ullahhafeez321
/

bert-sentiment-classifier

@@ -1,199 +1,214 @@
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 ---
 ---
+language:
+  - en
+license: mit
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - bert
+  - sentiment-analysis
+  - text-classification
+  - imdb
+  - pytorch
+  - transformers
+# BERT Sentiment Classifier
+This repository contains a fine-tuned BERT model for **English movie review sentiment classification**. The model classifies text into two sentiment categories:
+- **POSITIVE** — The review expresses a positive opinion.
+- **NEGATIVE** — The review expresses a negative opinion.
+The model is based on the pretrained `google-bert/bert-base-uncased` architecture and was fine-tuned using the IMDb movie reviews dataset.
 ## Model Details
+| Property | Description |
+|---|---|
+| Model architecture | BERT Base Uncased |
+| Base model | `google-bert/bert-base-uncased` |
+| Task | Sentiment Classification |
+| Number of classes | 2 |
+| Positive label | 1 |
+| Negative label | 0 |
+| Maximum sequence length | 192 tokens |
+| Framework | PyTorch |
+| Library | Hugging Face Transformers |
+## Dataset
+The model was fine-tuned using the [IMDb Movie Reviews Dataset](https://huggingface.co/datasets/stanfordnlp/imdb).
+The notebook uses the following data split:
+| Dataset | Number of samples |
+|---|---:|
+| Training | 8,000 |
+| Validation | 1,000 |
+| Testing | 2,000 |
+| **Total** | **11,000** |
+The IMDb dataset contains movie reviews with binary sentiment labels:
+- `0`: Negative
+- `1`: Positive
+The training data is used to fine-tune the model, validation data is used to evaluate training progress and select the best checkpoint, and the separate test set is used for final evaluation.
+## Training Configuration
+The model was fine-tuned using the following configuration:
+| Parameter | Value |
+|---|---|
+| Base learning model | BERT Base Uncased |
+| Epochs | 3 |
+| Batch size | 16 |
+| Learning rate | 2e-5 |
+| Weight decay | 0.01 |
+| Warmup steps | 100 |
+| Maximum sequence length | 192 |
+| Random seed | 42 |
+| Best checkpoint metric | Validation accuracy |
+The pretrained BERT encoder provides contextual language representations. A two-class classification head is added and fine-tuned for sentiment prediction.
 ## Evaluation
+The notebook evaluates the model using:
+- **Accuracy:** Measures the percentage of correctly classified reviews.
+- **Precision:** Measures how many predicted positive examples are actually positive.
+- **Recall:** Measures how many actual positive examples the model identifies.
+- **F1-score:** Combines precision and recall.
+- **Confusion Matrix:** Shows correct and incorrect predictions for both classes.
+Final test metrics are produced by running the evaluation cells in the notebook. No numerical performance results are stated here because they depend on the actual training run.
+## Usage
+You can load this model directly using Hugging Face Transformers.
+### Install dependencies
+```bash
+pip install transformers torch
+```
+### Load the model
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+MODEL_NAME = "ullahhafeez321/bert-sentiment-classifier"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+```
+### Make a prediction
+```python
+def predict_sentiment(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=192
+    )
+    with torch.inference_mode():
+        outputs = model(**inputs)
+        probabilities = torch.softmax(
+            outputs.logits,
+            dim=-1
+        )[0]
+    predicted_id = int(probabilities.argmax())
+    sentiment = model.config.id2label[predicted_id]
+    confidence = float(probabilities[predicted_id]) * 100
+    return {
+        "text": text,
+        "sentiment": sentiment,
+        "confidence": round(confidence, 2)
+    }
+result = predict_sentiment(
+    "This movie was excellent. I really enjoyed it!"
+)
+print(result)
+```
+### Example predictions
+| Input review | Expected sentiment |
+|---|---|
+| This movie was absolutely wonderful! | POSITIVE |
+| This movie was terrible and boring. | NEGATIVE |
+| I loved every moment of this film. | POSITIVE |
+| I hated this movie. It was a waste of time. | NEGATIVE |
+These are illustrative examples, not guaranteed outputs for every sentence.
+## Applications
+This model can be integrated into:
+- Sentiment analysis websites
+- Movie review classification applications
+- Text classification projects
+- Python applications
+- Streamlit applications
+- Educational NLP projects
+## Limitations
+- The model was fine-tuned on English IMDb movie reviews.
+- It is primarily intended for movie-review sentiment classification.
+- It may not perform equally well on other domains, such as product reviews, news, or social media.
+- Sarcasm, irony, ambiguous language, and mixed sentiment can be difficult to classify.
+- Confidence scores are model probabilities, not guarantees of correctness.
+## Technologies Used
+- Python
+- PyTorch
+- Hugging Face Transformers
+- Hugging Face Datasets
+- Scikit-learn
+- IMDb Dataset
+- Google BERT Base Uncased
+## Training Notebook
+The training notebook includes:
+1. Library installation and environment setup.
+2. IMDb dataset loading and splitting.
+3. Pretrained BERT tokenizer and model initialization.
+4. Dataset tokenization.
+5. Model fine-tuning.
+6. Validation and test evaluation.
+7. Classification report and confusion matrix.
+8. Custom sentiment prediction.
+9. Saving and uploading the model to Hugging Face.
+## Model Repository
+**Hugging Face:** https://huggingface.co/ullahhafeez321/bert-sentiment-classifier
+## License
+MIT License.