docnli-bert-finetuned

📘 DocNLI-BERT (Finetuned for Natural Language Inference)

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🧾 Model Overview

This model is a BERT-based sequence classification model finetuned on a Document NLI (Natural Language Inference) dataset.

Task:
Given a premise and a hypothesis, the model predicts whether the hypothesis is entailed by the premise or not.

• Base model: bert-base-uncased
• Fine-tuned head: Sequence classification head with 2 labels (entailment, not_entailment)

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📊 Training Details

• Framework: Hugging Face Transformers + PEFT (LoRA)
• Optimizer: AdamW
• Learning rate: 5e-5
• Batch size: 16
• Epochs: 6
• Loss function: CrossEntropyLoss
• Best checkpoint selection: Based on validation accuracy

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🚀 Usage

Load model & tokenizer

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "ProdD/docnli-bert-finetuned" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name)