---
license: apache-2.0
datasets:
- ajaykarthick/imdb-movie-reviews
base_model:
- google-bert/bert-base-uncased
tags:
- sentiment_analysis
---

# BERT Sentiment Analysis 🚀

This is a fine-tuned BERT model (`bert-base-uncased`) for **sentiment analysis** on the IMDb movie review dataset. The model classifies text into:

- ✅ Positive
- ❌ Negative

---

## 🧠 Model Details

- **Base Model:** `bert-base-uncased`
- **Dataset:** IMDb (via Hugging Face Datasets)
- **Classes:** Binary classification (0: Positive, 1: Negative)
- **Framework:** PyTorch
- **Training:** Fine-tuned using Hugging Face Transformers and Datasets on GPU

---

## 📥 How to Use

You can use this model directly with 🤗 `transformers`:

```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "saubhagya122k4/bert-sentiment-analysis"

tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()

text = "This movie was fantastic! I really enjoyed it."

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=70)

with torch.no_grad():
    outputs = model(**inputs)
    probs = F.softmax(outputs.logits, dim=1)
    predicted = torch.argmax(probs, dim=1).item()

labels = {0: "Positive", 1: "Negative"}
print(f"Sentiment: {labels[predicted]}")
````

---

## 📊 Performance

* **Accuracy:** \~93% on test set
* **Tokenizer:** `bert-base-uncased`
* **Sequence Length:** 70 tokens

---

## 🛠 Training & Fine-tuning

* Notebook: [View on Kaggle](https://www.kaggle.com/code/saubhagyavishwakarma/sentiment-bert-model?scriptVersionId=247854860)
* Framework: PyTorch + Hugging Face Transformers
* Batch Size: 16
* Epochs: 3
* Optimizer: AdamW

---

## 📦 Use Cases

* Movie review classification
* Customer feedback analysis
* Product sentiment detection

---

## 🧾 License

This model is available for public use under the **Apache 2.0**.

---

## 🙋‍♂️ Author

**Saubhagya Vishwakarma**
📧 [saubhagya.v@simformsolutions.com](mailto:saubhagyah5331@gmail.com)
🔗 [GitHub](https://github.com/Saubhagyah5331)