metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: barthez-deft-archeologie
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 37.1845
barthez-deft-archeologie
This model is a fine-tuned version of moussaKam/barthez on an unknown dataset.
Note: this model is one of the preliminary experiments and it underperforms the models published in the paper (using MBartHez and HAL/Wiki pre-training + copy mechanisms)
It achieves the following results on the evaluation set:
- Loss: 2.0733
- Rouge1: 37.1845
- Rouge2: 16.9534
- Rougel: 28.8416
- Rougelsum: 29.077
- Gen Len: 34.4028
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: 3e-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: 20.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 3.4832 | 1.0 | 108 | 2.4237 | 22.6662 | 10.009 | 19.8729 | 19.8814 | 15.8333 |
| 2.557 | 2.0 | 216 | 2.2328 | 24.8102 | 11.9911 | 20.4773 | 20.696 | 19.0139 |
| 2.2702 | 3.0 | 324 | 2.2002 | 25.6482 | 11.6191 | 21.8383 | 21.9341 | 18.1944 |
| 2.1119 | 4.0 | 432 | 2.1266 | 25.5806 | 11.9765 | 21.3973 | 21.3503 | 19.4306 |
| 1.9582 | 5.0 | 540 | 2.1072 | 25.6578 | 12.2709 | 22.182 | 22.0548 | 19.1528 |
| 1.8137 | 6.0 | 648 | 2.1008 | 26.5272 | 11.4033 | 22.359 | 22.3259 | 19.4722 |
| 1.7725 | 7.0 | 756 | 2.1074 | 25.0405 | 11.1773 | 21.1369 | 21.1847 | 19.1806 |
| 1.6772 | 8.0 | 864 | 2.0959 | 26.5237 | 11.6028 | 22.5018 | 22.3931 | 19.3333 |
| 1.5798 | 9.0 | 972 | 2.0976 | 27.7443 | 11.9898 | 22.4052 | 22.2954 | 19.7222 |
| 1.4753 | 10.0 | 1080 | 2.0733 | 28.3502 | 12.9162 | 22.6352 | 22.6015 | 19.8194 |
| 1.4646 | 11.0 | 1188 | 2.1091 | 27.9198 | 12.8591 | 23.0718 | 23.0779 | 19.6111 |
| 1.4082 | 12.0 | 1296 | 2.1036 | 28.8509 | 13.0987 | 23.4189 | 23.5044 | 19.4861 |
| 1.2862 | 13.0 | 1404 | 2.1222 | 28.6641 | 12.8157 | 22.6799 | 22.7051 | 19.8611 |
| 1.2612 | 14.0 | 1512 | 2.1487 | 26.9709 | 11.6084 | 22.0312 | 22.0543 | 19.875 |
| 1.2327 | 15.0 | 1620 | 2.1808 | 28.218 | 12.6239 | 22.7372 | 22.7881 | 19.7361 |
| 1.2264 | 16.0 | 1728 | 2.1778 | 26.7393 | 11.4474 | 21.6057 | 21.555 | 19.7639 |
| 1.1848 | 17.0 | 1836 | 2.1995 | 27.6902 | 12.1082 | 22.0406 | 22.0101 | 19.6806 |
| 1.133 | 18.0 | 1944 | 2.2038 | 27.0402 | 12.1846 | 21.7793 | 21.7513 | 19.8056 |
| 1.168 | 19.0 | 2052 | 2.2116 | 27.5149 | 11.9876 | 22.1113 | 22.1527 | 19.7222 |
| 1.1206 | 20.0 | 2160 | 2.2133 | 28.2321 | 12.677 | 22.749 | 22.8485 | 19.5972 |
Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1+cu110
- Datasets 1.11.0
- Tokenizers 0.10.3