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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-cnn-12-6 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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model-index: |
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- name: distilbart-cnn-12-6-finetuned-samsum |
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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: samsum |
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type: samsum |
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config: samsum |
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split: validation |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 41.0557 |
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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-12-6-finetuned-samsum |
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5040 |
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- Rouge1: 41.0557 |
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- Rouge2: 20.8627 |
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- Rougel: 31.6375 |
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- Rougelsum: 38.3023 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 0.5843 | 1.0 | 921 | 0.5095 | 40.4545 | 21.2232 | 31.2992 | 37.9698 | |
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| 0.4562 | 2.0 | 1842 | 0.5010 | 40.9057 | 21.0576 | 31.4701 | 38.2105 | |
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| 0.3938 | 3.0 | 2763 | 0.5040 | 41.0557 | 20.8627 | 31.6375 | 38.3023 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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