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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_0.0001
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
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: 0.0162
- Precision: 0.8877
- Recall: 0.8973
- F1: 0.8925
- Accuracy: 0.9971
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2263 | 1.47 | 25 | 0.0776 | 0.0 | 0.0 | 0.0 | 0.9843 |
| 0.05 | 2.94 | 50 | 0.0400 | 0.2868 | 0.4216 | 0.3414 | 0.9872 |
| 0.0271 | 4.41 | 75 | 0.0219 | 0.5381 | 0.6486 | 0.5882 | 0.9925 |
| 0.0108 | 5.88 | 100 | 0.0132 | 0.8324 | 0.8324 | 0.8324 | 0.9965 |
| 0.0029 | 7.35 | 125 | 0.0107 | 0.8934 | 0.9514 | 0.9215 | 0.9979 |
| 0.0025 | 8.82 | 150 | 0.0123 | 0.8691 | 0.8973 | 0.8830 | 0.9972 |
| 0.0011 | 10.29 | 175 | 0.0127 | 0.8579 | 0.9135 | 0.8848 | 0.9969 |
| 0.0006 | 11.76 | 200 | 0.0102 | 0.8969 | 0.9405 | 0.9182 | 0.9981 |
| 0.0005 | 13.24 | 225 | 0.0118 | 0.8942 | 0.9135 | 0.9037 | 0.9978 |
| 0.0005 | 14.71 | 250 | 0.0106 | 0.8768 | 0.9622 | 0.9175 | 0.9981 |
| 0.0015 | 16.18 | 275 | 0.0119 | 0.855 | 0.9243 | 0.8883 | 0.9976 |
| 0.0006 | 17.65 | 300 | 0.0134 | 0.8814 | 0.9243 | 0.9024 | 0.9977 |
| 0.0004 | 19.12 | 325 | 0.0177 | 0.8617 | 0.8757 | 0.8686 | 0.9969 |
| 0.0003 | 20.59 | 350 | 0.0162 | 0.8877 | 0.8973 | 0.8925 | 0.9971 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
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