Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Details
+This project demonstrates the fine-tuning of the DistilBERT model on the IMDB dataset for text classification, using the Hugging Face Transformers library.
+
+## Model Architecture
+
+- **Model**: `DistilBERT-base-uncased`
+- **Optimizer**: AdamW
+- **Loss Function**: Cross-entropy loss
+- **Epochs**: 4
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16
+
+## Dataset
+
+The imdb data is the collection of reviews of movies categorized into TWO classes:
+
+- **POSITIVE**
+- **NEGATIVE**
+
+You can access the dataset via the Hugging Face `datasets` library.
+
+## Training Configuration
+
+The training arguments are set as follows:
+
+```python
+training_args = TrainingArguments(
+    output_dir="distilbert-base-uncased-finetuned-sentiment-analysis",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=4,
+    weight_decay=0.01,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    push_to_hub=True,
+)
+```
+You can change the parameters according to your requirements!!
+
+## Model Evaluation Results
+
+| Epoch | Eval Loss   | Eval Accuracy |
+|-------|-------------|---------------|
+| 1     | 0.1881      | 92.90%        |
+| 2     | 0.2331      | 93.39%        |
+| 3     | 0.2919      | 93.39%        |
+| 4     | 0.3253      | 93.67%        |
+
+## Dependencies
+The required dependencies for this project are:
+
+* transformers
+* datasets
+* torch
+* sklearn
+* numpy
+
+## How to Use the Model
+You can use the fine-tuned model for sentiment analysis using the Hugging Face `pipeline` as follows:
+
+```python
+from transformers import pipeline
+
+# Load the model from Hugging Face Hub
+sentiment_analysis = pipeline("sentiment-analysis", model="Sathyam03/distilbert-base-uncased-finetuned-sentiment-analysis")
+
+# Example usage
+reviews = [
+    "I absolutely loved this movie! It was fantastic.",
+    "The film was okay, but it dragged on in some parts.",
+    "I didn't like this movie at all. It was boring."
+]
+
+results = sentiment_analysis(reviews)
+
+# Print the results
+for review, result in zip(reviews, results):
+    print(f"Review: {review}")
+    print(f"Sentiment: {result['label']}, Confidence: {result['score']:.4f}\n")
+)
+```