train.py

import csv
from typing import TypedDict
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_dataset, Dataset
import pandas as pd
import evaluate
import os
import torch

data_files = {
	"train": os.path.realpath(os.path.join(__file__, "..", "./datasets/train.csv")),
	"test": os.path.realpath(os.path.join(__file__, "..", "./datasets/test.csv"))
}
output_dir = os.path.realpath(os.path.join(__file__, "..", "./outputs/v2-deberta-100-max"))

tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-base")

label_map = {
	"FAVOR": 0,
	"NONE": 1,
	"AGAINST": 2
}

torch.cuda.empty_cache()

def tokenize(examples):
	examples["label"] = [label_map[label] for label in examples["label"]]
	examples["text"] = [examples["Target"][i] + " [SEP] " + text for i , text in enumerate(examples["text"])]
	return tokenizer(examples["text"], padding="max_length", return_tensors='pt', truncation=True, max_length=100)


def load_dataset(path: str) -> Dataset:
	dataframe = pd.read_csv(path)
	dataframe = dataframe.drop("Opinion Towards", axis=1)
	dataframe = dataframe.drop("Sentiment", axis=1)
	dataset = Dataset.from_pandas(dataframe)
	dataset = dataset.rename_column('Tweet', 'text')
	dataset = dataset.rename_column("Stance", "label")

	return dataset.map(tokenize, batched=True)

train_ds = load_dataset(data_files["train"])
test_ds = load_dataset(data_files["test"])

model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-base", num_labels=3)

metric = evaluate.load("accuracy")

def compute_metrics(eval_pred):
	logits, labels = eval_pred
	predictions = np.argmax(logits, axis=-1)
	return metric.compute(predictions=predictions, references=labels)

training_args = TrainingArguments(output_dir=output_dir, evaluation_strategy="epoch")

trainer = Trainer(
	model=model,
	args=training_args,
	train_dataset=train_ds,
	eval_dataset=test_ds,
	compute_metrics=compute_metrics
)
print("TRAINING")

trainer.train()