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t5-small-finetuned-NL2ModelioMQ-FR
JuanCadavid/t5-small-finetuned-NL2ModelioMQ-FR
t5-small-finetuned-NL2ModelioMQ-FR is a machine learning model from JuanCadavid. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Dec 12, 2022
- Updated
- Dec 13, 2022
- SHA
- f42f1727
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
t5-small-finetuned-NL2ModelioMQ-FR
This model is a fine-tuned version of t5-small on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge2 Precision: 0.9788
- Rouge2 Recall: 0.6055
- Rouge2 Fmeasure: 0.7295
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| 0.0087 | 1.0 | 4449 | 0.0002 | 0.9787 | 0.6054 | 0.7294 |
| 0.0017 | 2.0 | 8898 | 0.0000 | 0.9788 | 0.6055 | 0.7295 |
| 0.0008 | 3.0 | 13347 | 0.0000 | 0.9788 | 0.6055 | 0.7295 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Dec 12, 2022
- Updated
- Dec 13, 2022