
# 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.


{"language": "fr", "tags": ["pytorch", "t5", "seq2seq", "summarization"], "datasets": "cnn_dailymail", "widget": [{"text": "Apollo 11 est une mission du programme spatial am\u00e9ricain Apollo au cours de laquelle, pour la premi\u00e8re fois, des hommes se sont pos\u00e9s sur la Lune, le lundi 21 juillet 1969. L'agence spatiale am\u00e9ricaine, la NASA, remplit ainsi l'objectif fix\u00e9 par le pr\u00e9sident John F. Kennedy en 1961 de poser un \u00e9quipage sur la Lune avant la fin de la d\u00e9cennie 1960. Il s'agissait de d\u00e9montrer la sup\u00e9riorit\u00e9 des \u00c9tats-Unis sur l'Union sovi\u00e9tique qui avait \u00e9t\u00e9 mise \u00e0 mal par les succ\u00e8s sovi\u00e9tiques au d\u00e9but de l'\u00e8re spatiale dans le contexte de la guerre froide qui oppose alors ces deux pays. Ce d\u00e9fi est lanc\u00e9 alors que la NASA n'a pas encore plac\u00e9 en orbite un seul astronaute. Gr\u00e2ce \u00e0 une mobilisation de moyens humains et financiers consid\u00e9rables, l'agence spatiale rattrape puis d\u00e9passe le programme spatial sovi\u00e9tique.", "example_title": "Apollo 11"}]}
transformers pytorch t5 text2text-generation seq2seq summarization fr dataset:cnn_dailymail autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #summarization #fr #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# 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



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.


# 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



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.
TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #summarization #fr #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # 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



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.
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #summarization #fr #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# 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



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.