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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_0.0001_250
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. -->
# tmvar_0.0001_250
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: 0.0142
- Precision: 0.8520
- Recall: 0.9027
- F1: 0.8766
- Accuracy: 0.9972
## 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: 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2033 | 1.0 | 25 | 0.0313 | 0.6273 | 0.3730 | 0.4678 | 0.9899 |
| 0.0336 | 2.0 | 50 | 0.0197 | 0.6723 | 0.8541 | 0.7524 | 0.9946 |
| 0.0133 | 3.0 | 75 | 0.0134 | 0.8763 | 0.8811 | 0.8787 | 0.9969 |
| 0.0075 | 4.0 | 100 | 0.0192 | 0.7110 | 0.8378 | 0.7692 | 0.9952 |
| 0.0065 | 5.0 | 125 | 0.0126 | 0.8681 | 0.8541 | 0.8610 | 0.9969 |
| 0.0029 | 6.0 | 150 | 0.0130 | 0.8513 | 0.8973 | 0.8737 | 0.9974 |
| 0.002 | 7.0 | 175 | 0.0121 | 0.8446 | 0.8811 | 0.8624 | 0.9969 |
| 0.0017 | 8.0 | 200 | 0.0103 | 0.8462 | 0.8919 | 0.8684 | 0.9974 |
| 0.0011 | 9.0 | 225 | 0.0148 | 0.8299 | 0.8703 | 0.8496 | 0.9967 |
| 0.0007 | 10.0 | 250 | 0.0150 | 0.8426 | 0.8973 | 0.8691 | 0.9971 |
| 0.0005 | 11.0 | 275 | 0.0142 | 0.8376 | 0.8919 | 0.8639 | 0.9970 |
| 0.0004 | 12.0 | 300 | 0.0142 | 0.8513 | 0.8973 | 0.8737 | 0.9972 |
| 0.0003 | 13.0 | 325 | 0.0143 | 0.8469 | 0.8973 | 0.8714 | 0.9971 |
| 0.0003 | 14.0 | 350 | 0.0142 | 0.8520 | 0.9027 | 0.8766 | 0.9972 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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