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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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base_model: t5-small
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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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model-index:
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- name: my_awesome_billsum_model
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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: 0.1435
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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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# my_awesome_billsum_model
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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: 2.5437
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- Rouge1: 0.1435
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- Rouge2: 0.0517
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- Rougel: 0.1186
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- Rougelsum: 0.1186
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- Gen Len: 19.0
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## Model description
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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| No log | 1.0 | 62 | 2.8391 | 0.1268 | 0.0345 | 0.1057 | 0.1057 | 19.0 |
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| No log | 2.0 | 124 | 2.6240 | 0.1377 | 0.0446 | 0.1132 | 0.1132 | 19.0 |
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| No log | 3.0 | 186 | 2.5605 | 0.1429 | 0.0509 | 0.1181 | 0.1179 | 19.0 |
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| No log | 4.0 | 248 | 2.5437 | 0.1435 | 0.0517 | 0.1186 | 0.1186 | 19.0 |
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### Framework versions
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- Transformers 4.
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- Pytorch 2.0.1+cu118
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- Datasets 2.
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- Tokenizers 0.13.3
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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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model-index:
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- name: my_awesome_billsum_model
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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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# my_awesome_billsum_model
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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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## Model description
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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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### Framework versions
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- Transformers 4.26.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.0
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- Tokenizers 0.13.3
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