Update README.md

README.md

@@ -6,4 +6,91 @@ 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)