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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: LifeScienceBART
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. -->
# LifeScienceBART
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4310
- Rouge1: 52.3694
- Rouge2: 17.5874
- Rougel: 36.4217
- Rougelsum: 48.765
- Bertscore Precision: 82.295
- Bertscore Recall: 83.951
- Bertscore F1: 83.1121
- Bleu: 0.1308
- Gen Len: 227.8869
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.1988 | 0.0881 | 100 | 6.0815 | 43.5317 | 12.8172 | 29.5886 | 40.6668 | 78.4798 | 81.4664 | 79.9395 | 0.0939 | 227.8869 |
| 5.7388 | 0.1762 | 200 | 5.6510 | 41.3899 | 12.8237 | 29.1108 | 38.0304 | 77.5037 | 81.7443 | 79.5601 | 0.0978 | 227.8869 |
| 5.3718 | 0.2643 | 300 | 5.2822 | 46.279 | 14.1045 | 31.7158 | 43.1347 | 79.8268 | 82.2875 | 81.0344 | 0.1041 | 227.8869 |
| 5.1682 | 0.3524 | 400 | 5.1072 | 48.1957 | 15.1732 | 32.7384 | 44.0672 | 80.3745 | 82.94 | 81.6328 | 0.1137 | 227.8869 |
| 5.1315 | 0.4405 | 500 | 4.9408 | 48.9502 | 15.6058 | 33.6297 | 45.5085 | 81.0706 | 83.1289 | 82.0835 | 0.1158 | 227.8869 |
| 4.9456 | 0.5286 | 600 | 4.7786 | 48.4843 | 15.8565 | 34.014 | 45.2987 | 80.9541 | 83.0806 | 81.9998 | 0.1151 | 227.8869 |
| 4.8396 | 0.6167 | 700 | 4.6607 | 51.3313 | 16.5503 | 35.0136 | 47.9755 | 82.0251 | 83.4743 | 82.7408 | 0.1210 | 227.8869 |
| 4.7481 | 0.7048 | 800 | 4.5922 | 51.9257 | 16.9939 | 35.583 | 48.1998 | 82.2219 | 83.8107 | 83.0061 | 0.1262 | 227.8869 |
| 4.6688 | 0.7929 | 900 | 4.5112 | 51.3896 | 17.1313 | 35.8696 | 47.7303 | 81.926 | 83.7943 | 82.8465 | 0.1277 | 227.8869 |
| 4.4321 | 0.8810 | 1000 | 4.4624 | 52.6168 | 17.6855 | 36.2987 | 49.0759 | 82.3644 | 83.8994 | 83.1222 | 0.1305 | 227.8869 |
| 4.5732 | 0.9691 | 1100 | 4.4310 | 52.3694 | 17.5874 | 36.4217 | 48.765 | 82.295 | 83.951 | 83.1121 | 0.1308 | 227.8869 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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