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app.py

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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))

requirements.txt

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+
+transformers
+datasets
+scikit-learn
+torch