| | --- |
| | library_name: transformers |
| | base_model: ccdv/lsg-bart-base-16384 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: pubmed-sum |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # pubmed-sum |
| |
|
| | This model is a fine-tuned version of [ccdv/lsg-bart-base-16384](https://huggingface.co/ccdv/lsg-bart-base-16384) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.1303 |
| | - Rouge1: 0.1396 |
| | - Rouge2: 0.0651 |
| | - Rougel: 0.1198 |
| | - Rougelsum: 0.1306 |
| |
|
| | ## 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: 5e-05 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 16 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 2 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | | 3.6342 | 0.32 | 50 | 2.7400 | 0.1174 | 0.0443 | 0.0971 | 0.1071 | |
| | | 2.9319 | 0.64 | 100 | 2.3386 | 0.1276 | 0.0531 | 0.1071 | 0.1176 | |
| | | 2.5759 | 0.96 | 150 | 2.2462 | 0.1313 | 0.057 | 0.111 | 0.1219 | |
| | | 2.4678 | 1.28 | 200 | 2.1933 | 0.1308 | 0.0581 | 0.1116 | 0.1221 | |
| | | 2.4149 | 1.6 | 250 | 2.1717 | 0.1353 | 0.0608 | 0.115 | 0.1257 | |
| | | 2.3553 | 1.92 | 300 | 2.1303 | 0.1396 | 0.0651 | 0.1198 | 0.1306 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.44.2 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.19.1 |
| |
|
