update readme

README.md

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+---
+title: "Adaptive T5 Summarization"
+emoji: 📝
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_file: ""
+---
+
+# Adaptive T5 Summarization
+
+This project builds a meta-model on top of T5 to adapt model selection based on text complexity.
+
+## Installation
+
+To use this project, clone the repository and install the required dependencies:
+
+```bash
+git clone https://huggingface.co/spaces/your_space_name
+cd your_space_name
+pip install -r requirements.txt
+python -m spacy download en_core_web_lg
+```
+
+
+## Repository Structure
+```bash
+.
+├── demo.ipynb                   #  Jupyter notebook demonstrating the usage.
+├── model.py                    # Contains the T5 models and the meta-model implementation.
+├── requirements.txt                     # Lists the required Python packages.
+├── LICENSE
+└── README.md
+```
+
+## Usage
+What the meta model does:
+- Extract complexity-based features from input texts.
+- Apply multiple T5-based summarization models.
+- Use BertScore to determine the best-performing model for each text.
+- Train a classifier to predict the best model based on extracted features.
+- At inference, the classifier selects the appropriate model dynamically.
+
+## TODO
+The results are not satisfactory. Improvements should be made:
+
+- Reduce the tolerance (the small model is nearly always used).
+- Include feature computation time in the meta-model cost analysis.
+- Analyze feature relevance and classifier performance more deeply.
+- Modify the MetaModel structure since it is too large to commit (4GB).
+
+This is an ongoing project, and contributions or feedback are welcome!

packages.txt

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-python -m spacy download en_core_web_lg