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from datasets import load_dataset
from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
from sklearn.metrics import classification_report
import torch
# Load few-shot dataset
dataset = load_dataset("ai4bharat/sangraha")
train_data = dataset["train"].select(range(30))
test_data = dataset["validation"].select(range(10))
# Tokenizer and preprocessing
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def tokenize(example):
return tokenizer(example["context"], padding="max_length", truncation=True)
def encode_label(example):
example["label"] = 1 if "bank" in example["context"].lower() else 0
return example
train_data = train_data.map(tokenize).map(encode_label)
test_data = test_data.map(tokenize).map(encode_label)
train_data.set_format("torch", columns=["input_ids", "attention_mask", "label"])
test_data.set_format("torch", columns=["input_ids", "attention_mask", "label"])
# Model and trainer
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
evaluation_strategy="epoch",
logging_dir="./logs"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_data,
eval_dataset=test_data,
)
trainer.train()
metrics = trainer.evaluate()
predictions = trainer.predict(test_data).predictions.argmax(-1)
print(classification_report(test_data["label"], predictions))
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