| --- |
| license: mit |
| base_model: facebook/bart-large-cnn |
| tags: |
| - summarization |
| - generated_from_trainer |
| datasets: |
| - tldr_news |
| metrics: |
| - rouge |
| model-index: |
| - name: summary_model |
| results: |
| - task: |
| name: Sequence-to-sequence Language Modeling |
| type: text2text-generation |
| dataset: |
| name: tldr_news |
| type: tldr_news |
| config: all |
| split: test |
| args: all |
| metrics: |
| - name: Rouge1 |
| type: rouge |
| value: 0.21590240799799404 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # summary_model |
| |
| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the tldr_news dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.9573 |
| - Rouge1: 0.2159 |
| - Rouge2: 0.0831 |
| - Rougel: 0.1829 |
| - Rougelsum: 0.1869 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5.6e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 2 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | 0.5871 | 1.0 | 63 | 2.7134 | 0.2176 | 0.0872 | 0.1881 | 0.1951 | |
| | 0.4422 | 2.0 | 126 | 2.9573 | 0.2159 | 0.0831 | 0.1829 | 0.1869 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
|
|
