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bert2gpt2_med_v3
Chemsseddine/bert2gpt2_med_v3
bert2gpt2_med_v3 is a machine learning model from Chemsseddine. 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.
<img src="https://huggingface.co/Chemsseddine/bert2gpt2medfr/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/
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At a glance
- Library
- transformers
- Model type
- encoder-decoder
- Access
- Public
- Created
- Jun 26, 2022
- Updated
- Jun 30, 2022
- SHA
- 22c8a6ba
Model card
From the Hugging Face model README
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_fr/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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bert2gpt2_med_v3
This model is a fine-tuned version of Chemsseddine/bert2gpt2_med_v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.5474
- Rouge1: 31.8871
- Rouge2: 14.4411
- Rougel: 31.6716
- Rougelsum: 31.579
- Gen Len: 22.8412
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.5621 | 1.0 | 900 | 1.9724 | 30.3731 | 13.8412 | 29.9606 | 29.9716 | 22.6353 |
| 1.3692 | 2.0 | 1800 | 1.9634 | 29.6409 | 13.7674 | 29.5202 | 29.5207 | 22.5059 |
| 0.8308 | 3.0 | 2700 | 2.1431 | 30.9317 | 14.5594 | 30.8021 | 30.7287 | 22.6118 |
| 0.4689 | 4.0 | 3600 | 2.2970 | 30.1132 | 14.6407 | 29.9657 | 30.0182 | 23.3235 |
| 0.2875 | 5.0 | 4500 | 2.3787 | 30.9378 | 14.7108 | 30.861 | 30.9097 | 22.7529 |
| 0.1564 | 6.0 | 5400 | 2.4137 | 30.5338 | 13.9702 | 30.1252 | 30.1975 | 23.1588 |
| 0.1007 | 7.0 | 6300 | 2.4822 | 30.872 | 14.9353 | 30.835 | 30.7694 | 23.0529 |
| 0.0783 | 8.0 | 7200 | 2.4974 | 29.9825 | 14.1702 | 29.7507 | 29.7271 | 23.1882 |
| 0.0504 | 9.0 | 8100 | 2.5175 | 31.96 | 15.0705 | 31.9669 | 31.9839 | 23.0588 |
| 0.0339 | 10.0 | 9000 | 2.5474 | 31.8871 | 14.4411 | 31.6716 | 31.579 | 22.8412 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Library
- transformers
- Type
- encoder-decoder
- Created
- Jun 26, 2022
- Updated
- Jun 30, 2022