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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: multiCorp_5e-05_LabelNorm_0404
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. -->
# multiCorp_5e-05_LabelNorm_0404
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
- Precision: 0.6966
- Recall: 0.6039
- F1: 0.6469
- Accuracy: 0.9907
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2699 | 0.08 | 25 | 0.1037 | 0.0 | 0.0 | 0.0 | 0.9838 |
| 0.0844 | 0.15 | 50 | 0.0821 | 0.0 | 0.0 | 0.0 | 0.9838 |
| 0.0776 | 0.23 | 75 | 0.0761 | 0.0 | 0.0 | 0.0 | 0.9838 |
| 0.0767 | 0.31 | 100 | 0.0680 | 0.0 | 0.0 | 0.0 | 0.9838 |
| 0.0541 | 0.39 | 125 | 0.0602 | 0.6392 | 0.2054 | 0.3110 | 0.9856 |
| 0.0586 | 0.46 | 150 | 0.0553 | 0.7209 | 0.1447 | 0.2411 | 0.9851 |
| 0.051 | 0.54 | 175 | 0.0508 | 0.3651 | 0.4265 | 0.3934 | 0.9837 |
| 0.0325 | 0.62 | 200 | 0.0449 | 0.4337 | 0.3307 | 0.3753 | 0.9858 |
| 0.0429 | 0.7 | 225 | 0.0454 | 0.5562 | 0.4272 | 0.4833 | 0.9874 |
| 0.0357 | 0.77 | 250 | 0.0448 | 0.5351 | 0.3502 | 0.4233 | 0.9855 |
| 0.0542 | 0.85 | 275 | 0.0420 | 0.6611 | 0.2778 | 0.3912 | 0.9872 |
| 0.0396 | 0.93 | 300 | 0.0379 | 0.5781 | 0.4755 | 0.5218 | 0.9882 |
| 0.0354 | 1.01 | 325 | 0.0417 | 0.5083 | 0.6195 | 0.5584 | 0.9877 |
| 0.0326 | 1.08 | 350 | 0.0378 | 0.5295 | 0.4677 | 0.4967 | 0.9875 |
| 0.0277 | 1.16 | 375 | 0.0413 | 0.7018 | 0.3424 | 0.4603 | 0.9882 |
| 0.0429 | 1.24 | 400 | 0.0431 | 0.4924 | 0.5767 | 0.5312 | 0.9869 |
| 0.025 | 1.32 | 425 | 0.0368 | 0.6256 | 0.5331 | 0.5756 | 0.9891 |
| 0.0364 | 1.39 | 450 | 0.0353 | 0.6193 | 0.5354 | 0.5743 | 0.9882 |
| 0.0321 | 1.47 | 475 | 0.0366 | 0.6695 | 0.4949 | 0.5691 | 0.9895 |
| 0.0238 | 1.55 | 500 | 0.0340 | 0.5968 | 0.5230 | 0.5574 | 0.9895 |
| 0.0289 | 1.63 | 525 | 0.0320 | 0.6191 | 0.5907 | 0.6045 | 0.9900 |
| 0.0272 | 1.7 | 550 | 0.0325 | 0.5938 | 0.6257 | 0.6093 | 0.9898 |
| 0.028 | 1.78 | 575 | 0.0316 | 0.6309 | 0.6 | 0.6151 | 0.9903 |
| 0.025 | 1.86 | 600 | 0.0364 | 0.6781 | 0.3852 | 0.4913 | 0.9891 |
| 0.0235 | 1.93 | 625 | 0.0323 | 0.6543 | 0.6467 | 0.6505 | 0.9903 |
| 0.0256 | 2.01 | 650 | 0.0320 | 0.7196 | 0.5292 | 0.6099 | 0.9904 |
| 0.0182 | 2.09 | 675 | 0.0337 | 0.6773 | 0.6436 | 0.6600 | 0.9910 |
| 0.0179 | 2.17 | 700 | 0.0319 | 0.6592 | 0.6218 | 0.6400 | 0.9905 |
| 0.0171 | 2.24 | 725 | 0.0340 | 0.6966 | 0.6039 | 0.6469 | 0.9907 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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