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update model card README.md
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README.md
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---
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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: prophetnet_summarization_pretrained
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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: 0.4982
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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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# prophetnet_summarization_pretrained
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This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) on the billsum dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3683
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- Rouge1: 0.4982
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- Rouge2: 0.2267
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- Rougel: 0.2983
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- Rougelsum: 0.2985
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- Gen Len: 139.3831
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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: 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: 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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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
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| No log | 1.0 | 124 | 2.5178 | 0.4894 | 0.2223 | 0.2903 | 0.2903 | 139.8105 |
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| No log | 2.0 | 248 | 2.4170 | 0.4973 | 0.2279 | 0.2975 | 0.297 | 140.6492 |
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| No log | 3.0 | 372 | 2.3895 | 0.4964 | 0.2282 | 0.2984 | 0.2981 | 138.5323 |
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| No log | 4.0 | 496 | 2.3683 | 0.4982 | 0.2267 | 0.2983 | 0.2985 | 139.3831 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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