File size: 2,668 Bytes
2d1694c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
license: apache-2.0
tags:
- summarization
- mT5_multilingual_XLSum
- mt5
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-finetune-ar-xlsum
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. -->
# mt5-base-finetune-ar-xlsum
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2546
- Rouge-1: 22.2
- Rouge-2: 9.57
- Rouge-l: 20.26
- Gen Len: 19.0
- Bertscore: 71.43
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.9261 | 1.0 | 585 | 3.6314 | 18.19 | 6.49 | 16.37 | 19.0 | 70.17 |
| 3.8429 | 2.0 | 1170 | 3.4253 | 19.45 | 7.58 | 17.73 | 19.0 | 70.35 |
| 3.6311 | 3.0 | 1755 | 3.3569 | 20.83 | 8.54 | 18.9 | 19.0 | 70.89 |
| 3.4917 | 4.0 | 2340 | 3.3101 | 20.77 | 8.53 | 18.89 | 19.0 | 70.98 |
| 3.3873 | 5.0 | 2925 | 3.2867 | 21.47 | 9.0 | 19.54 | 19.0 | 71.23 |
| 3.3037 | 6.0 | 3510 | 3.2693 | 21.41 | 9.0 | 19.5 | 19.0 | 71.21 |
| 3.2357 | 7.0 | 4095 | 3.2581 | 22.05 | 9.36 | 20.04 | 19.0 | 71.43 |
| 3.1798 | 8.0 | 4680 | 3.2522 | 22.21 | 9.56 | 20.23 | 19.0 | 71.41 |
| 3.1359 | 9.0 | 5265 | 3.2546 | 22.27 | 9.58 | 20.23 | 19.0 | 71.46 |
| 3.0997 | 10.0 | 5850 | 3.2546 | 22.2 | 9.57 | 20.26 | 19.0 | 71.43 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|