|
--- |
|
license: apache-2.0 |
|
base_model: moussaKam/AraBART |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: AraBART-finetuned-xlsum-ar |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# AraBART-finetuned-xlsum-ar |
|
|
|
This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.4655 |
|
- Rouge1: 24.4029 |
|
- Rouge2: 10.6961 |
|
- Rougel: 21.8597 |
|
- Rougelsum: 21.9193 |
|
- Gen Len: 19.6173 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 2.8881 | 1.0 | 2111 | 2.5078 | 23.0537 | 9.805 | 20.6712 | 20.7358 | 19.4371 | |
|
| 2.7229 | 2.0 | 4222 | 2.4712 | 23.4792 | 10.0638 | 21.0179 | 21.0808 | 19.5933 | |
|
| 2.6235 | 3.0 | 6333 | 2.4606 | 23.793 | 10.2551 | 21.2806 | 21.3525 | 19.5784 | |
|
| 2.5475 | 4.0 | 8444 | 2.4557 | 23.8559 | 10.2547 | 21.3093 | 21.383 | 19.6013 | |
|
| 2.4579 | 5.0 | 10555 | 2.4567 | 24.3906 | 10.6549 | 21.8215 | 21.8672 | 19.6471 | |
|
| 2.4124 | 6.0 | 12666 | 2.4578 | 24.3648 | 10.6614 | 21.8584 | 21.9202 | 19.6018 | |
|
| 2.38 | 7.0 | 14777 | 2.4606 | 24.3488 | 10.722 | 21.8546 | 21.9218 | 19.5938 | |
|
| 2.3422 | 8.0 | 16888 | 2.4605 | 24.4836 | 10.7873 | 21.9424 | 21.9996 | 19.6215 | |
|
| 2.3185 | 9.0 | 18999 | 2.4630 | 24.2878 | 10.6124 | 21.8332 | 21.8687 | 19.5949 | |
|
| 2.2988 | 10.0 | 21110 | 2.4655 | 24.4029 | 10.6961 | 21.8597 | 21.9193 | 19.6173 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
