Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: text2text-generation
+---
+# Model Card for Model ID
+
+## Model Details
+
+### Model Description
+
+- **Model type:** Text-to-Text Generation
+- **Language(s) (NLP):**  English
+- **License:** MIT License
+- **Finetuned from model:** T5 Base Model (Google AI)
+
+## Uses
+
+The News2Topic T5-base model is designed for automatic generation of topic names from news articles or news-like text. It can be integrated into news aggregation platforms, content management systems, or used for enhancing news browsing and searching experiences by providing concise topics.
+
+## How to Get Started with the Model
+
+```
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", model="textgain/News2Topic-T5-base")
+
+```
+# Example usage
+
+```
+news_text = "Your news text here."
+print(pipe(news_text))
+```
+
+## Training Details
+
+### Training Data
+
+The News2Topic T5-base model was trained on a 21K sample of the "newsroom" dataset annotated with synthetic data generated by GPT-3.5-turbo
+
+### Training Procedure
+
+The model was trained for 3 epochs, with a learning rate of 0.00001, a maximum sequence length of 512, and a training batch size of 12.
+
+## Citation
+
+**BibTeX:**
+```
+@article{Kosar_De Pauw_Daelemans_2024,
+title={Comparative Evaluation of Topic Detection: Humans vs. LLMs}, volume={13},
+url={https://www.clinjournal.org/clinj/article/view/173}, journal={Computational Linguistics in the Netherlands Journal},
+author={Kosar, Andriy and De Pauw, Guy and Daelemans, Walter},
+year={2024},
+month={Mar.},
+pages={91–120} }
+```