| | --- |
| | language: en |
| | license: mit |
| | library_name: transformers |
| | tags: |
| | - summarization |
| | - bart |
| | datasets: ccdv/arxiv-summarization |
| | model-index: |
| | - name: BARTxiv |
| | results: |
| | - task: |
| | type: summarization |
| | dataset: |
| | name: arxiv-summarization |
| | type: ccdv/arxiv-summarization |
| | split: validation |
| | metrics: |
| | - type: rouge1 |
| | value: 41.70204016592095 |
| | - type: rouge2 |
| | value: 15.134827404979639 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # BARTxiv |
| |
|
| | This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the [arxiv-summarization](https://huggingface.co/datasets/ccdv/arxiv-summarization) dataset. |
| | It achieves the following results on the validation set: |
| | - Loss: 0.86 |
| | - Rouge1: 41.70 |
| | - Rouge2: 15.13 |
| | - Rougel: 22.85 |
| | - Rougelsum: 37.77 |
| |
|
| | ## 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: 1e-6 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - optimizer: Adafactor |
| | - num_epochs: 9 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
| | | 1.24 | 1.0 | 1073 | 1.24 | 38.32 | 12.80 | 20.55 | 34.50 | |
| | | 1.04 | 2.0 | 2146 | 1.04 | 39.65 | 13.74 | 21.28 | 35.83 | |
| | | 0.979 | 3.0 | 3219 | 0.98 | 40.19 | 14.30 | 21.87 | 36.38 | |
| | | 0.970 | 4.0 | 4292 | 0.97 | 40.87 | 14.44 | 22.14 | 36.89 | |
| | | 0.918 | 5.0 | 5365 | 0.92 | 41.17 | 14.94 | 22.54 | 37.40 | |
| | | 0.901 | 6.0 | 6438 | 0.90 | 41.02 | 14.65 | 22.46 | 37.05 | |
| | | 0.889 | 7.0 | 7511 | 0.89 | 41.32 | 15.09 | 22.64 | 37.42 | |
| | | 0.900 | 8.0 | 8584 | 0 .90 | 41.23 | 15.02 | 22.67 | 37.28 | |
| | | 0.869 | 9.0 | 9657 | 0.87 | 41.70 | 15.13 | 22.85 | 37.77 | |
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.25.1 |
| | - Pytorch 1.13.0+cu117 |
| | - Datasets 2.6.1 |
| | - Tokenizers 0.13.1 |
