Update README.md

README.md

@@ -15,4 +15,86 @@ tags:
 - tourism
 - sentiment
 - multilingual
----
+---
+
+
+Below is the revised README in Markdown format that incorporates the thesis details:
+
+---
+
+# distilbertTourism-multilingual-sentiment
+
+A fine-tuned DistilBERT model for performing sentiment analysis on tourism-related texts in multiple languages. This model is a key component of the thesis project **"Enhancing Tourist Destination Management through a Multilingual Web-Based Tourist Survey System with Machine Learning."** It is designed to analyze reviews, feedback, and other textual data to improve tourist feedback collection in Panglao.
+
+## Overview
+
+This model builds on the [distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) architecture and has been fine-tuned on tourism-specific sentiment data. With support for eight languages, it provides a practical solution for multilingual sentiment classification in the tourism sector.
+
+> **Thesis Context:**  
+> As part of the thesis project, this model integrates with a comprehensive system that leverages advanced natural language processing techniques. In addition to this DistilBERT-based sentiment analyzer, the system utilizes BERTopic for topic modeling. The project aims to surpass the 70% accuracy benchmark set by the IPCR while addressing language barriers and inefficiencies inherent in traditional survey methods.
+
+## Model Details
+
+- **Task:** Text Classification (Sentiment Analysis)
+- **Base Model:** [distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased)
+- **Architecture:** DistilBERT
+- **Parameters:** 135M
+- **Tensor Format:** F32 (Safetensors)
+- **Supported Languages:** 8 (Multilingual)
+- **Training Data:** 160k synthetic tourism reviews
+- **Performance:** Achieves over 95% confidence in sentiment classification for tourism-related texts.
+- **Fine-tuning:** Adapted to the tourism domain (242 fine-tuning iterations/steps indicated)
+
+## Usage
+
+To integrate this model into your application, you can use the Hugging Face Transformers library. Below is an example in Python:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Define the model repository
+model_name = "SCANSKY/distilbertTourism-multilingual-sentiment"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example input text (replace with your own tourism-related text)
+text = "I had an amazing experience during my trip!"
+inputs = tokenizer(text, return_tensors="pt")
+
+# Perform inference
+outputs = model(**inputs)
+logits = outputs.logits
+
+# You can further process the logits to get predicted sentiment labels.
+```
+
+### Installation
+
+Ensure you have the required packages installed:
+
+```bash
+pip install transformers safetensors
+```
+
+## Limitations
+
+- **Domain Specific:** This model is fine-tuned specifically for tourism sentiment analysis and may not perform optimally on texts from other domains.
+- **Inference API:** Currently, the model does not support direct deployment to the Hugging Face Inference API since it lacks a library tag.
+
+## Future Work
+
+- **Dataset Expansion:** Incorporating additional data from more tourism sources could further improve performance.
+- **Model Optimization:** Experimentation with different fine-tuning strategies or hyperparameters might yield even better sentiment classification accuracy.
+- **API Integration:** Future updates may include support for direct inference API deployment.
+
+## Acknowledgements
+
+- This model is based on the robust [DistilBERT](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) architecture.
+- Special thanks to the Hugging Face community for providing the infrastructure that makes deploying and sharing models seamless.
+- This work is part of the thesis project **"Enhancing Tourist Destination Management through a Multilingual Web-Based Tourist Survey System with Machine Learning."** The project also utilizes BERTopic for topic modeling, aiming to revolutionize the collection and analysis of tourist feedback by overcoming language barriers and improving upon traditional survey methods.
+
+---
+
+Feel free to adjust or expand upon this README as your thesis project evolves!