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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- it5/datasets |
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metrics: |
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- rouge |
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model-index: |
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- name: it5-efficient-small-el32-st_r2g-0.0003 |
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results: |
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- task: |
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name: Summarization |
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type: summarization |
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dataset: |
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name: it5/datasets st_r2g |
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type: it5/datasets |
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args: st_r2g |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 30.0502 |
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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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# it5-efficient-small-el32-st_r2g-0.0003 |
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This model is a fine-tuned version of [stefan-it/it5-efficient-small-el32](https://huggingface.co/stefan-it/it5-efficient-small-el32) on the it5/datasets st_r2g dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6135 |
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- Rouge1: 30.0502 |
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- Rouge2: 11.5687 |
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- Rougel: 26.5953 |
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- Rougelsum: 27.0402 |
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- Gen Len: 16.9578 |
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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: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 10.0 |
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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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| 3.1265 | 0.74 | 5000 | 2.7247 | 26.8378 | 9.3464 | 23.9521 | 24.2837 | 15.5914 | |
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| 2.8786 | 1.49 | 10000 | 2.6532 | 27.5869 | 10.0861 | 24.7406 | 25.0245 | 15.3272 | |
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| 2.6587 | 2.23 | 15000 | 2.6080 | 28.2336 | 10.5229 | 25.3053 | 25.6716 | 15.4338 | |
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| 2.664 | 2.98 | 20000 | 2.5630 | 28.6673 | 10.8421 | 25.7032 | 26.0245 | 15.6255 | |
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| 2.4896 | 3.72 | 25000 | 2.5679 | 28.842 | 10.885 | 25.6757 | 26.0633 | 16.1841 | |
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| 2.34 | 4.47 | 30000 | 2.5564 | 29.3246 | 11.1981 | 26.1637 | 26.5392 | 15.7826 | |
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| 2.2204 | 5.21 | 35000 | 2.5744 | 29.5545 | 11.3806 | 26.3237 | 26.6993 | 15.8374 | |
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| 2.2301 | 5.96 | 40000 | 2.5614 | 29.5872 | 11.4227 | 26.3139 | 26.7196 | 15.7213 | |
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| 2.1219 | 6.7 | 45000 | 2.5617 | 29.8256 | 11.3702 | 26.4156 | 26.8465 | 15.936 | |
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| 2.007 | 7.45 | 50000 | 2.6014 | 29.743 | 11.4336 | 26.38 | 26.772 | 15.7144 | |
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| 1.9398 | 8.19 | 55000 | 2.6080 | 29.9478 | 11.4801 | 26.5352 | 26.9746 | 15.9308 | |
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| 1.9426 | 8.94 | 60000 | 2.6022 | 30.097 | 11.5602 | 26.705 | 27.1092 | 15.8598 | |
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| 1.8853 | 9.68 | 65000 | 2.6138 | 30.1588 | 11.5823 | 26.6984 | 27.1371 | 15.803 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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