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--- |
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base_model: fnlp/bart-base-chinese |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-base-chinese-6615-chinese |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: xlsum |
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type: xlsum |
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config: chinese_traditional |
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split: validation |
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args: chinese_traditional |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.0774 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-base-chinese-6615-chinese |
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This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co./fnlp/bart-base-chinese) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8576 |
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- Rouge1: 0.0774 |
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- Rouge2: 0.0179 |
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- Rougel: 0.0772 |
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- Rougelsum: 0.077 |
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- Gen Len: 19.9552 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 1.1216 | 0.86 | 500 | 0.9150 | 0.0523 | 0.0113 | 0.0521 | 0.052 | 19.9345 | |
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| 1.0346 | 1.71 | 1000 | 0.8817 | 0.0585 | 0.0119 | 0.0583 | 0.0582 | 19.9535 | |
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| 1.0063 | 2.57 | 1500 | 0.8624 | 0.0603 | 0.0112 | 0.0598 | 0.0599 | 19.9512 | |
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| 0.9219 | 3.42 | 2000 | 0.8592 | 0.0715 | 0.0145 | 0.071 | 0.0712 | 19.9535 | |
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| 0.8757 | 4.28 | 2500 | 0.8577 | 0.072 | 0.0153 | 0.0717 | 0.0717 | 19.9636 | |
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| 0.8832 | 5.14 | 3000 | 0.8567 | 0.0721 | 0.0157 | 0.0717 | 0.0718 | 19.9493 | |
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| 0.8788 | 5.99 | 3500 | 0.8498 | 0.0763 | 0.0173 | 0.0759 | 0.0759 | 19.9565 | |
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| 0.8659 | 6.85 | 4000 | 0.8513 | 0.076 | 0.017 | 0.0756 | 0.0754 | 19.9546 | |
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| 0.7802 | 7.71 | 4500 | 0.8563 | 0.0772 | 0.0185 | 0.077 | 0.0768 | 19.9525 | |
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| 0.8114 | 8.56 | 5000 | 0.8562 | 0.0769 | 0.0169 | 0.0766 | 0.0764 | 19.954 | |
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| 0.7715 | 9.42 | 5500 | 0.8576 | 0.0774 | 0.0179 | 0.0772 | 0.077 | 19.9552 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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