Create README.md

README.md

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+---
+language:
+- en
+- ko
+- zh
+- ja
+- es
+- fr
+- ru
+- hi
+metrics:
+- accuracy
+base_model:
+- distilbert/distilbert-base-multilingual-cased
+pipeline_tag: text-classification
+---
+# Model Card: BERTopic Model for Serverless Inference
+
+## Model Description
+This is a BERTopic model trained for **topic modeling** on a multilingual dataset. The model is serialized in **safetensors** format for optimized loading and is designed for **serverless inference** in cloud environments.
+
+### Features
+- **Multilingual support** (Supports 8 languages)
+- **Pre-trained and fine-tuned on synthetic and real tourist reviews**
+- **Safetensors format for faster and safer model loading**
+- **Optimized for serverless architectures (FastAPI, AWS Lambda, Cloud Functions, etc.)**
+
+## Intended Use
+- **Tourism feedback analysis**
+- **Customer review topic modeling**
+- **Data-driven decision-making for tourism offices**
+- **Research on multilingual topic modeling**
+
+## Model Details
+- **Architecture**: BERTopic
+- **Embedding Model**: `paraphrase-multilingual-MiniLM-L12-v2`
+- **Dimensionality Reduction**: UMAP
+- **Clustering Algorithm**: HDBSCAN
+- **Vectorizer**: CountVectorizer (TF-IDF preprocessing)
+- **Languages**: English, Spanish, French, Chinese, Japanese, German, Korean, Tagalog
+- **Dataset**: 160k synthetic and real tourist reviews categorized by emotional tone and topics
+
+## Model Performance
+- **Topic Coherence Score**: *XX.XX*
+- **Diversity Score**: *XX.XX*
+- **Sentiment Analysis Accuracy**: *≥ 70%*
+
+## How to Use
+### Load the Model:
+```python
+from bertopic import BERTopic
+from safetensors.torch import load_file
+
+# Load model
+model = BERTopic.load("path/to/model.safetensors")
+```
+### Perform Topic Modeling:
+```python
+docs = ["The hotel had a great view of the beach and excellent service.",
+        "Transportation was a bit difficult to find late at night."]
+
+topics, probs = model.transform(docs)
+print(topics)
+```
+
+## Deployment Guide
+- **AWS Lambda / FastAPI**: Ensure `safetensors`, `bertopic`, and `sentence-transformers` are included in the dependencies.
+- **Memory Optimization**: Use `safetensors` for faster inference and reduced memory footprint.
+- **Serverless Scaling**: Load the model in memory at cold start and reuse for subsequent requests.
+
+## Limitations
+- **Topic coherence may vary by language**
+- **Sensitive to dataset biases**
+- **Not suitable for real-time low-latency applications (<50ms response time)**
+
+## License
+[Insert License Here]
+
+## Citation
+```
+@inproceedings{your_citation,
+  title={BERTopic Model for Multilingual Tourism Feedback},
+  author={Your Name},
+  year={2025}
+}
+```
+
+---
+*For inquiries or contributions, please open an issue on the Hugging Face repository.*