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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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- generated_from_trainer |
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datasets: |
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- billsum |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-billsum-ca_test |
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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: billsum |
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type: billsum |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 52.2582 |
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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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# t5-small-finetuned-billsum-ca_test |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5234 |
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- Rouge1: 52.2582 |
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- Rouge2: 34.8162 |
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- Rougel: 50.5491 |
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- Rougelsum: 50.6121 |
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- Gen Len: 18.996 |
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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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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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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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| No log | 1.0 | 495 | 1.8113 | 58.4024 | 41.7432 | 56.9521 | 57.0516 | 18.9597 | |
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| 2.709 | 2.0 | 990 | 1.6230 | 47.7769 | 32.1777 | 46.0344 | 46.046 | 18.996 | |
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| 1.9323 | 3.0 | 1485 | 1.5459 | 51.2371 | 33.8242 | 49.4532 | 49.5038 | 18.996 | |
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| 1.7842 | 4.0 | 1980 | 1.5234 | 52.2582 | 34.8162 | 50.5491 | 50.6121 | 18.996 | |
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### Framework versions |
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- Transformers 4.12.2 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.14.0 |
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- Tokenizers 0.10.3 |
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