Create README.md

README.md

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+# DeBERTa-v3-base Fine-Tuned for Hallucination Detection
+
+Model Details
+Model Name: DeBERTa-v3-base
+Architecture: DeBERTa (Decoding-enhanced BERT with disentangled attention)
+Base Model: DeBERTa-v3-base
+Fine-tuned Dataset: PAWS (Paraphrase Adversaries from Word Scrambling)
+Task: Sentence Pair Classification (Hallucination Detection)
+Model Description
+This model is a fine-tuned version of the DeBERTa-v3-base model specifically for the task of detecting hallucinations between pairs of sentences. Hallucinations in this context refer to statements or information present in one sentence but not supported or contradicted by the other.
+
+Fine-Tuning Dataset
+Dataset Name: PAWS (Paraphrase Adversaries from Word Scrambling)
+Dataset Description: The PAWS dataset contains pairs of sentences with high lexical overlap but different meanings, designed to challenge models' understanding of semantic content.
+
+Dataset: https://huggingface.co/datasets/paws
+Training Procedure
+Number of Epochs: 10
+Hardware Used: NVIDIA -A 100
+
+Performance:
+Accuracy: 94.88%
+F1 Score: 92.3%
+Precision: 92.82%
+Recall: 95.81%
+
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Varun-Chowdary/hallucination_detect")
+model = AutoModelForSequenceClassification.from_pretrained("Varun-Chowdary/hallucination_detect")
+
+# Define the sentences
+sentence1 = "Maradona was born in Argentina, South America."
+sentence2 = "Maradona was born in Brazil, South America. "
+
+# Tokenize and prepare input
+inputs = tokenizer(sentence1, sentence2, return_tensors='pt', truncation=True, padding=True)
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1)
+
+# Get the predicted label
+predicted_label = torch.argmax(probabilities, dim=1).item()
+labels = ["No Hallucination", "Hallucination"]
+print(f"Predicted label: {labels[predicted_label]}")