| | --- |
| | license: mit |
| | base_model: facebook/bart-large-cnn |
| | tags: |
| | - summarization |
| | - generated_from_trainer |
| | datasets: |
| | - tldr_news |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: my_summ |
| | 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: 0.21647643221587914 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # my_summ |
| | |
| | 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: 4.1133 |
| | - Rouge1: 0.2165 |
| | - Rouge2: 0.0872 |
| | - Rougel: 0.1846 |
| | - Rougelsum: 0.1881 |
| |
|
| | ## 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: 8 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | | 2.2607 | 1.0 | 125 | 2.2706 | 0.2318 | 0.0950 | 0.1983 | 0.2024 | |
| | | 1.1698 | 2.0 | 250 | 2.3624 | 0.2150 | 0.0848 | 0.1828 | 0.1856 | |
| | | 0.5798 | 3.0 | 375 | 2.8369 | 0.2144 | 0.0838 | 0.1802 | 0.1848 | |
| | | 0.2813 | 4.0 | 500 | 3.3045 | 0.2112 | 0.0803 | 0.1788 | 0.1821 | |
| | | 0.1544 | 5.0 | 625 | 3.6092 | 0.2096 | 0.0793 | 0.1780 | 0.1838 | |
| | | 0.0862 | 6.0 | 750 | 3.7615 | 0.2168 | 0.0848 | 0.1851 | 0.1881 | |
| | | 0.0518 | 7.0 | 875 | 3.9039 | 0.2180 | 0.0861 | 0.1842 | 0.1873 | |
| | | 0.0253 | 8.0 | 1000 | 4.1133 | 0.2165 | 0.0872 | 0.1846 | 0.1881 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.0 |
| |
|
