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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-cnn-12-3-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: 40.5642 |
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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-cnn-12-3-finetuned-pubmed |
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-3](https://huggingface.co/sshleifer/distilbart-cnn-12-3) 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: 2.1743 |
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- Rouge1: 40.5642 |
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- Rouge2: 16.9812 |
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- Rougel: 25.3449 |
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- Rougelsum: 36.46 |
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- Gen Len: 141.95 |
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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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| 2.469 | 1.0 | 4000 | 2.2956 | 38.3713 | 15.2594 | 23.6734 | 34.1634 | 141.707 | |
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| 2.2527 | 2.0 | 8000 | 2.1994 | 39.5939 | 16.2376 | 24.6363 | 35.5106 | 141.831 | |
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| 2.0669 | 3.0 | 12000 | 2.1780 | 40.078 | 16.6705 | 25.1119 | 35.9605 | 141.8475 | |
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| 1.9275 | 4.0 | 16000 | 2.1669 | 40.0825 | 16.6169 | 24.9702 | 36.0191 | 141.928 | |
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| 1.8102 | 5.0 | 20000 | 2.1743 | 40.5642 | 16.9812 | 25.3449 | 36.46 | 141.95 | |
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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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