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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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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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- bleu
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model-index:
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- name: T5-small_finetuned_billsum_subset_model_bs16_lr5e-05
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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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config: default
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split: ca_test
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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: 0.1918
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- name: Bleu
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type: bleu
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value: 0.0008
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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_subset_model_bs16_lr5e-05
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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: 2.0113
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- Rouge1: 0.1918
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- Rouge2: 0.0975
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- Rougel: 0.1668
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- Rougelsum: 0.1665
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- Gen Len: 19.0
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- Bleu: 0.0008
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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: 16
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- eval_batch_size: 16
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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 | Bleu |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
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| No log | 1.0 | 62 | 2.0260 | 0.1936 | 0.0982 | 0.1681 | 0.1678 | 19.0 | 0.0008 |
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| No log | 2.0 | 124 | 2.0155 | 0.1908 | 0.0949 | 0.1659 | 0.1656 | 19.0 | 0.0007 |
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| No log | 3.0 | 186 | 2.0131 | 0.1902 | 0.0948 | 0.1651 | 0.1646 | 19.0 | 0.0008 |
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| No log | 4.0 | 248 | 2.0113 | 0.1918 | 0.0975 | 0.1668 | 0.1665 | 19.0 | 0.0008 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 2.0.0+cu118
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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