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# fr-summarization

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the GEM/wiki_lingua fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 100.0
- Rouge2: 100.0
- Rougel: 100.0
- Rougelsum: 100.0
- Gen Len: 13.9390

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["GEM/wiki_lingua"], "metrics": ["rouge"], "model-index": [{"name": "fr-summarization", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "GEM/wiki_lingua fr", "type": "GEM/wiki_lingua", "config": "fr", "split": "validation", "args": "fr"}, "metrics": [{"type": "rouge", "value": 100.0, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:GEM/wiki_lingua license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-GEM/wiki_lingua #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# fr-summarization

This model is a fine-tuned version of t5-small on the GEM/wiki_lingua fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 100.0
- Rouge2: 100.0
- Rougel: 100.0
- Rougelsum: 100.0
- Gen Len: 13.9390

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# fr-summarization

This model is a fine-tuned version of t5-small on the GEM/wiki_lingua fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 100.0
- Rouge2: 100.0
- Rougel: 100.0
- Rougelsum: 100.0
- Gen Len: 13.9390 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-GEM/wiki_lingua #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # fr-summarization

This model is a fine-tuned version of t5-small on the GEM/wiki_lingua fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 100.0
- Rouge2: 100.0
- Rougel: 100.0
- Rougelsum: 100.0
- Gen Len: 13.9390 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3