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---
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
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