222 lines
8.6 KiB
Python
222 lines
8.6 KiB
Python
from os.path import expanduser
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import torch
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from transformers import (
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AutoModelForSequenceClassification,
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AutoTokenizer,
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DataCollatorWithPadding,
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TrainingArguments,
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)
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from gfun.vgfs.commons import Trainer
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from datasets import load_dataset, DatasetDict
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from transformers import Trainer
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import transformers
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import evaluate
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transformers.logging.set_verbosity_error()
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IWSLT_D_COLUMNS = ["text", "category", "rating", "summary", "title"]
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RAI_D_COLUMNS = ["id", "lang", "provider", "date", "title", "text", "str_label", "label"]
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def init_callbacks(patience=-1, nosave=False):
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callbacks = []
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if patience != -1 and not nosave:
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callbacks.append(transformers.EarlyStoppingCallback(early_stopping_patience=patience))
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return callbacks
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def init_model(model_name, nlabels):
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if model_name == "mbert":
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hf_name = "bert-base-multilingual-cased"
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elif model_name == "xlm-roberta":
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hf_name = "xlm-roberta-base"
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else:
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raise NotImplementedError
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tokenizer = AutoTokenizer.from_pretrained(hf_name)
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model = AutoModelForSequenceClassification.from_pretrained(hf_name, num_labels=nlabels)
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return tokenizer, model
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def main(args):
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tokenizer, model = init_model(args.model, args.nlabels)
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# data = load_dataset(
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# "json",
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# data_files={
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# "train": "local_datasets/webis-cls/all-domains/train.json",
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# "test": "local_datasets/webis-cls/all-domains/test.json",
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# },
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# )
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data = load_dataset(
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"csv",
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data_files = {
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# "train": expanduser("~/datasets/rai/csv/rai-no-it-train.csv"),
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# "test": expanduser("~/datasets/rai/csv/rai-no-it-test.csv")
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# "train": expanduser("~/datasets/rai/csv/rai-train.csv"),
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# "test": expanduser("~/datasets/rai/csv/rai-test-ita-labeled.csv")
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"train": expanduser("~/datasets/rai/csv/train-split-rai.csv"),
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"test": expanduser("~/datasets/rai/csv/test-split-rai-labeled.csv")
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}
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)
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def process_sample_iwslt(sample):
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inputs = sample["text"]
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ratings = [r - 1 for r in sample["rating"]]
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targets = torch.zeros((len(inputs), 3), dtype=float)
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lang_mapper = {
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lang: lang_id for lang_id, lang in enumerate(set(sample["lang"]))
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}
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lang_ids = [lang_mapper[l] for l in sample["lang"]]
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for i, r in enumerate(ratings):
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targets[i][r - 1] = 1
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model_inputs = tokenizer(inputs, max_length=128, truncation=True)
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model_inputs["labels"] = targets
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model_inputs["lang_ids"] = torch.tensor(lang_ids)
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return model_inputs
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def process_sample_rai(sample):
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inputs = [f"{title}. {text}" for title, text in zip(sample["title"], sample["text"])]
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labels = sample["label"]
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model_inputs = tokenizer(inputs, max_length=512, truncation=True) # TODO pre-process text cause there's a lot of noise in there...
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model_inputs["labels"] = labels
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return model_inputs
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data = data.map(
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process_sample_rai,
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batched=True,
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num_proc=4,
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load_from_cache_file=True,
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remove_columns=RAI_D_COLUMNS,
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)
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train_val_splits = data["train"].train_test_split(test_size=0.2, seed=42)
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data.set_format("torch")
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data = DatasetDict(
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{
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"train": train_val_splits["train"],
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"validation": train_val_splits["test"],
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"test": data["test"],
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}
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)
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data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
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callbacks = init_callbacks(args.patience, args.nosave)
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f1_metric = evaluate.load("f1")
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accuracy_metric = evaluate.load("accuracy")
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precision_metric = evaluate.load("precision")
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recall_metric = evaluate.load("recall")
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training_args = TrainingArguments(
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output_dir=f"{args.model}-rai-final",
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do_train=True,
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evaluation_strategy="steps",
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per_device_train_batch_size=args.batch,
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per_device_eval_batch_size=args.batch,
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gradient_accumulation_steps=args.gradacc,
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eval_accumulation_steps=10,
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learning_rate=args.lr,
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weight_decay=0.1,
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max_grad_norm=5.0,
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num_train_epochs=args.epochs,
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lr_scheduler_type=args.scheduler,
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# warmup_ratio=0.1,
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warmup_ratio=1500,
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logging_strategy="steps",
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logging_first_step=True,
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logging_steps=args.steplog,
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seed=42,
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fp16=args.fp16,
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load_best_model_at_end=False if args.nosave else True,
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save_strategy="no" if args.nosave else "steps",
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save_total_limit=3,
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eval_steps=args.stepeval,
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run_name=f"{args.model}-rai-stratified",
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disable_tqdm=False,
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log_level="warning",
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report_to=["wandb"] if args.wandb else "none",
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optim="adamw_torch",
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)
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def compute_metrics(eval_preds):
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preds = eval_preds.predictions.argmax(-1)
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# targets = eval_preds.label_ids.argmax(-1)
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targets = eval_preds.label_ids
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setting = "macro"
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f1_score_macro = f1_metric.compute(
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predictions=preds, references=targets, average="macro"
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)
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f1_score_micro = f1_metric.compute(
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predictions=preds, references=targets, average="micro"
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)
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accuracy_score = accuracy_metric.compute(predictions=preds, references=targets)
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precision_score = precision_metric.compute(
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predictions=preds, references=targets, average=setting, zero_division=1
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)
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recall_score = recall_metric.compute(
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predictions=preds, references=targets, average=setting, zero_division=1
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)
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results = {
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"macro_f1score": f1_score_macro["f1"],
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"micro_f1score": f1_score_micro["f1"],
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"accuracy": accuracy_score["accuracy"],
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"precision": precision_score["precision"],
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"recall": recall_score["recall"],
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}
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results = {k: round(v, 4) for k, v in results.items()}
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return results
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if args.wandb:
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import wandb
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wandb.init(entity="andreapdr", project=f"gfun-rai-hf", name="mbert-sent", config=vars(args))
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=data["train"],
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eval_dataset=data["validation"],
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compute_metrics=compute_metrics,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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)
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print("- Training:")
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trainer.train()
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print("- Testing:")
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test_results = trainer.evaluate(eval_dataset=data["test"])
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print(test_results)
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exit()
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if __name__ == "__main__":
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from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
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parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
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parser.add_argument("--model", type=str, metavar="", default="mbert")
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parser.add_argument("--nlabels", type=int, metavar="", default=3)
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parser.add_argument("--lr", type=float, metavar="", default=1e-5, help="Set learning rate",)
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parser.add_argument("--scheduler", type=str, metavar="", default="linear", help="Accepted: [\"cosine\", \"cosine-reset\", \"cosine-warmup\", \"cosine-warmup-reset\", \"constant\"]")
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parser.add_argument("--batch", type=int, metavar="", default=16, help="Set batch size")
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parser.add_argument("--gradacc", type=int, metavar="", default=1, help="Gradient accumulation steps")
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parser.add_argument("--epochs", type=int, metavar="", default=100, help="Set epochs")
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parser.add_argument("--stepeval", type=int, metavar="", default=50, help="Run evaluation every n steps")
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parser.add_argument("--steplog", type=int, metavar="", default=50, help="Log training every n steps")
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parser.add_argument("--patience", type=int, metavar="", default=10, help="EarlyStopper patience")
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parser.add_argument("--fp16", action="store_true", help="Use fp16 precision")
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parser.add_argument("--wandb", action="store_true", help="Log to wandb")
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parser.add_argument("--nosave", action="store_true", help="Avoid saving model")
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# parser.add_argument("--onlytest", action="store_true", help="Simply test model on test set")
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# parser.add_argument("--sanity", action="store_true", help="Train and evaluate on the same reduced (1000) data")
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args = parser.parse_args()
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main(args)
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