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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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- pub_med_summarization_dataset |
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metrics: |
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- rouge |
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model-index: |
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- name: distilbart-xsum-12-1-finetuned-pubmed |
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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: pub_med_summarization_dataset |
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type: pub_med_summarization_dataset |
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args: document |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 27.0012 |
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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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# distilbart-xsum-12-1-finetuned-pubmed |
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This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-1](https://huggingface.co/sshleifer/distilbart-xsum-12-1) on the pub_med_summarization_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8236 |
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- Rouge1: 27.0012 |
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- Rouge2: 12.728 |
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- Rougel: 19.8685 |
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- Rougelsum: 25.0485 |
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- Gen Len: 59.969 |
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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: 5 |
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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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| 3.3604 | 1.0 | 4000 | 3.1575 | 25.0078 | 11.5381 | 18.4246 | 23.1605 | 54.8935 | |
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| 3.0697 | 2.0 | 8000 | 2.9478 | 26.4947 | 12.5411 | 19.4328 | 24.6123 | 57.948 | |
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| 2.8638 | 3.0 | 12000 | 2.8672 | 26.8856 | 12.7568 | 19.8949 | 24.8745 | 59.6245 | |
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| 2.7243 | 4.0 | 16000 | 2.8347 | 26.7347 | 12.5152 | 19.6516 | 24.7756 | 60.439 | |
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| 2.6072 | 5.0 | 20000 | 2.8236 | 27.0012 | 12.728 | 19.8685 | 25.0485 | 59.969 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.6 |
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