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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- scientific_papers |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3 |
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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: scientific_papers |
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type: scientific_papers |
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args: arxiv |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 42.5835 |
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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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# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3 |
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This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3) on the scientific_papers dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0646 |
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- Rouge1: 42.5835 |
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- Rouge2: 16.1887 |
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- Rougel: 24.7972 |
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- Rougelsum: 38.1846 |
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- Gen Len: 129.9291 |
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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: 1 |
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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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| 2.0865 | 1.0 | 33840 | 2.0646 | 42.5835 | 16.1887 | 24.7972 | 38.1846 | 129.9291 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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