Create README.md

README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+# Sentiment Analysis Models
+
+This repository contains multiple trained sentiment analysis models for binary sentiment classification (Positive / Negative).
+
+## Repository Structure
+
+```
+Sentiment/
+│
+├── BERT/
+├── Bi_LSTM/
+├── Linear_Svm/
+├── Logistic_Regression/
+├── Lstm/
+├── Naive Bayes/
+├── XGBoost/
+└── Notebooks/
+```
+
+---
+
+# Available Models
+
+- BERT
+- LSTM
+- Bi-LSTM
+- Logistic Regression
+- Linear SVM
+- Naive Bayes
+- XGBoost
+
+---
+
+# Using the BERT Model
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="Sudheer17/Sentiment/BERT"
+)
+
+result = classifier("I love this movie!")
+
+print(result)
+```
+
+Example Output
+
+```python
+[{'label': 'Positive', 'score': 0.9848}]
+```
+
+---
+
+# Example Inputs
+
+```text
+I love this movie!
+```
+
+```text
+This is the worst experience ever.
+```
+
+```text
+The service was average.
+```
+
+---
+
+# Notes
+
+- The BERT model can be loaded directly using the Hugging Face Transformers library.
+- Classical Machine Learning and Deep Learning models are stored in their respective folders.
+- Use the appropriate tokenizer and preprocessing pipeline when working with non-BERT models.
+
+---
+
+# Requirements
+
+```
+transformers
+torch
+tensorflow
+scikit-learn
+joblib
+numpy
+pandas
+```
+
+Install them using:
+
+```bash
+pip install transformers torch tensorflow scikit-learn joblib numpy pandas
+```
+
+---
+
+# License
+
+This repository is released under the MIT License.