---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: bart-base-chinese-6615-chinese
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: xlsum
      type: xlsum
      config: chinese_traditional
      split: validation
      args: chinese_traditional
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.0774
---

<!-- 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. -->

# bart-base-chinese-6615-chinese

This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8576
- Rouge1: 0.0774
- Rouge2: 0.0179
- Rougel: 0.0772
- Rougelsum: 0.077
- Gen Len: 19.9552

## 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: 5e-05
- 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: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.1216        | 0.86  | 500  | 0.9150          | 0.0523 | 0.0113 | 0.0521 | 0.052     | 19.9345 |
| 1.0346        | 1.71  | 1000 | 0.8817          | 0.0585 | 0.0119 | 0.0583 | 0.0582    | 19.9535 |
| 1.0063        | 2.57  | 1500 | 0.8624          | 0.0603 | 0.0112 | 0.0598 | 0.0599    | 19.9512 |
| 0.9219        | 3.42  | 2000 | 0.8592          | 0.0715 | 0.0145 | 0.071  | 0.0712    | 19.9535 |
| 0.8757        | 4.28  | 2500 | 0.8577          | 0.072  | 0.0153 | 0.0717 | 0.0717    | 19.9636 |
| 0.8832        | 5.14  | 3000 | 0.8567          | 0.0721 | 0.0157 | 0.0717 | 0.0718    | 19.9493 |
| 0.8788        | 5.99  | 3500 | 0.8498          | 0.0763 | 0.0173 | 0.0759 | 0.0759    | 19.9565 |
| 0.8659        | 6.85  | 4000 | 0.8513          | 0.076  | 0.017  | 0.0756 | 0.0754    | 19.9546 |
| 0.7802        | 7.71  | 4500 | 0.8563          | 0.0772 | 0.0185 | 0.077  | 0.0768    | 19.9525 |
| 0.8114        | 8.56  | 5000 | 0.8562          | 0.0769 | 0.0169 | 0.0766 | 0.0764    | 19.954  |
| 0.7715        | 9.42  | 5500 | 0.8576          | 0.0774 | 0.0179 | 0.0772 | 0.077     | 19.9552 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0