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--- |
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license: apache-2.0 |
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base_model: google/mt5-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: deed_summarization_mt5_version_1 |
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results: [] |
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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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# deed_summarization_mt5_version_1 |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5863 |
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- Rouge1: 1.0138 |
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- Rouge2: 0.6875 |
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- Rougel: 1.0233 |
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- Rougelsum: 1.0941 |
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- Gen Len: 288.1509 |
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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: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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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- lr_scheduler_warmup_steps: 5000 |
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- num_epochs: 25 |
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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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| 25.5422 | 1.0 | 375 | 16.6467 | 0.6477 | 0.0 | 0.6383 | 0.6639 | 25.0 | |
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| 11.9626 | 2.0 | 750 | 13.5214 | 0.6633 | 0.0 | 0.6553 | 0.6745 | 38.5912 | |
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| 10.1002 | 3.0 | 1125 | 7.4294 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 386.6164 | |
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| 2.8844 | 4.0 | 1500 | 2.8574 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 499.0 | |
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| 4.1183 | 5.0 | 1875 | 9.6893 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 499.0 | |
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| 1.4443 | 6.0 | 2250 | 2.4224 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 466.7673 | |
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| 3.8512 | 7.0 | 2625 | 1.5813 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 432.4717 | |
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| 8.6527 | 8.0 | 3000 | 1.4532 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 480.6164 | |
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| 0.5302 | 9.0 | 3375 | 1.1597 | 0.7257 | 0.0 | 0.7163 | 0.7264 | 419.239 | |
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| 1.2311 | 10.0 | 3750 | 0.9806 | 0.9895 | 0.1006 | 0.9135 | 0.9189 | 383.6855 | |
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| 0.8903 | 11.0 | 4125 | 0.8961 | 0.9609 | 0.1578 | 0.8871 | 0.8934 | 376.6038 | |
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| 0.8742 | 12.0 | 4500 | 0.8109 | 1.1104 | 0.2243 | 1.0038 | 1.007 | 388.3648 | |
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| 0.5934 | 13.0 | 4875 | 0.7588 | 0.3145 | 0.2287 | 0.3145 | 0.3145 | 341.717 | |
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| 0.1715 | 14.0 | 5250 | 0.7073 | 0.2795 | 0.2013 | 0.2795 | 0.2795 | 333.434 | |
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| 0.4363 | 15.0 | 5625 | 0.6780 | 0.4368 | 0.2287 | 0.4368 | 0.4368 | 326.7044 | |
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| 1.0736 | 16.0 | 6000 | 0.6647 | 0.7163 | 0.5169 | 0.7512 | 0.7512 | 299.4151 | |
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| 0.1069 | 17.0 | 6375 | 0.6294 | 0.856 | 0.6038 | 0.863 | 0.8595 | 312.434 | |
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| 0.1434 | 18.0 | 6750 | 0.6358 | 0.7512 | 0.5222 | 0.7862 | 0.808 | 291.4403 | |
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| 0.4344 | 19.0 | 7125 | 0.6164 | 1.1082 | 0.7576 | 1.1305 | 1.1574 | 304.7484 | |
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| 0.1038 | 20.0 | 7500 | 0.6066 | 0.8572 | 0.6108 | 0.8758 | 0.9085 | 297.3145 | |
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| 0.5519 | 21.0 | 7875 | 0.5972 | 0.4354 | 0.2935 | 0.5382 | 0.5382 | 281.5786 | |
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| 0.0804 | 22.0 | 8250 | 0.5994 | 0.6464 | 0.5583 | 0.7741 | 0.7794 | 305.805 | |
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| 0.3696 | 23.0 | 8625 | 0.5884 | 0.6362 | 0.3246 | 0.6362 | 0.6362 | 291.434 | |
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| 0.3966 | 24.0 | 9000 | 0.5852 | 0.7133 | 0.408 | 0.7311 | 0.8082 | 281.9119 | |
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| 0.3484 | 25.0 | 9375 | 0.5863 | 1.0138 | 0.6875 | 1.0233 | 1.0941 | 288.1509 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0.dev20230811+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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