File size: 1,953 Bytes
65dc3d3 613174b 65dc3d3 613174b 65dc3d3 d72bdca 65dc3d3 613174b d72bdca 65dc3d3 c91ccb9 65dc3d3 |
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 |
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cantonese-chinese-translation
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. -->
# cantonese-chinese-translation
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2373
- Bleu: 58.9213
- Chrf: 57.6665
- Gen Len: 12.8396
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|
| 0.3565 | 0.48 | 1000 | 0.2624 | 58.3152 | 56.9278 | 12.8539 |
| 0.3077 | 0.96 | 2000 | 0.2403 | 58.4429 | 57.226 | 12.8036 |
| 0.2297 | 1.44 | 3000 | 0.2469 | 58.6654 | 57.3437 | 12.8374 |
| 0.2256 | 1.92 | 4000 | 0.2373 | 58.9213 | 57.6665 | 12.8396 |
| 0.1711 | 2.39 | 5000 | 0.2427 | 58.8291 | 57.4604 | 12.8506 |
| 0.1694 | 2.87 | 6000 | 0.2500 | 58.4154 | 57.0752 | 12.813 |
| 0.1336 | 3.35 | 7000 | 0.2575 | 58.4311 | 57.0237 | 12.8415 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.13.3
|