Upload DistilBERT IMDB sentiment model and results

.gitattributes

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MODEL_CARD.md

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+
+# Sentiment Analysis Model Card
+
+## Model Description
+- **Base Model**: distilbert-base-uncased
+- **Task**: Binary Sentiment Classification (Positive/Negative)
+- **Dataset**: IMDB Movie Reviews
+- **Training Samples**: 16,000
+- **Validation Samples**: 4,000
+- **Test Samples**: 5,000
+
+## Performance
+- **Test Accuracy**: 0.9460
+- **Test F1 Score**: 0.9723
+- **Best Validation Accuracy**: 0.9300
+
+## Training Details
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Learning Rate**: 2e-05
+- **Max Sequence Length**: 512
+- **Optimizer**: AdamW with weight decay
+- **Scheduler**: Linear with warmup
+
+## Model Size
+- **Total Parameters**: 66,955,010
+- **Trainable Parameters**: 66,955,010
+- **Frozen Parameters**: 0
+
+## Explainability Features
+- ✅ Attention weights available
+- ✅ Hidden states available
+- ✅ Compatible with LIME
+- ✅ Compatible with Integrated Gradients

README.md

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+---
+language: en
+tags:
+- sentiment-analysis
+- imdb
+- distilbert
+- transformers
+license: apache-2.0
+datasets:
+- imdb
+---
+
+# DistilBERT Sentiment Analysis Model
+
+This model is a fine-tuned version of `distilbert-base-uncased` for binary sentiment classification on the IMDB movie reviews dataset.
+
+## Model Details
+
+### Model Description
+- **Model type**: DistilBERT (transformer-based)
+- **Task**: Binary sentiment classification (positive/negative)
+- **Base Model**: `distilbert-base-uncased`
+- **Language**: English
+
+### Training Details
+
+#### Training Data
+- **Dataset**: IMDB Movie Reviews
+- **Training Samples**: 16,000
+- **Validation Samples**: 4,000
+- **Test Samples**: 5,000
+- **Class Distribution**: 50% positive, 50% negative
+
+#### Training Procedure
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Learning Rate**: 2e-05
+- **Max Sequence Length**: 512
+- **Optimizer**: AdamW with weight decay (0.01)
+- **Scheduler**: Linear with 10% warmup
+
+#### Evaluation Results
+- **Test Accuracy**: 0.9460
+- **Test F1 Score**: 0.9723
+- **Best Validation Accuracy**: 0.9300
+- **Training Time**: ~6 minutes on Google Colab T4 GPU
+
+## How to Use
+
+### Direct Inference
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "Hums003/distilbert-imdb-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare text
+text = "This movie was absolutely fantastic! I loved every minute of it."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+# Interpret results
+sentiment = "positive" if predictions[0][1] > 0.5 else "negative"
+confidence = predictions[0][1].item() if predictions[0][1] > 0.5 else predictions[0][0].item()
+print(f"Sentiment: {sentiment} (confidence: {confidence:.2%})")
+```

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