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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-paraphrase-pubmed
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. -->
# bart-paraphrase-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6340
- Rouge2 Precision: 0.83
- Rouge2 Recall: 0.6526
- Rouge2 Fmeasure: 0.7144
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6613 | 1.0 | 663 | 0.4750 | 0.8321 | 0.6552 | 0.7167 |
| 0.4993 | 2.0 | 1326 | 0.4404 | 0.8366 | 0.6583 | 0.7203 |
| 0.443 | 3.0 | 1989 | 0.4261 | 0.8319 | 0.6562 | 0.7176 |
| 0.3482 | 4.0 | 2652 | 0.4198 | 0.8348 | 0.6571 | 0.7191 |
| 0.3206 | 5.0 | 3315 | 0.4233 | 0.8344 | 0.656 | 0.7183 |
| 0.294 | 6.0 | 3978 | 0.4334 | 0.835 | 0.657 | 0.719 |
| 0.2404 | 7.0 | 4641 | 0.4437 | 0.8334 | 0.6559 | 0.7178 |
| 0.2228 | 8.0 | 5304 | 0.4438 | 0.8348 | 0.6565 | 0.7187 |
| 0.211 | 9.0 | 5967 | 0.4516 | 0.8329 | 0.6549 | 0.717 |
| 0.1713 | 10.0 | 6630 | 0.4535 | 0.8332 | 0.6547 | 0.7169 |
| 0.1591 | 11.0 | 7293 | 0.4763 | 0.8349 | 0.6561 | 0.7184 |
| 0.1555 | 12.0 | 7956 | 0.4824 | 0.8311 | 0.6534 | 0.7153 |
| 0.1262 | 13.0 | 8619 | 0.4883 | 0.8322 | 0.655 | 0.7167 |
| 0.1164 | 14.0 | 9282 | 0.5025 | 0.8312 | 0.6539 | 0.7158 |
| 0.1108 | 15.0 | 9945 | 0.5149 | 0.8321 | 0.6535 | 0.7157 |
| 0.0926 | 16.0 | 10608 | 0.5340 | 0.8315 | 0.6544 | 0.7159 |
| 0.0856 | 17.0 | 11271 | 0.5322 | 0.8306 | 0.6518 | 0.7142 |
| 0.0785 | 18.0 | 11934 | 0.5346 | 0.8324 | 0.6549 | 0.7167 |
| 0.071 | 19.0 | 12597 | 0.5488 | 0.8311 | 0.652 | 0.714 |
| 0.0635 | 20.0 | 13260 | 0.5624 | 0.8287 | 0.6517 | 0.7132 |
| 0.0608 | 21.0 | 13923 | 0.5612 | 0.8299 | 0.6527 | 0.7141 |
| 0.0531 | 22.0 | 14586 | 0.5764 | 0.8283 | 0.6498 | 0.7119 |
| 0.0486 | 23.0 | 15249 | 0.5832 | 0.8298 | 0.6532 | 0.7148 |
| 0.0465 | 24.0 | 15912 | 0.5866 | 0.83 | 0.6522 | 0.7142 |
| 0.0418 | 25.0 | 16575 | 0.5825 | 0.83 | 0.6523 | 0.7141 |
| 0.0391 | 26.0 | 17238 | 0.5997 | 0.8306 | 0.6545 | 0.716 |
| 0.0376 | 27.0 | 17901 | 0.5894 | 0.8315 | 0.6546 | 0.7164 |
| 0.035 | 28.0 | 18564 | 0.6045 | 0.8306 | 0.6529 | 0.7149 |
| 0.0316 | 29.0 | 19227 | 0.6168 | 0.8311 | 0.6546 | 0.7162 |
| 0.0314 | 30.0 | 19890 | 0.6203 | 0.8311 | 0.6552 | 0.7164 |
| 0.0292 | 31.0 | 20553 | 0.6173 | 0.8315 | 0.6548 | 0.7163 |
| 0.0265 | 32.0 | 21216 | 0.6226 | 0.832 | 0.6548 | 0.7166 |
| 0.0274 | 33.0 | 21879 | 0.6264 | 0.8314 | 0.6538 | 0.7155 |
| 0.0247 | 34.0 | 22542 | 0.6254 | 0.8289 | 0.6515 | 0.7132 |
| 0.0238 | 35.0 | 23205 | 0.6254 | 0.8307 | 0.6519 | 0.7142 |
| 0.0232 | 36.0 | 23868 | 0.6295 | 0.8287 | 0.6515 | 0.7133 |
| 0.0215 | 37.0 | 24531 | 0.6326 | 0.8293 | 0.6523 | 0.7138 |
| 0.0212 | 38.0 | 25194 | 0.6332 | 0.8295 | 0.6522 | 0.714 |
| 0.0221 | 39.0 | 25857 | 0.6335 | 0.8305 | 0.6528 | 0.7147 |
| 0.0202 | 40.0 | 26520 | 0.6340 | 0.83 | 0.6526 | 0.7144 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
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