glimpse-ui/alternative_polarity/scideberta/scideberta_full_polarity_train.py

import pandas as pd
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import numpy as np
import torch
from torch.nn import functional as F

from transformers import Trainer

class WeightedTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
        labels = inputs.pop("labels")
        outputs = model(**inputs)
        logits = outputs.logits
        weights = class_weights.to(logits.device)
        loss = F.cross_entropy(logits, labels, weight=weights)
        return (loss, outputs) if return_outputs else loss



# Load data
train_df = pd.read_csv("./data/DISAPERE-main/SELFExtractedData/disapere_polarity_train.csv")
dev_df = pd.read_csv("./data/DISAPERE-main/SELFExtractedData/disapere_polarity_dev.csv")
test_df = pd.read_csv("./data/DISAPERE-main/SELFExtractedData/disapere_polarity_test.csv")

# Convert to HuggingFace Datasets
train_ds = Dataset.from_pandas(train_df)
dev_ds = Dataset.from_pandas(dev_df)
test_ds = Dataset.from_pandas(test_df)

model_name = "KISTI-AI/Scideberta-full"
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize(batch):
    return tokenizer(batch["text"], padding="max_length", truncation=True, max_length=512)

train_ds = train_ds.map(tokenize, batched=True)
dev_ds = dev_ds.map(tokenize, batched=True)
test_ds = test_ds.map(tokenize, batched=True)

# Set format for PyTorch
train_ds.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
dev_ds.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
test_ds.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])

# Load model
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)

# Compute class weights
label_counts = train_df['label'].value_counts()
total_samples = len(train_df)
class_weights = torch.tensor([total_samples / (len(label_counts) * count) for count in label_counts.sort_index().values])
class_weights = class_weights.to(dtype=torch.float32)
print("Class weights:", class_weights)

class WeightedTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        labels = inputs.pop("labels")
        outputs = model(**inputs)
        logits = outputs.logits
        weights = class_weights.to(logits.device)
        loss = F.cross_entropy(logits, labels, weight=weights)
        return (loss, outputs) if return_outputs else loss


# Metrics
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=1)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average="macro")
    acc = accuracy_score(labels, preds)
    return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}

# Training arguments
args = TrainingArguments(
    output_dir="./alternative_polarity/scideberta/checkpoints",
    eval_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=8,
    num_train_epochs=4,
    weight_decay=0.01,
    load_best_model_at_end=True,
    metric_for_best_model="f1"
)

# Trainer
trainer = WeightedTrainer(
    model=model,
    args=args,
    train_dataset=train_ds,
    eval_dataset=dev_ds,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)

# Train
trainer.train()

# Evaluate on test
results = trainer.evaluate(test_ds)
print("Test results:", results)

# Save the model and tokenizer
model.save_pretrained("./alternative_polarity/scideberta/scideberta_full_polarity_final_model")
tokenizer.save_pretrained("./alternative_polarity/scideberta/scideberta_full_polarity_final_model")