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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_5e-05
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_5e-05
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: 0.0165
- Precision: 0.8814
- Recall: 0.9243
- F1: 0.9024
- Accuracy: 0.9977
## 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2905 | 1.47 | 25 | 0.0978 | 0.0 | 0.0 | 0.0 | 0.9843 |
| 0.0551 | 2.94 | 50 | 0.0382 | 0.3893 | 0.6270 | 0.4803 | 0.9887 |
| 0.0239 | 4.41 | 75 | 0.0192 | 0.5915 | 0.7514 | 0.6619 | 0.9947 |
| 0.0111 | 5.88 | 100 | 0.0153 | 0.8564 | 0.8703 | 0.8633 | 0.9964 |
| 0.0031 | 7.35 | 125 | 0.0126 | 0.8731 | 0.9297 | 0.9005 | 0.9975 |
| 0.002 | 8.82 | 150 | 0.0129 | 0.865 | 0.9351 | 0.8987 | 0.9978 |
| 0.0013 | 10.29 | 175 | 0.0163 | 0.8830 | 0.8973 | 0.8901 | 0.9968 |
| 0.0011 | 11.76 | 200 | 0.0171 | 0.9 | 0.9243 | 0.912 | 0.9970 |
| 0.001 | 13.24 | 225 | 0.0165 | 0.8808 | 0.9189 | 0.8995 | 0.9973 |
| 0.0008 | 14.71 | 250 | 0.0138 | 0.8923 | 0.9405 | 0.9158 | 0.9981 |
| 0.0007 | 16.18 | 275 | 0.0165 | 0.8763 | 0.9189 | 0.8971 | 0.9975 |
| 0.0005 | 17.65 | 300 | 0.0170 | 0.8854 | 0.9189 | 0.9019 | 0.9974 |
| 0.0005 | 19.12 | 325 | 0.0148 | 0.8731 | 0.9297 | 0.9005 | 0.9979 |
| 0.0005 | 20.59 | 350 | 0.0171 | 0.8848 | 0.9135 | 0.8989 | 0.9973 |
| 0.0005 | 22.06 | 375 | 0.0176 | 0.8848 | 0.9135 | 0.8989 | 0.9973 |
| 0.0005 | 23.53 | 400 | 0.0167 | 0.8860 | 0.9243 | 0.9048 | 0.9975 |
| 0.0004 | 25.0 | 425 | 0.0166 | 0.8860 | 0.9243 | 0.9048 | 0.9976 |
| 0.0004 | 26.47 | 450 | 0.0165 | 0.8814 | 0.9243 | 0.9024 | 0.9977 |
| 0.0004 | 27.94 | 475 | 0.0165 | 0.8814 | 0.9243 | 0.9024 | 0.9977 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
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