Downloads · 30 days
7
1% of all-time downloads
bert2gpt2_med_v4
Chemsseddine/bert2gpt2_med_v4
bert2gpt2_med_v4 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/bert2gpt2medmlorangesumm-finetunedmedsumnew-finetunedmedsumnew/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/
All-time downloads
484
Public
Repo size
4.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.1 GB · 100%
At a glance
- Library
- transformers
- Model type
- encoder-decoder
- Access
- Public
- Created
- Jun 26, 2022
- Updated
- Jun 30, 2022
- SHA
- 767e8ea8
Model card
From the Hugging Face model README
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
bert2gpt2_med_v4
This model is a fine-tuned version of Chemsseddine/bert2gpt2_med_v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4780
- Rouge1: 36.7502
- Rouge2: 18.5992
- Rougel: 36.2566
- Rougelsum: 36.161
- Gen Len: 22.96
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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 169 | 1.4796 | 33.9893 | 16.2462 | 33.5685 | 33.4738 | 22.42 |
| No log | 2.0 | 338 | 1.4404 | 34.0811 | 16.219 | 34.0206 | 33.9139 | 22.76 |
| 1.0815 | 3.0 | 507 | 1.4078 | 35.2755 | 18.2266 | 34.9186 | 34.9052 | 22.63 |
| 1.0815 | 4.0 | 676 | 1.4207 | 34.0146 | 17.4167 | 33.9904 | 33.9735 | 22.92 |
| 1.0815 | 5.0 | 845 | 1.4285 | 35.2093 | 17.3269 | 35.1023 | 35.222 | 22.75 |
| 0.4699 | 6.0 | 1014 | 1.4607 | 34.5503 | 16.9067 | 34.6404 | 34.5957 | 22.8 |
| 0.4699 | 7.0 | 1183 | 1.4469 | 35.0539 | 17.0677 | 34.7607 | 34.8734 | 22.73 |
| 0.4699 | 8.0 | 1352 | 1.4632 | 35.2308 | 17.9663 | 35.1657 | 35.1012 | 22.9 |
| 0.2522 | 9.0 | 1521 | 1.4734 | 35.5699 | 18.53 | 35.4927 | 35.3747 | 22.84 |
| 0.2522 | 10.0 | 1690 | 1.4780 | 36.7502 | 18.5992 | 36.2566 | 36.161 | 22.96 |
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