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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bionlp2004
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased_1_16619_token
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. -->
# bert-base-uncased_1_16619_token
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the bionlp2004 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1660
- Precision: 0.7730
- Recall: 0.8185
- F1: 0.7951
- Accuracy: 0.9478
## 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: 0.001
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.233 | 1.0 | 1039 | 0.1956 | 0.6860 | 0.7301 | 0.7073 | 0.9331 |
| 0.2099 | 2.0 | 2078 | 0.1890 | 0.7214 | 0.7508 | 0.7358 | 0.9370 |
| 0.1991 | 3.0 | 3117 | 0.1862 | 0.7058 | 0.7785 | 0.7404 | 0.9375 |
| 0.19 | 4.0 | 4156 | 0.1736 | 0.7420 | 0.7943 | 0.7673 | 0.9434 |
| 0.1894 | 5.0 | 5195 | 0.1748 | 0.7319 | 0.7722 | 0.7515 | 0.9417 |
| 0.1814 | 6.0 | 6234 | 0.1686 | 0.7351 | 0.7952 | 0.7640 | 0.9427 |
| 0.177 | 7.0 | 7273 | 0.1682 | 0.7404 | 0.8086 | 0.7730 | 0.9448 |
| 0.1756 | 8.0 | 8312 | 0.1740 | 0.7386 | 0.7796 | 0.7585 | 0.9423 |
| 0.1761 | 9.0 | 9351 | 0.1691 | 0.7442 | 0.7664 | 0.7551 | 0.9430 |
| 0.1693 | 10.0 | 10390 | 0.1641 | 0.7506 | 0.8113 | 0.7797 | 0.9446 |
| 0.1697 | 11.0 | 11429 | 0.1669 | 0.7297 | 0.7938 | 0.7604 | 0.9427 |
| 0.1607 | 12.0 | 12468 | 0.1654 | 0.7593 | 0.8185 | 0.7878 | 0.9454 |
| 0.1643 | 13.0 | 13507 | 0.1652 | 0.7288 | 0.8035 | 0.7644 | 0.9430 |
| 0.1618 | 14.0 | 14546 | 0.1592 | 0.7548 | 0.7988 | 0.7762 | 0.9464 |
| 0.1598 | 15.0 | 15585 | 0.1641 | 0.7575 | 0.8006 | 0.7785 | 0.9454 |
| 0.16 | 16.0 | 16624 | 0.1621 | 0.7440 | 0.8174 | 0.7790 | 0.9456 |
| 0.1572 | 17.0 | 17663 | 0.1669 | 0.7598 | 0.8015 | 0.7801 | 0.9453 |
| 0.1528 | 18.0 | 18702 | 0.1680 | 0.7332 | 0.8073 | 0.7685 | 0.9427 |
| 0.1513 | 19.0 | 19741 | 0.1653 | 0.7630 | 0.7920 | 0.7772 | 0.9453 |
| 0.1504 | 20.0 | 20780 | 0.1635 | 0.7645 | 0.8073 | 0.7853 | 0.9461 |
| 0.1491 | 21.0 | 21819 | 0.1591 | 0.7547 | 0.8262 | 0.7889 | 0.9473 |
| 0.1455 | 22.0 | 22858 | 0.1627 | 0.7634 | 0.8145 | 0.7881 | 0.9457 |
| 0.145 | 23.0 | 23897 | 0.1584 | 0.7529 | 0.8210 | 0.7855 | 0.9464 |
| 0.1438 | 24.0 | 24936 | 0.1603 | 0.7592 | 0.8012 | 0.7796 | 0.9466 |
| 0.1413 | 25.0 | 25975 | 0.1614 | 0.7699 | 0.8134 | 0.7911 | 0.9470 |
| 0.1437 | 26.0 | 27014 | 0.1594 | 0.7557 | 0.8226 | 0.7877 | 0.9465 |
| 0.1414 | 27.0 | 28053 | 0.1605 | 0.7680 | 0.8183 | 0.7923 | 0.9478 |
| 0.1385 | 28.0 | 29092 | 0.1631 | 0.7588 | 0.8028 | 0.7802 | 0.9459 |
| 0.1365 | 29.0 | 30131 | 0.1568 | 0.7701 | 0.8167 | 0.7927 | 0.9482 |
| 0.1352 | 30.0 | 31170 | 0.1607 | 0.7660 | 0.8271 | 0.7954 | 0.9481 |
| 0.1331 | 31.0 | 32209 | 0.1646 | 0.7627 | 0.8122 | 0.7867 | 0.9461 |
| 0.1328 | 32.0 | 33248 | 0.1658 | 0.7560 | 0.8176 | 0.7856 | 0.9464 |
| 0.1319 | 33.0 | 34287 | 0.1579 | 0.7639 | 0.8228 | 0.7923 | 0.9486 |
| 0.1309 | 34.0 | 35326 | 0.1595 | 0.7666 | 0.8151 | 0.7901 | 0.9471 |
| 0.1271 | 35.0 | 36365 | 0.1616 | 0.7645 | 0.8248 | 0.7935 | 0.9476 |
| 0.1262 | 36.0 | 37404 | 0.1615 | 0.7641 | 0.8104 | 0.7866 | 0.9464 |
| 0.1227 | 37.0 | 38443 | 0.1614 | 0.7667 | 0.8273 | 0.7958 | 0.9475 |
| 0.1207 | 38.0 | 39482 | 0.1640 | 0.7763 | 0.8014 | 0.7887 | 0.9472 |
| 0.1212 | 39.0 | 40521 | 0.1613 | 0.7716 | 0.8142 | 0.7923 | 0.9487 |
| 0.1192 | 40.0 | 41560 | 0.1596 | 0.7773 | 0.8196 | 0.7979 | 0.9493 |
| 0.1193 | 41.0 | 42599 | 0.1684 | 0.7769 | 0.8071 | 0.7917 | 0.9473 |
| 0.1171 | 42.0 | 43638 | 0.1636 | 0.7717 | 0.8183 | 0.7943 | 0.9471 |
| 0.1146 | 43.0 | 44677 | 0.1613 | 0.7675 | 0.8217 | 0.7937 | 0.9476 |
| 0.1154 | 44.0 | 45716 | 0.1648 | 0.7725 | 0.8066 | 0.7892 | 0.9467 |
| 0.1149 | 45.0 | 46755 | 0.1660 | 0.7745 | 0.8172 | 0.7953 | 0.9476 |
| 0.1133 | 46.0 | 47794 | 0.1655 | 0.7742 | 0.8187 | 0.7958 | 0.9480 |
| 0.1121 | 47.0 | 48833 | 0.1659 | 0.7768 | 0.8156 | 0.7957 | 0.9481 |
| 0.1104 | 48.0 | 49872 | 0.1663 | 0.7714 | 0.8129 | 0.7916 | 0.9478 |
| 0.1069 | 49.0 | 50911 | 0.1659 | 0.7746 | 0.8163 | 0.7949 | 0.9479 |
| 0.11 | 50.0 | 51950 | 0.1660 | 0.7730 | 0.8185 | 0.7951 | 0.9478 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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