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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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- reddit |
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metrics: |
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- rouge |
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model-index: |
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- name: distilbart-cnn-6-6-reddit |
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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: reddit |
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type: reddit |
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config: default |
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split: train |
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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: 0.1849 |
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--- |
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# distilbart-cnn-6-6-reddit |
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the reddit dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9883 |
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- Rouge1: 0.1849 |
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- Rouge2: 0.0437 |
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- Rougel: 0.1273 |
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- Rougelsum: 0.1601 |
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## More information and training script |
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You can find more information about how this model was trained, including the actual training script in [this github repository](https://github.com/VerleysenNiels/arxiv-summarizer). |
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## Training and evaluation data |
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I made a split in a train and test set. The test size is 1% of the total dataset, which comes down to about 38k samples. |
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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: 0.0001 |
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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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| 3.13 | 1.0 | 238116 | 3.2736 | 0.1773 | 0.0392 | 0.1223 | 0.1539 | |
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| 2.8586 | 2.0 | 476232 | 3.0449 | 0.1846 | 0.0431 | 0.127 | 0.1601 | |
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| 2.7844 | 3.0 | 714348 | 2.9883 | 0.1849 | 0.0437 | 0.1273 | 0.1601 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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