688a62ce2b011 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 19:01:17 +1000 Subject: [PATCH 30/41] added training data and graphs for README.md --- .../assets/images/loss_curve_full.png | Bin 0 -> 38665 bytes .../assets/images/loss_curve_med.png | Bin 0 -> 45568 bytes .../assets/images/rouge_val_curve_full.png | Bin 0 -> 44374 bytes .../assets/images/rouge_val_curve_med.png | Bin 0 -> 55063 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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added dataset section to README --- recognition/Project13-TristanGreen/README.md | 43 +++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 4b33f5346..f07884629 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -94,7 +94,7 @@ python train.py --train_source hf --train_path train --val_source hf --val - **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams` - **Misc**: `--epochs`, `--seed`, `--fp16` -### 5) Outputs (verify or it didn’t happen) +### 5) Outputs Inside your `--output_dir`: ``` runs// @@ -210,6 +210,47 @@ This mid-range view highlights the epoch-to-epoch change more clearly: Overall, Brain-T5 demonstrates reliable convergence and solid generalisation across validation and test splits. The model maintains smooth training dynamics and rising ROUGE performance without evidence of overfitting or divergence — validating the correctness of the pipeline in train.py and the dataset tokenization logic in dataset.py +## Dataset +Brain-T5 is trained on the BioLaySumm 2025 – LaymanRRG (Open-Source Track) dataset, hosted on Hugging Face under the identifier [BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track](https://huggingface.co/datasets/BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track). + +This dataset is specifically curated for the layperson summarisation of biomedical text. +Each entry contains a technical radiology report paired with a human-written lay summary, enabling fine-tuning of models for domain translation between clinical and plain language. + +### Structure + +Each example includes two main fields: + +Column Description +`radiology_report`: The source input - detailed, jargon-heavy text extracted from radiology or clinical notes. +`layman_report`: The target output - a simplified explanation written for a general audience. + +During preprocessing, dataset.py automatically prefixes each input with "summarize: " for FLAN-T5 instruction consistency, tokenizes both columns using the model’s tokenizer, and pads sequences for batch training. + +### Usage in Training + +In train.py, datasets are loaded via `make_datasets(...)` which: + +Fetches all splits (train, validation, test) directly from Hugging Face. + +Optionally performs an 80/10/10 self-split when the open-source test set lacks reference summaries (`--self_split` flag). + +Encodes all samples into token IDs (input_ids, attention_mask, labels) ready for PyTorch training. + +A custom Seq2SeqCollatorFast batches and pads sequences efficiently, ensuring label alignment and correct masking for loss computation. +This design minimises preprocessing overhead and keeps I/O throughput optimal even on smaller consumer GPUs. + +### Why BioLaySumm? + +BioLaySumm provides: + +Authentic biomedical phrasing, exposing the model to realistic clinical structure and terminology. + +Human-validated lay summaries, ensuring stylistic and semantic accuracy for non-expert readability. + +Consistent formatting, ideal for instruction-based models like FLAN-T5 that thrive on aligned input/output pairs. + +Together, these qualities make BioLaySumm the ideal foundation for training Brain-T5 to bridge the gap between clinical documentation and human-understandable summaries. + # The FLAN-T5 Model ## What is T5? From 9c218ffe19167f28796a17ad1f7913a5fea00364 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 21:31:04 +1000 Subject: [PATCH 32/41] added explanation for ROUGE scores and removed table of contents since it is unnecessary --- recognition/Project13-TristanGreen/README.md | 92 ++++++++++++++------ 1 file changed, 63 insertions(+), 29 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index f07884629..4c2aafc12 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -9,18 +9,6 @@ Brain-T5 is a lightweight language model designed to translate technical clinical and biomedical text into layperson summaries so non-experts can understand them. Built on top of FLAN-T5 using LoRA fine-tuning, it is deployable on consumer grade GPUs and acts to assist research into medical fields from outer disciplines and acts as an assistant for patient communication. This repository includes full training, evaluation and inference pipelines, from dataset intake to an interactive chat mode. -## Table of Contents -- Brain-T5 - - [Project Motivation](#project-motivation) - - [Features](#features) - - [Project Structure](#project-structure) - - [Installation](#installation) - - [Training Usage](#training-usage) - - [Chat Usage](#chat-usage) - - [Training Results](#training-results) -- The FLAN-T5 Model - - [What is FLAN-T5?](#what-is-flan-t5) - - [Why not other models?](#why-not-other-models) ## Project Motivation: Between medical professionals and the average person or researcher in an outer discipline, the scope of what "standard language" is does not cross over very well. Jargon is used excessively inside the medical world which may cause outer folk to struggle to understand basic summaries, research abstracts/results, or diagnostic reports. The only tools that exist that fit this use case effectively are large language models such as OpenAI's GPT-3+, Google's Gemini, Anthropic's Sonnet and others, however they cannot be localised easily on consumer grade hardware and use inputted conversational data to train their models. Many medical institutions may not want their data to cross borders, making a local option preferrable. @@ -71,7 +59,6 @@ You're now ready to go! ### 1) Quick-start commands **Hugging Face (BioLaySumm)** -> Requires `pip install datasets`. Uses the built-in dataset loader. ```bash python train.py --output_dir [dir_name] ``` @@ -118,7 +105,6 @@ runs// ``` You should also see console logs during training like: ``` -[epoch 1] train_loss=... [epoch 1] ROUGE: {'rouge1': ..., 'rouge2': ..., 'rougeL': ..., 'rougeLsum': ...} ``` @@ -160,11 +146,63 @@ The model consistently drops critical figures (example 1: ~70%, example 3: 9.2%) The model also hallucinates (rarely though) (example 4: *use standard lung medicines* in heart context) and can forget the context loosely. This is obviously problematic but in downstream fine-tuning, the model should be explicitly over-attentive to the context as mistakes of this nature can cause problems in the medical field. These limitations, while problematic, can be overcome via training on consumer grade hardware. Also, under proper supervision from a medical professional, these bugs can be quickly identified and flagged. +## Evaluation Metrics +### ROUGE Evaluation +Brain-T5’s summarization quality is evaluated using **ROUGE** (Recall-Oriented Understudy for Gisting Evaluation), the standard metric for text summarization.
+ROUGE measures the degree of overlap between the model’s generated summaries and the ground-truth human-written ones, capturing how well the model reproduces key phrases and sentence structure from the reference. + +### How It Works + +In `train.py`, the evaluation loop computes ROUGE using: + +```py +scores = rouge_metric.compute(predictions=preds, references=refs, use_stemmer=True) +``` + +This returns four main sub-metrics: + +* ROUGE-1 – overlap of unigrams (single words). + +* ROUGE-2 – overlap of bigrams (two-word sequences). + +* ROUGE-L – measures the Longest Common Subsequence (LCS) between prediction and reference. + +* ROUGE-Lsum – a sentence-level variant of ROUGE-L, emphasizing structural similarity in multi-sentence outputs. + +Each score ranges from 0 to 1, where higher is better. ROUGE can be computed in terms of precision, recall, and F1, but this project uses the F1-form returned by the Hugging Face `evaluate` package, balancing both correctness and completeness. + +### Why It Matters + +* ROUGE-1 reflects general lexical similarity — whether the model uses similar vocabulary. + +* ROUGE-2 indicates phrase-level fluency — capturing short-range coherence. + +* ROUGE-L and ROUGE-Lsum capture long-range structure and sentence organization — essential for readability and factual flow in lay summaries. + +In practice, ROUGE-Lsum serves as the primary checkpoint criterion in train.py: + +```py +if rougeLsum > best_rougeLsum: + model.save_pretrained(args.output_dir) +``` +meaning the “best” model is whichever epoch achieves the highest ROUGE-Lsum score across validation. + +### Interpretation + +A strong model shows: + +* Gradual increases in ROUGE-1/2/L/Lsum across epochs. + +* Consistent correlation between lower loss and higher ROUGE scores. + +* Minimal gap between validation and test ROUGE, indicating good generalization. ## Training Resuts: Training was performed on the BioLaySumm 2025 - LaymanRRG opensource track, using FLAN-T5-Base with LoRA fine-tuning for 3 epochs. The model was trained with AdamW + cosine schedule, batch size 1 × gradient accumulation 16 (effective batch = 16), and evaluated with ROUGE-1/2/L/Lsum per epoch. +The full run was ran over the entire dataset (150,000 datapoints) per-epoch, while the medium zoom was from a much smaller dataset (2000 datapoints) but with a higer epoch count. + 1. Training Loss (full run) @@ -173,9 +211,9 @@ The curve steadily declines and stabilises, showing smooth convergence without m * The learning rate and warm-up schedule were well-tuned. -* Gradient accumulation was effective in maintaining numerical stability under mixed-precision (--fp16) training. +* Gradient accumulation was effective in maintaining numerical stability under training. -* No gradient explosions or plateaus occurred (loss range ≈ 1.9 → 1.2). +* No gradient explosions or plateaus occurred. 2. Training Loss (medium zoom) @@ -208,7 +246,7 @@ This mid-range view highlights the epoch-to-epoch change more clearly: * No regression in ROUGE-Lsum, evidence that the checkpoint selected (highest ROUGE-Lsum) indeed corresponds to the global optimum seen during training. Overall, Brain-T5 demonstrates reliable convergence and solid generalisation across validation and test splits. -The model maintains smooth training dynamics and rising ROUGE performance without evidence of overfitting or divergence — validating the correctness of the pipeline in train.py and the dataset tokenization logic in dataset.py +The model maintains smooth training dynamics and rising ROUGE performance without evidence of overfitting or divergence — validating the correctness of the pipeline in train.py and the dataset tokenization logic in dataset.py. Furthermore, doing full-passes over the dataset converges to a higher set of ROUGE values, making longer passes - less epochs preferrable over smaller passes - more epochs. ## Dataset Brain-T5 is trained on the BioLaySumm 2025 – LaymanRRG (Open-Source Track) dataset, hosted on Hugging Face under the identifier [BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track](https://huggingface.co/datasets/BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track). @@ -220,9 +258,8 @@ Each entry contains a technical radiology report paired with a human-written lay Each example includes two main fields: -Column Description -`radiology_report`: The source input - detailed, jargon-heavy text extracted from radiology or clinical notes. -`layman_report`: The target output - a simplified explanation written for a general audience. +* `radiology_report`: The source input - detailed, jargon-heavy text extracted from radiology or clinical notes. +* `layman_report`: The target output - a simplified explanation written for a general audience. During preprocessing, dataset.py automatically prefixes each input with "summarize: " for FLAN-T5 instruction consistency, tokenizes both columns using the model’s tokenizer, and pads sequences for batch training. @@ -230,11 +267,11 @@ During preprocessing, dataset.py automatically prefixes each input with "summari In train.py, datasets are loaded via `make_datasets(...)` which: -Fetches all splits (train, validation, test) directly from Hugging Face. +* Fetches all splits (train, validation, test) directly from Hugging Face. -Optionally performs an 80/10/10 self-split when the open-source test set lacks reference summaries (`--self_split` flag). +* Optionally performs an 80/10/10 self-split when the open-source test set lacks reference summaries (`--self_split` flag). -Encodes all samples into token IDs (input_ids, attention_mask, labels) ready for PyTorch training. +* Encodes all samples into token IDs (input_ids, attention_mask, labels) ready for PyTorch training. A custom Seq2SeqCollatorFast batches and pads sequences efficiently, ensuring label alignment and correct masking for loss computation. This design minimises preprocessing overhead and keeps I/O throughput optimal even on smaller consumer GPUs. @@ -243,11 +280,11 @@ This design minimises preprocessing overhead and keeps I/O throughput optimal ev BioLaySumm provides: -Authentic biomedical phrasing, exposing the model to realistic clinical structure and terminology. +* Authentic biomedical phrasing, exposing the model to realistic clinical structure and terminology. -Human-validated lay summaries, ensuring stylistic and semantic accuracy for non-expert readability. +* Human-validated lay summaries, ensuring stylistic and semantic accuracy for non-expert readability. -Consistent formatting, ideal for instruction-based models like FLAN-T5 that thrive on aligned input/output pairs. +* Consistent formatting, ideal for instruction-based models like FLAN-T5 that thrive on aligned input/output pairs. Together, these qualities make BioLaySumm the ideal foundation for training Brain-T5 to bridge the gap between clinical documentation and human-understandable summaries. @@ -259,7 +296,6 @@ T5 (Text-to-Text Transfer Transformer) is a transformer model built completely o The super summarised explanation on how the model works (provided by OpenAI's ChatGPT) is: -> This is the quoted text from ChatGPT. 1. Tokenise → Embed → + Position. Words become vectors, add position info so the model knows order. 2. Encoder (repeated N×): @@ -275,8 +311,6 @@ The super summarised explanation on how the model works (provided by OpenAI's Ch 5. Linear → Softmax: turn the decoder’s last vector into a probability over the vocabulary; pick the next token; loop 4–5 until done. -> End quote. - This is a representation of how T5 unifies all forms of text-to-text input/output to heavily generalise its use case and simply learning. From bd9a93b3ca49c9e8f4af7c3620af5112a82454ea Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 21:31:23 +1000 Subject: [PATCH 33/41] fixed some unnecessary comments to improve clarity --- recognition/Project13-TristanGreen/modules.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recognition/Project13-TristanGreen/modules.py b/recognition/Project13-TristanGreen/modules.py index ffb293ff7..b53dfbf6f 100644 --- a/recognition/Project13-TristanGreen/modules.py +++ b/recognition/Project13-TristanGreen/modules.py @@ -28,12 +28,12 @@ def get_tokenizer(name: str = "google/flan-t5-base"): def load_base_model( name: str = "google/flan-t5-base", - dtype: Optional[torch.dtype] = torch.float16, # <-- use 'dtype', not 'torch_dtype' + dtype: Optional[torch.dtype] = torch.float16, device_map: Optional[str] = None, ): model = AutoModelForSeq2SeqLM.from_pretrained( name, - dtype=dtype, # <-- fixes deprecation + dtype=dtype, device_map=device_map, ) if getattr(model.config, "decoder_start_token_id", None) is None: From aec136fd40ab19f5c4fae13576618ac8f68d2faa Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 21:52:24 +1000 Subject: [PATCH 34/41] testing with another computer, fixed requirements and installation steps. --- recognition/Project13-TristanGreen/README.md | 7 ++++--- .../Project13-TristanGreen/requirements.txt | 18 ++++++++++-------- 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 4c2aafc12..a9f25e189 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -46,16 +46,17 @@ Brain-T5 is a major step toward bridging the gap between the average person and git clone -b topic-recognition https://github.com/TPGCIG/PatternAnalysis-2025/ # Change directory to the Brain-T5 one. -cd recognition/Project13-TristanGreen +cd PatternAnalysis-2025/recognition/Project13-TristanGreen # Install the dependencies. pip install -r requirements.txt ``` -You're now ready to go! +`torch` is not included in this install. You must go to [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally). This project uses PyTorch 2.8.0 and this project **strongly recommends** the use of CUDA 12.6. -## Training Usage +You're now ready to go! +## Training Usages ### 1) Quick-start commands **Hugging Face (BioLaySumm)** diff --git a/recognition/Project13-TristanGreen/requirements.txt b/recognition/Project13-TristanGreen/requirements.txt index 09bd267d7..586e41917 100644 --- a/recognition/Project13-TristanGreen/requirements.txt +++ b/recognition/Project13-TristanGreen/requirements.txt @@ -1,8 +1,10 @@ -torch -transformers -peft -evaluate -datasets -tqdm -numpy - +transformers==4.57.0 +peft==0.17.1 +evaluate==0.4.6 +datasets==4.1.1 +tqdm==4.67.1 +numpy==2.2.5 +matplotlib==3.10.5 +absl-py==2.3.1 +nltk==3.9.2 +rouge_score==0.1.2 \ No newline at end of file From 239dbfb7cd53a16132b722ee8299fb24fd732b5b Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 22:01:47 +1000 Subject: [PATCH 35/41] added instructions on how to use chat.py --- recognition/Project13-TristanGreen/README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index a9f25e189..e89b9817d 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -56,6 +56,9 @@ pip install -r requirements.txt You're now ready to go! +### Dependencies + + ## Training Usages ### 1) Quick-start commands @@ -110,6 +113,15 @@ You should also see console logs during training like: ``` ### 6) Use the trained adapters + +We **highly recommend** using `chat.py` to talk to the model you've trained: + +```bash +python chat.py --model_dir runs/ +``` + +`predict.py` is also available: + Single text: ```bash python predict.py --adapter_dir runs/ --text "Put clinical text here" --fp16 @@ -119,6 +131,7 @@ Batch JSONL: python predict.py --adapter_dir runs/ --jsonl dev.jsonl --input_col report --out_path predictions.jsonl ``` + ### 7) General usage tips - If CUDA OOM: increase `--accum`, or drop `--fp16` if your GPU cannot handle the defaults. - If ROUGE is flat, your data columns are probably wrong. Print a few samples. From 7b3ea6b09b18df951b74fa44a75126848f060ea1 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 22:02:21 +1000 Subject: [PATCH 36/41] added comment headers to each file to clarify the functionality --- recognition/Project13-TristanGreen/chat.py | 13 +++++++++++++ recognition/Project13-TristanGreen/dataset.py | 18 ++++++++++++++++-- recognition/Project13-TristanGreen/modules.py | 16 +++++++++++++++- recognition/Project13-TristanGreen/predict.py | 17 ++++++++++++++++- recognition/Project13-TristanGreen/train.py | 19 ++++++++++++++++++- 5 files changed, 78 insertions(+), 5 deletions(-) diff --git a/recognition/Project13-TristanGreen/chat.py b/recognition/Project13-TristanGreen/chat.py index 23830de9d..6d4f7639c 100644 --- a/recognition/Project13-TristanGreen/chat.py +++ b/recognition/Project13-TristanGreen/chat.py @@ -1,3 +1,16 @@ +# ------------------------------------------------------------ +# Interactive CLI for Brain-T5 (Chat Mode) +# ----------------------------------------------------------- +# Description: +# Lightweight interface for real-time summarization queries. +# Runs inference loop over the fine-tuned LoRA FLAN-T5 model. +# +# Usage: +# $ python chat.py --model_dir runs/flan_t5_base_lora_biolaysumm +# +# Notes: +# - Press Enter to re-prompt; type 'exit' or 'quit' to stop. +# ------------------------------------------------------------ import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from peft import PeftModel diff --git a/recognition/Project13-TristanGreen/dataset.py b/recognition/Project13-TristanGreen/dataset.py index fab2f09c2..c92d2ec51 100644 --- a/recognition/Project13-TristanGreen/dataset.py +++ b/recognition/Project13-TristanGreen/dataset.py @@ -1,5 +1,19 @@ - -# dataset.py — locked to BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track +# ------------------------------------------------------------ +# Dataset Loader and Preprocessing for Brain-T5 +# ----------------------------------------------------------- +# Description: +# Handles dataset intake and preprocessing for FLAN-T5 fine-tuning. +# Supports Hugging Face (BioLaySumm) datasets, CSV, or JSONL inputs. +# +# Key Components: +# - make_datasets(): loads and tokenizes splits (train/val/test). +# - Seq2SeqCollatorFast: dynamic padding & label masking for T5. +# +# Notes: +# - Automatically prefixes "summarize: " to each input. +# - Pads to model’s max token length. +# - Masks tokens in labels with -100 for CrossEntropyLoss. +# ------------------------------------------------------------ from __future__ import annotations from typing import Optional, List, Dict import torch diff --git a/recognition/Project13-TristanGreen/modules.py b/recognition/Project13-TristanGreen/modules.py index b53dfbf6f..de74b7838 100644 --- a/recognition/Project13-TristanGreen/modules.py +++ b/recognition/Project13-TristanGreen/modules.py @@ -1,4 +1,18 @@ -#modules.py +# ------------------------------------------------------------ +# Model Utilities for Brain-T5 +# ----------------------------------------------------------- +# Description: +# Provides helper functions for loading base models and attaching +# LoRA adapters to target layers of FLAN-T5. +# +# Key Functions: +# - load_base_model(): loads pretrained T5/FLAN-T5 with dtype control. +# - attach_lora(): injects trainable low-rank adapters for fine-tuning. +# +# Notes: +# - Uses PEFT (Parameter-Efficient Fine-Tuning) via Hugging Face. +# - Keeps original model frozen except LoRA-injected parameters. +# ------------------------------------------------------------ """ General design ideas are that the datasets are defensively imported and are not taken for granted since this is public software. All imports have guardrails. diff --git a/recognition/Project13-TristanGreen/predict.py b/recognition/Project13-TristanGreen/predict.py index 22a7ceae3..126579ab1 100644 --- a/recognition/Project13-TristanGreen/predict.py +++ b/recognition/Project13-TristanGreen/predict.py @@ -1,4 +1,19 @@ -# predict.py +# ------------------------------------------------------------ +# Prediction and Inference for Brain-T5 +# ----------------------------------------------------------- +# Description: +# Generates summaries from fine-tuned LoRA adapters. +# Supports both single-text (--text) and batch (--jsonl) modes. +# +# Key Functions: +# - load_model(): loads base + LoRA adapter for inference. +# - generate_batch(): batched generation with beam search. +# +# Notes: +# - Outputs JSONL with 'prediction' field appended to each input. +# - Uses max_new_tokens and num_beams for generation control. +# ------------------------------------------------------------ + import os, argparse, json from typing import List import torch diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index 9ad8b67d8..632b5f5b0 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -1,4 +1,21 @@ -# train.py — locked to BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track +# ------------------------------------------------------------ +# Brain-T5: FLAN-T5 + LoRA Fine-Tuning Pipeline +# ----------------------------------------------------------- +# Description: +# Main training script for Brain-T5. Handles dataset loading, +# LoRA adapter attachment, training loop, logging, and evaluation. +# +# Key Functions: +# - run_eval(): computes ROUGE scores on validation/test splits. +# - log_val_rouge_row(): logs per-epoch ROUGE metrics to CSV. +# - plot_loss_curve(), plot_val_rouge_curve(): generate plots. +# +# Notes: +# - Uses AdamW + cosine schedule. +# - Gradient accumulation supported via --accum. +# - Mixed precision enabled via torch.amp. +# - Best model checkpoint chosen by highest ROUGE-Lsum. +# ------------------------------------------------------------ import os, json, math, argparse, random, time, uuid, csv from typing import Optional import numpy as np From 7f4bd7d822ecd0f7ee8953e2c9b51e26db1d40cc Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 22:10:41 +1000 Subject: [PATCH 37/41] small edits discussing data split functionalities --- recognition/Project13-TristanGreen/README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index e89b9817d..97b706826 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -74,6 +74,8 @@ python train.py --train_source hf --train_path train --val_source hf --val ### 3) What the script actually does - Builds tokenizer + datasets via `make_datasets(...)` with `hf` and columns (`--input_col`, `--target_col`). +- Performs an **80/10/10 train–validation–test split** automatically when `--self_split` is used, ensuring there is no data leakage between training and evaluation sets. + - Attaches **LoRA** adapters to FLAN‑T5 and trains with AdamW + cosine schedule. - Evaluates with **ROUGE** at epoch. - Saves best adapters + tokenizer to `--output_dir`, along with `metrics.json`, `train_log.csv` and graphs for `loss` and `ROUGE` scores per-epoch. @@ -83,6 +85,10 @@ python train.py --train_source hf --train_path train --val_source hf --val - **Optim**: `--lr`, `--weight_decay`, `--warmup_steps`, `--clip` - **LoRA**: `--lora_r`, `--lora_alpha`, `--lora_dropout` - **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams` +- **Data Splitting**: + - `--self_split` automatically performs an **80/10/10** train–validation–test division when a pre-defined test split is unavailable. + - Custom paths can be provided via `--train_path`, `--val_path`, and `--test_path` to manually control dataset partitions. + - Prevents **data leakage** by ensuring all splits are loaded and cached independently. - **Misc**: `--epochs`, `--seed`, `--fp16` ### 5) Outputs @@ -114,7 +120,7 @@ You should also see console logs during training like: ### 6) Use the trained adapters -We **highly recommend** using `chat.py` to talk to the model you've trained: +We **highly recommend** using `chat.py` to talk to the model you've trained (heavily inspired by OpenAI's ChatGPT): ```bash python chat.py --model_dir runs/ @@ -290,6 +296,9 @@ In train.py, datasets are loaded via `make_datasets(...)` which: A custom Seq2SeqCollatorFast batches and pads sequences efficiently, ensuring label alignment and correct masking for loss computation. This design minimises preprocessing overhead and keeps I/O throughput optimal even on smaller consumer GPUs. +Users can also manually override dataset splits by specifying `--train_path`, `--val_path`, and `--test_path` when providing local data sources. +This makes the training pipeline flexible for custom or extended datasets while maintaining strict isolation between splits. + ### Why BioLaySumm? BioLaySumm provides: From 4363c5dc98279285a84cc400b939c2c803143e66 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 22:52:39 +1000 Subject: [PATCH 38/41] added information on how to use venv in installation --- recognition/Project13-TristanGreen/README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 97b706826..24880f77e 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -41,6 +41,8 @@ Brain-T5 is a major step toward bridging the gap between the average person and ## Installation: +We recommend using a virtual environment for your install. See [here](https://www.w3schools.com/python/python_virtualenv.asp) for a tutorial in Windows/MacOS/Linux for making a virtual environment. + ``` # First, clone the repository and at the same time, checkout the topic-recognition branch. git clone -b topic-recognition https://github.com/TPGCIG/PatternAnalysis-2025/ @@ -48,6 +50,8 @@ git clone -b topic-recognition https://github.com/TPGCIG/PatternAnalysis-2025/ # Change directory to the Brain-T5 one. cd PatternAnalysis-2025/recognition/Project13-TristanGreen +# Here is where you will access your virtual environment - check the linked tutorial for your OS. It is not required though. + # Install the dependencies. pip install -r requirements.txt ``` @@ -62,6 +66,9 @@ You're now ready to go! ## Training Usages ### 1) Quick-start commands + + + **Hugging Face (BioLaySumm)** ```bash python train.py --output_dir [dir_name] @@ -69,7 +76,7 @@ python train.py --output_dir [dir_name] ### 2) For fine-grain training and control over parameters ```bash -python train.py --train_source hf --train_path train --val_source hf --val_path validation --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 --fp16 +python train.py --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 --accum 16 --epochs 3 --lr 2e-4 ``` ### 3) What the script actually does From 61bef03008154a84d13bad35257d98ab6c507298 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Sun, 2 Nov 2025 23:09:27 +1000 Subject: [PATCH 39/41] added comments around codebase to improve clarity to different functions and major loops --- recognition/Project13-TristanGreen/chat.py | 28 +++++----- recognition/Project13-TristanGreen/dataset.py | 42 ++++++++------ recognition/Project13-TristanGreen/modules.py | 45 ++++++++------- recognition/Project13-TristanGreen/predict.py | 40 ++++++++----- recognition/Project13-TristanGreen/train.py | 56 ++++++++++++------- 5 files changed, 125 insertions(+), 86 deletions(-) diff --git a/recognition/Project13-TristanGreen/chat.py b/recognition/Project13-TristanGreen/chat.py index 6d4f7639c..74add8607 100644 --- a/recognition/Project13-TristanGreen/chat.py +++ b/recognition/Project13-TristanGreen/chat.py @@ -1,16 +1,18 @@ -# ------------------------------------------------------------ -# Interactive CLI for Brain-T5 (Chat Mode) -# ----------------------------------------------------------- -# Description: -# Lightweight interface for real-time summarization queries. -# Runs inference loop over the fine-tuned LoRA FLAN-T5 model. -# -# Usage: -# $ python chat.py --model_dir runs/flan_t5_base_lora_biolaysumm -# -# Notes: -# - Press Enter to re-prompt; type 'exit' or 'quit' to stop. -# ------------------------------------------------------------ +""" +------------------------------------------------------------ + Interactive CLI for Brain-T5 (Chat Mode) + ----------------------------------------------------------- + Description: + Lightweight interface for real-time summarization queries. + Runs inference loop over the fine-tuned LoRA FLAN-T5 model. + + Usage: + $ python chat.py --model_dir runs/flan_t5_base_lora_biolaysumm + + Notes: + - Press Enter to re-prompt; type 'exit' or 'quit' to stop. +------------------------------------------------------------ +""" import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from peft import PeftModel diff --git a/recognition/Project13-TristanGreen/dataset.py b/recognition/Project13-TristanGreen/dataset.py index c92d2ec51..eb60cdf6a 100644 --- a/recognition/Project13-TristanGreen/dataset.py +++ b/recognition/Project13-TristanGreen/dataset.py @@ -1,19 +1,21 @@ -# ------------------------------------------------------------ -# Dataset Loader and Preprocessing for Brain-T5 -# ----------------------------------------------------------- -# Description: -# Handles dataset intake and preprocessing for FLAN-T5 fine-tuning. -# Supports Hugging Face (BioLaySumm) datasets, CSV, or JSONL inputs. -# -# Key Components: -# - make_datasets(): loads and tokenizes splits (train/val/test). -# - Seq2SeqCollatorFast: dynamic padding & label masking for T5. -# -# Notes: -# - Automatically prefixes "summarize: " to each input. -# - Pads to model’s max token length. -# - Masks tokens in labels with -100 for CrossEntropyLoss. -# ------------------------------------------------------------ +""" +------------------------------------------------------------ + Dataset Loader and Preprocessing for Brain-T5 + ----------------------------------------------------------- + Description: + Handles dataset intake and preprocessing for FLAN-T5 fine-tuning. + Supports Hugging Face (BioLaySumm) datasets, CSV, or JSONL inputs. + + Key Components: + - make_datasets(): loads and tokenizes splits (train/val/test). + - Seq2SeqCollatorFast: dynamic padding & label masking for T5. + + Notes: + - Automatically prefixes "summarize: " to each input. + - Pads to model’s max token length. + - Masks tokens in labels with -100 for CrossEntropyLoss. +------------------------------------------------------------ +""" from __future__ import annotations from typing import Optional, List, Dict import torch @@ -25,6 +27,8 @@ INPUT_COL = "radiology_report" TARGET_COL = "layman_report" +# Collator: batch pad inputs/labels and map pad tokens in labels to -100 (ignored by CE loss). +# pad_to_multiple_of lets you round sequence lengths (e.g., to 8/16/32) for Tensor Core efficiency. class Seq2SeqCollatorFast: def __init__(self, tokenizer, label_pad_token_id=-100, pad_to_multiple_of=None): self.tok = tokenizer @@ -41,6 +45,7 @@ def _maybe_pad_to_multiple(self, tensor, pad_value): return torch.nn.functional.pad(tensor, (0, add), value=pad_value) def __call__(self, feats: List[Dict[str, torch.Tensor]]): + # IMPORTANT: convert tokenizer pad tokens in labels to -100 so loss ignores padded positions. ids = [f["input_ids"] if isinstance(f["input_ids"], torch.Tensor) else torch.tensor(f["input_ids"]) for f in feats] am = [f["attention_mask"] if isinstance(f["attention_mask"], torch.Tensor) else torch.tensor(f["attention_mask"]) for f in feats] labs = [f["labels"] if isinstance(f["labels"], torch.Tensor) else torch.tensor(f["labels"]) for f in feats] @@ -56,7 +61,8 @@ def __call__(self, feats: List[Dict[str, torch.Tensor]]): labs = self._maybe_pad_to_multiple(labs, self.label_pad_token_id) return {"input_ids": ids, "attention_mask": am, "labels": labs} - +# Build tokenizer + HF datasets with optional self-split (80/10/10). +# Ensures input instruction prefix and truncation to max lengths. def make_datasets( tokenizer_name: str = "google/flan-t5-base", train_split: str = "train", @@ -80,6 +86,7 @@ def make_datasets( from datasets import DatasetDict + # Vectorize one batch: add instruction prefix, tokenize src/tgt independently, attach 'labels'. if self_split: base = ds["train"].train_test_split(test_size=self_split_test, seed=self_split_seed) train_part = base["train"] @@ -98,6 +105,7 @@ def make_datasets( if INPUT_COL not in cols or TARGET_COL not in cols: raise KeyError(f"Expected columns '{INPUT_COL}', '{TARGET_COL}' in split '{split}', found {cols}") + # Vectorize one batch: add instruction prefix, tokenize src/tgt independently, attach 'labels'. def encode_batch(batch): srcs = [prefix_text + s for s in batch[INPUT_COL]] enc = tok(srcs, max_length=max_input_len, truncation=True) diff --git a/recognition/Project13-TristanGreen/modules.py b/recognition/Project13-TristanGreen/modules.py index de74b7838..896768ae3 100644 --- a/recognition/Project13-TristanGreen/modules.py +++ b/recognition/Project13-TristanGreen/modules.py @@ -1,21 +1,19 @@ -# ------------------------------------------------------------ -# Model Utilities for Brain-T5 -# ----------------------------------------------------------- -# Description: -# Provides helper functions for loading base models and attaching -# LoRA adapters to target layers of FLAN-T5. -# -# Key Functions: -# - load_base_model(): loads pretrained T5/FLAN-T5 with dtype control. -# - attach_lora(): injects trainable low-rank adapters for fine-tuning. -# -# Notes: -# - Uses PEFT (Parameter-Efficient Fine-Tuning) via Hugging Face. -# - Keeps original model frozen except LoRA-injected parameters. -# ------------------------------------------------------------ """ -General design ideas are that the datasets are defensively imported and are not -taken for granted since this is public software. All imports have guardrails. +------------------------------------------------------------ + Model Utilities for Brain-T5 + ----------------------------------------------------------- + Description: + Provides helper functions for loading base models and attaching + LoRA adapters to target layers of FLAN-T5. + + Key Functions: + - load_base_model(): loads pretrained T5/FLAN-T5 with dtype control. + - attach_lora(): injects trainable low-rank adapters for fine-tuning. + + Notes: + - Uses PEFT (Parameter-Efficient Fine-Tuning) via Hugging Face. + - Keeps original model frozen except LoRA-injected parameters. +------------------------------------------------------------ """ from __future__ import annotations from typing import Optional, Dict, Any, List @@ -32,14 +30,15 @@ except Exception: PEFT_AVAILABLE = False - +# Use fast tokenizer; default pad_token from eos_token if missing (required by T5 decoding). def get_tokenizer(name: str = "google/flan-t5-base"): tok = AutoTokenizer.from_pretrained(name, use_fast=True) if tok.pad_token is None: tok.pad_token = tok.eos_token return tok - +# Load FLAN-T5 with dtype/device_map options. +# Ensure decoder_start_token_id is set so generation starts from a valid token. def load_base_model( name: str = "google/flan-t5-base", dtype: Optional[torch.dtype] = torch.float16, @@ -54,7 +53,8 @@ def load_base_model( model.config.decoder_start_token_id = model.config.pad_token_id return model - +# Inject LoRA on attention projections (q/k/v/o). Bias=none keeps adapter minimal. +# r/alpha/dropout control rank, scaling, and regularization of the adapters. def attach_lora(model, r: int = 8, alpha: int = 16, dropout: float = 0.05, target_modules: Optional[List[str]] = None): if not PEFT_AVAILABLE: raise RuntimeError("peft not installed. `pip install peft` to use LoRA.") @@ -66,7 +66,10 @@ def attach_lora(model, r: int = 8, alpha: int = 16, dropout: float = 0.05, targe ) return get_peft_model(model, cfg) - +# Convenience generation wrapper (batched): +# - Applies optional "summarize: " prefix. +# - Pads/truncates, moves to device, decodes without special tokens. +# - Beam search defaults tuned for readability over speed. @torch.no_grad() def generate( model, diff --git a/recognition/Project13-TristanGreen/predict.py b/recognition/Project13-TristanGreen/predict.py index 126579ab1..1e5efcb2b 100644 --- a/recognition/Project13-TristanGreen/predict.py +++ b/recognition/Project13-TristanGreen/predict.py @@ -1,25 +1,28 @@ -# ------------------------------------------------------------ -# Prediction and Inference for Brain-T5 -# ----------------------------------------------------------- -# Description: -# Generates summaries from fine-tuned LoRA adapters. -# Supports both single-text (--text) and batch (--jsonl) modes. -# -# Key Functions: -# - load_model(): loads base + LoRA adapter for inference. -# - generate_batch(): batched generation with beam search. -# -# Notes: -# - Outputs JSONL with 'prediction' field appended to each input. -# - Uses max_new_tokens and num_beams for generation control. -# ------------------------------------------------------------ +""" +------------------------------------------------------------ + Prediction and Inference for Brain-T5 + ----------------------------------------------------------- + Description: + Generates summaries from fine-tuned LoRA adapters. + Supports both single-text (--text) and batch (--jsonl) modes. + Key Functions: + - load_model(): loads base + LoRA adapter for inference. + - generate_batch(): batched generation with beam search. + + Notes: + - Outputs JSONL with 'prediction' field appended to each input. + - Uses max_new_tokens and num_beams for generation control. +------------------------------------------------------------ +""" import os, argparse, json from typing import List import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from peft import PeftModel +# Load tokenizer from adapter_dir (ensures identical preproc as training), attach LoRA onto base. +# dtype=float16 only when CUDA+--fp16; always move model to device and set eval(). def load_model(adapter_dir: str, base_model: str, fp16: bool): tok = AutoTokenizer.from_pretrained(adapter_dir) dtype = torch.float16 if (fp16 and torch.cuda.is_available()) else torch.float32 @@ -35,6 +38,7 @@ def chunk(lst: List[str], n: int): for i in range(0, len(lst), n): yield lst[i:i+n] +# Generate a batch with beam search; always prefix with instruction to match training distribution. def generate_batch(model, tok, device, texts: List[str], max_in: int, max_new: int, beams: int, prefix: str): batch = [prefix + t for t in texts] enc = tok(batch, return_tensors="pt", truncation=True, max_length=max_in, padding=True).to(device) @@ -66,6 +70,12 @@ def main(): ap.add_argument("--fp16", action="store_true") args = ap.parse_args() + # Modes: + # --text "..." -> print single summary to stdout + # --jsonl file.jsonl -> stream predictions and write to --out_path + # --input_col selects field in JSONL to summarize (default: 'report') + + tok, model, device = load_model(args.adapter_dir, args.base_model, args.fp16) # single text mode diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index 632b5f5b0..3594bf52c 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -1,21 +1,23 @@ -# ------------------------------------------------------------ -# Brain-T5: FLAN-T5 + LoRA Fine-Tuning Pipeline -# ----------------------------------------------------------- -# Description: -# Main training script for Brain-T5. Handles dataset loading, -# LoRA adapter attachment, training loop, logging, and evaluation. -# -# Key Functions: -# - run_eval(): computes ROUGE scores on validation/test splits. -# - log_val_rouge_row(): logs per-epoch ROUGE metrics to CSV. -# - plot_loss_curve(), plot_val_rouge_curve(): generate plots. -# -# Notes: -# - Uses AdamW + cosine schedule. -# - Gradient accumulation supported via --accum. -# - Mixed precision enabled via torch.amp. -# - Best model checkpoint chosen by highest ROUGE-Lsum. -# ------------------------------------------------------------ +""" +------------------------------------------------------------ + Brain-T5: FLAN-T5 + LoRA Fine-Tuning Pipeline + ----------------------------------------------------------- + Description: + Main training script for Brain-T5. Handles dataset loading, + LoRA adapter attachment, training loop, logging, and evaluation. + + Key Functions: + - run_eval(): computes ROUGE scores on validation/test splits. + - log_val_rouge_row(): logs per-epoch ROUGE metrics to CSV. + - plot_loss_curve(), plot_val_rouge_curve(): generate plots. + + Notes: + - Uses AdamW + cosine schedule. + - Gradient accumulation supported via --accum. + - Mixed precision enabled via torch.amp. + - Best model checkpoint chosen by highest ROUGE-Lsum. +------------------------------------------------------------ +""" import os, json, math, argparse, random, time, uuid, csv from typing import Optional import numpy as np @@ -50,6 +52,11 @@ def set_seed(seed: int = 1337): if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) +# Eval loop: generate summaries for a dataloader and compute ROUGE. +# Notes: +# - We re-enable use_cache for fast generation. +# - Convert label pad (-100) back to tokenizer.pad_token_id before decoding refs. +# - no_repeat_ngram_size=3 reduces trivial repetition. def run_eval(model, tokenizer, loader: Optional[DataLoader], device, args, rouge_metric): if loader is None: return None @@ -61,6 +68,7 @@ def run_eval(model, tokenizer, loader: Optional[DataLoader], device, args, rouge with torch.inference_mode(): for vb in tqdm(loader, desc="Eval", unit="batch", dynamic_ncols=True): vb = {k: v.to(device) for k, v in vb.items()} + # Beam search generation for evaluation (deterministic-ish) gen_out = model.generate( input_ids=vb["input_ids"], attention_mask=vb["attention_mask"], @@ -87,6 +95,8 @@ def run_eval(model, tokenizer, loader: Optional[DataLoader], device, args, rouge RUN_ID = os.environ.get("RUN_ID", str(uuid.uuid4())[:8]) +# Persist per-epoch validation ROUGE to CSV for plotting and auditing. +# If multiple runs append to same file, we keep last row per epoch when plotting. def log_val_rouge_row(run_dir, epoch, scores): """ Append one row per epoch: @@ -113,6 +123,8 @@ def log_val_rouge_row(run_dir, epoch, scores): # Plotting helpers # ----------------------- +# Plot validation ROUGE vs epoch. +# Robust to restarts: we select the *latest* row per epoch (by timestamp) to avoid stale re-runs. def plot_val_rouge_curve(run_dir): """ Plot Validation ROUGE vs Epoch (robust): @@ -340,7 +352,7 @@ def main(): os.makedirs(args.output_dir, exist_ok=True) set_seed(args.seed) - # Start fresh logs (truncate; do NOT pre-write mismatched headers) + # Fresh logs for this run (truncate old content so headers/steps align with current run). open(os.path.join(args.output_dir, "train_log.csv"), "w").close() open(os.path.join(args.output_dir, "history_val.csv"), "w").close() @@ -399,7 +411,10 @@ def main(): log_file, log_writer = csv_logger(os.path.join(args.output_dir, "train_log.csv")) global_step = 0 - # Optim + sched + # Optimizer & schedule: + # - AdamW with weight decay. + # - Cosine schedule with ~6% warmup (capped by --warmup_steps). + # - GradScaler enabled only when --fp16 on CUDA. optim = AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) total_steps = math.ceil(len(train_loader) / args.accum) * args.epochs warmup = min(args.warmup_steps, int(0.06 * total_steps)) @@ -442,6 +457,7 @@ def main(): avg_loss = running / max(1, (step_in_epoch // args.accum)) elapsed = time.time() - start_time sps = (step_in_epoch * args.batch_size) / max(1e-6, elapsed) + # Live progress: smoothed loss and samples/sec for quick sanity checks. pbar.set_postfix({"loss": f"{avg_loss:.4f}", "sps": f"{sps:.1f}"}) print(f"[epoch {epoch}] train_loss={avg_loss:.4f}") From 032ce58d1ef9098f7471ecb20ebe223e7a87ac2c Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 3 Nov 2025 01:16:25 +1000 Subject: [PATCH 40/41] slight edits to remove unnecessary command line args from README and train --- recognition/Project13-TristanGreen/README.md | 6 ++++-- recognition/Project13-TristanGreen/train.py | 5 +---- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 24880f77e..3e9fe3541 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -88,13 +88,15 @@ python train.py --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 - Saves best adapters + tokenizer to `--output_dir`, along with `metrics.json`, `train_log.csv` and graphs for `loss` and `ROUGE` scores per-epoch. ### 4) Arguments + - **Batching**: `--batch_size`, `--accum` (effective batch = batch_size × accum) - **Optim**: `--lr`, `--weight_decay`, `--warmup_steps`, `--clip` - **LoRA**: `--lora_r`, `--lora_alpha`, `--lora_dropout` - **Eval**: `--eval_batch_size`, `--eval_max_new_tokens`, `--eval_beams` +- **Limiters**: `--max_train_samples`, `--max_eval_samples`, `--max_test_samples` - **Data Splitting**: - - `--self_split` automatically performs an **80/10/10** train–validation–test division when a pre-defined test split is unavailable. - - Custom paths can be provided via `--train_path`, `--val_path`, and `--test_path` to manually control dataset partitions. + - `--self_split_#` automatically performs an **80/10/10** train–validation–test division when a pre-defined test split is unavailable. + - `--train_split`, `--val_split`, `--test_split` can be used to manually split the dataset. - Prevents **data leakage** by ensuring all splits are loaded and cached independently. - **Misc**: `--epochs`, `--seed`, `--fp16` diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index 3594bf52c..e754614b9 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -311,9 +311,6 @@ def main(): p.add_argument("--val_split", default="validation") p.add_argument("--test_split", default="test") - # seq lengths + prefix - p.add_argument("--prefix", default="summarize: ") - # training p.add_argument("--epochs", type=int, default=5) p.add_argument("--lr", type=float, default=2e-4) @@ -364,7 +361,7 @@ def main(): test_split=args.test_split, max_input_len=1024, max_target_len=256, - prefix_text=args.prefix, + prefix_text="summarise", self_split=args.self_split, self_split_val=args.self_split_val, self_split_test=args.self_split_test, From 22171a733068160ab6ef0339e8e744d108d70692 Mon Sep 17 00:00:00 2001 From: TPGCIG Date: Mon, 3 Nov 2025 02:36:51 +1000 Subject: [PATCH 41/41] typo in train.py, added in justification for parameters in README --- recognition/Project13-TristanGreen/README.md | 54 +++++++++++--------- recognition/Project13-TristanGreen/train.py | 2 +- 2 files changed, 32 insertions(+), 24 deletions(-) diff --git a/recognition/Project13-TristanGreen/README.md b/recognition/Project13-TristanGreen/README.md index 3e9fe3541..5cab90397 100644 --- a/recognition/Project13-TristanGreen/README.md +++ b/recognition/Project13-TristanGreen/README.md @@ -60,16 +60,21 @@ pip install -r requirements.txt You're now ready to go! -### Dependencies - +### Dependencies (Named as they are installable via pip) +* transformers 4.57.0 +* peft 0.17.1 +* evaluate 0.4.6 +* datasets 4.1.1 +* tqdm 4.67.1 +* numpy 2.2.5 +* matplotlib 3.10.5 +* absl-py 2.3.1 +* nltk 3.9.2 +* rouge_score 0.1.2 ## Training Usages ### 1) Quick-start commands - - - -**Hugging Face (BioLaySumm)** ```bash python train.py --output_dir [dir_name] ``` @@ -80,7 +85,7 @@ python train.py --output_dir runs/flan_t5_base_lora_biolaysumm --batch_size 1 ``` ### 3) What the script actually does -- Builds tokenizer + datasets via `make_datasets(...)` with `hf` and columns (`--input_col`, `--target_col`). +- Builds tokenizer + datasets via `make_datasets(...)` with `hf`. - Performs an **80/10/10 train–validation–test split** automatically when `--self_split` is used, ensuring there is no data leakage between training and evaluation sets. - Attaches **LoRA** adapters to FLAN‑T5 and trains with AdamW + cosine schedule. @@ -277,6 +282,25 @@ This mid-range view highlights the epoch-to-epoch change more clearly: Overall, Brain-T5 demonstrates reliable convergence and solid generalisation across validation and test splits. The model maintains smooth training dynamics and rising ROUGE performance without evidence of overfitting or divergence — validating the correctness of the pipeline in train.py and the dataset tokenization logic in dataset.py. Furthermore, doing full-passes over the dataset converges to a higher set of ROUGE values, making longer passes - less epochs preferrable over smaller passes - more epochs. +### Pre-processing + +We keep the input text *as written* (no lowercasing, stop‑word removal or punctuation stripping) and rely on the FLAN‑T5 tokenizer to handle normalization. Concretely: + +- **Instruction prefix:** each input is prepended with a task prompt (e.g., `summarize: `) so the format matches FLAN‑T5’s instruction‑tuning. +- **Tokenization:** Hugging Face’s fast tokenizer for FLAN‑T5 (SentencePiece) encodes inputs/targets; the pad token is set to `` when missing (T5 requirement). Inputs are truncated/padded to **1024** tokens; targets to **256** tokens. +- **Label masking:** target padding positions are set to **-100** so they are ignored by the cross‑entropy loss during training. +- **Decoding safety:** we ensure `decoder_start_token_id` is defined so generation starts from a valid token. + +### Train/Validation/Test splits — justification + +- **Official splits when available.** If the dataset provides `train/validation/test`, we honor them exactly. +- **Self‑split when the test lacks references.** With `--self_split`, we create an **80/10/10** split *from the training partition only* to avoid leakage. A fixed random seed makes the partition reproducible. +- **Why 80/10/10 Split?** It gives the model the bulk of data for parameter estimation (80%), a sufficient **validation** slice (10%) for model selection/early‑stopping by ROUGE‑Lsum, and a **held‑out test** slice (10%) that is touched **once** at the end for unbiased reporting. Using different balances (e.g. 33/33/34) starves the component on training which requires the most compute, the training. If most of the dataset is committed to evaluation, the model starves and severely underperforms. The 20% used for ROUGE/Eval are valuable for evaluation, but evaluation does not dictate the quality of the model like training does, it only tests it. +- **Learning rate of 2e-4** - Conservative base LR for **LoRA‑only** updates on T5‑base. Stable with cosine decay and accumulation on small batches. +- **Weight_decay of 0.01** - Light L2 to regularise LoRA adapters (AdamW). Keeps updates from drifting without fighting low‑rank adaptation. +- **lora_r=8, lora_alpha=16, lora_dropout=0.05** Balanced adapter capacity vs stability. The **effective update scale** is roughly `lr × (alpha / r)` (= 2× lr here). `r=8` is a common sweet spot for T5‑base; small dropout helps generalisation without fighting instruction‑tuned priors. + + ## Dataset Brain-T5 is trained on the BioLaySumm 2025 – LaymanRRG (Open-Source Track) dataset, hosted on Hugging Face under the identifier [BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track](https://huggingface.co/datasets/BioLaySumm/BioLaySumm2025-LaymanRRG-opensource-track). @@ -292,22 +316,6 @@ Each example includes two main fields: During preprocessing, dataset.py automatically prefixes each input with "summarize: " for FLAN-T5 instruction consistency, tokenizes both columns using the model’s tokenizer, and pads sequences for batch training. -### Usage in Training - -In train.py, datasets are loaded via `make_datasets(...)` which: - -* Fetches all splits (train, validation, test) directly from Hugging Face. - -* Optionally performs an 80/10/10 self-split when the open-source test set lacks reference summaries (`--self_split` flag). - -* Encodes all samples into token IDs (input_ids, attention_mask, labels) ready for PyTorch training. - -A custom Seq2SeqCollatorFast batches and pads sequences efficiently, ensuring label alignment and correct masking for loss computation. -This design minimises preprocessing overhead and keeps I/O throughput optimal even on smaller consumer GPUs. - -Users can also manually override dataset splits by specifying `--train_path`, `--val_path`, and `--test_path` when providing local data sources. -This makes the training pipeline flexible for custom or extended datasets while maintaining strict isolation between splits. - ### Why BioLaySumm? BioLaySumm provides: diff --git a/recognition/Project13-TristanGreen/train.py b/recognition/Project13-TristanGreen/train.py index e754614b9..4072069c6 100644 --- a/recognition/Project13-TristanGreen/train.py +++ b/recognition/Project13-TristanGreen/train.py @@ -361,7 +361,7 @@ def main(): test_split=args.test_split, max_input_len=1024, max_target_len=256, - prefix_text="summarise", + prefix_text="summarize", self_split=args.self_split, self_split_val=args.self_split_val, self_split_test=args.self_split_test,

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