| language: fr | |
| tags: | |
| - pytorch | |
| - t5 | |
| - seq2seq | |
| - summarization | |
| datasets: cnn_dailymail | |
| # French T5 Abstractive Text Summarization | |
| ~~Version 1.0 (I will keep improving the model's performances.)~~ | |
| Version 2.0 is here! (with improved performances of course) | |
| I trained the model on 13x more data than v1. | |
| ROUGE-1: 44.5252 | |
| ROUGE-2: 22.652 | |
| ROUGE-L: 29.8866 | |
| ## Model description | |
| This model is a T5 Transformers model (JDBN/t5-base-fr-qg-fquad) that was fine-tuned in french for abstractive text summarization. | |
| ## How to use | |
| ```python | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| tokenizer = T5Tokenizer.from_pretrained("plguillou/t5-base-fr-sum-cnndm") | |
| model = T5ForConditionalGeneration.from_pretrained("plguillou/t5-base-fr-sum-cnndm") | |
| ``` | |
| To summarize an ARTICLE, just modify the string like this : "summarize: ARTICLE". | |
| ## Training data | |
| The base model I used is JDBN/t5-base-fr-qg-fquad (it can perform question generation, question answering and answer extraction). | |
| I used the "t5-base" model from the transformers library to translate in french the CNN / Daily Mail summarization dataset. | |