---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cantonese-chinese-parallel-corpus-bart-compare-alpha
  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-parallel-corpus-bart-compare-alpha

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: 1.2307
- Bleu: 28.1911
- Chrf: 27.3934
- Gen Len: 13.1593

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|
| 1.8245        | 0.14  | 1000  | 1.5392          | 23.4094 | 22.9586 | 12.9471 |
| 1.6283        | 0.29  | 2000  | 1.4433          | 24.6312 | 24.1038 | 12.9882 |
| 1.5527        | 0.43  | 3000  | 1.4074          | 25.4368 | 24.7944 | 13.0385 |
| 1.5125        | 0.58  | 4000  | 1.3743          | 25.6532 | 25.1073 | 13.0069 |
| 1.4572        | 0.72  | 5000  | 1.3468          | 26.2054 | 25.6527 | 13.0221 |
| 1.451         | 0.87  | 6000  | 1.3249          | 26.3433 | 25.7717 | 13.0345 |
| 1.4087        | 1.01  | 7000  | 1.3162          | 26.7569 | 26.0931 | 13.1037 |
| 1.296         | 1.16  | 8000  | 1.2961          | 26.7816 | 26.1834 | 13.0488 |
| 1.285         | 1.3   | 9000  | 1.2881          | 27.1895 | 26.4474 | 13.1257 |
| 1.281         | 1.45  | 10000 | 1.2778          | 27.248  | 26.5723 | 13.072  |
| 1.2809        | 1.59  | 11000 | 1.2772          | 27.3645 | 26.7016 | 13.0937 |
| 1.2741        | 1.74  | 12000 | 1.2568          | 27.3857 | 26.7455 | 13.0646 |
| 1.2658        | 1.88  | 13000 | 1.2552          | 27.4927 | 26.8279 | 13.0988 |
| 1.2412        | 2.03  | 14000 | 1.2632          | 27.5154 | 26.9238 | 13.0482 |
| 1.1303        | 2.17  | 15000 | 1.2627          | 27.7288 | 27.0753 | 13.0828 |
| 1.1449        | 2.32  | 16000 | 1.2596          | 27.7628 | 27.1038 | 13.0667 |
| 1.1352        | 2.46  | 17000 | 1.2465          | 27.9487 | 27.1672 | 13.1585 |
| 1.151         | 2.61  | 18000 | 1.2426          | 27.9699 | 27.2496 | 13.1294 |
| 1.1361        | 2.75  | 19000 | 1.2348          | 27.9343 | 27.218  | 13.0994 |
| 1.1368        | 2.9   | 20000 | 1.2307          | 28.1911 | 27.3934 | 13.1593 |
| 1.1012        | 3.04  | 21000 | 1.2487          | 28.1384 | 27.4055 | 13.1253 |
| 1.0201        | 3.19  | 22000 | 1.2482          | 28.0577 | 27.3169 | 13.1299 |
| 1.0274        | 3.33  | 23000 | 1.2479          | 28.149  | 27.4087 | 13.1401 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3