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fine-tuned-BioBART-12-epochs-1024-input-128-output
tanatapanun/fine-tuned-BioBART-12-epochs-1024-input-128-output
fine-tuned-BioBART-12-epochs-1024-input-128-output is a machine learning model from tanatapanun. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Jan 25, 2024
- Updated
- Jan 25, 2024
- SHA
- a57c66d2
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
fine-tuned-BioBART-12-epochs-1024-input-128-output
This model is a fine-tuned version of GanjinZero/biobart-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5051
- Rouge1: 0.1576
- Rouge2: 0.0366
- Rougel: 0.108
- Rougelsum: 0.1085
- Gen Len: 34.29
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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 151 | 4.4833 | 0.0206 | 0.0051 | 0.0196 | 0.02 | 7.18 |
| No log | 2.0 | 302 | 1.8298 | 0.1027 | 0.0249 | 0.0865 | 0.0866 | 26.29 |
| No log | 3.0 | 453 | 1.6450 | 0.0738 | 0.0169 | 0.0564 | 0.0568 | 20.31 |
| 3.8819 | 4.0 | 604 | 1.5676 | 0.1469 | 0.0336 | 0.1162 | 0.1163 | 33.72 |
| 3.8819 | 5.0 | 755 | 1.5271 | 0.1739 | 0.0342 | 0.1442 | 0.1441 | 29.99 |
| 3.8819 | 6.0 | 906 | 1.5005 | 0.1616 | 0.0337 | 0.1275 | 0.1286 | 36.78 |
| 1.1888 | 7.0 | 1057 | 1.4912 | 0.1618 | 0.0417 | 0.121 | 0.1212 | 41.89 |
| 1.1888 | 8.0 | 1208 | 1.4854 | 0.1293 | 0.0396 | 0.0905 | 0.0911 | 36.59 |
| 1.1888 | 9.0 | 1359 | 1.4949 | 0.1451 | 0.0337 | 0.1153 | 0.1158 | 27.18 |
| 0.8646 | 10.0 | 1510 | 1.4936 | 0.1743 | 0.0355 | 0.1292 | 0.1301 | 34.24 |
| 0.8646 | 11.0 | 1661 | 1.5060 | 0.1639 | 0.0367 | 0.1233 | 0.124 | 30.56 |
| 0.8646 | 12.0 | 1812 | 1.5051 | 0.1576 | 0.0366 | 0.108 | 0.1085 | 34.29 |
Framework versions
- Transformers 4.36.2
- Pytorch 1.12.1+cu113
- Datasets 2.16.1
- Tokenizers 0.15.0
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Jan 25, 2024
- Updated
- Jan 25, 2024