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--- |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-cnn-6-6 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wcep-10 |
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metrics: |
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- rouge |
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model-index: |
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- name: thesis-bart-finetuned-on-original-wcep |
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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: wcep-10 |
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type: wcep-10 |
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config: roberta |
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split: validation |
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args: roberta |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 37.1938 |
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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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# thesis-bart-finetuned-on-original-wcep |
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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 wcep-10 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9981 |
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- Rouge1: 37.1938 |
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- Rouge2: 16.5385 |
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- Rougel: 26.7997 |
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- Rougelsum: 30.3278 |
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- Gen Len: 67.5627 |
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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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- 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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| 2.0801 | 1.0 | 510 | 2.0119 | 36.476 | 16.0059 | 26.3489 | 29.7099 | 67.9882 | |
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| 1.7597 | 2.0 | 1020 | 1.9868 | 36.9333 | 16.3738 | 26.5067 | 30.1156 | 68.3961 | |
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| 1.5997 | 3.0 | 1530 | 1.9981 | 37.1938 | 16.5385 | 26.7997 | 30.3278 | 67.5627 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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