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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmVar_5e-05_30_03
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_30_03
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: 0.0230
- Precision: 0.8677
- Recall: 0.8865
- F1: 0.8770
- Accuracy: 0.9964
## Model description
Trained on Token set with max_length=475
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3602 | 1.39 | 25 | 0.0547 | 0.4823 | 0.3676 | 0.4172 | 0.9851 |
| 0.0498 | 2.78 | 50 | 0.0305 | 0.4518 | 0.5568 | 0.4988 | 0.9912 |
| 0.0237 | 4.17 | 75 | 0.0198 | 0.6338 | 0.7297 | 0.6784 | 0.9942 |
| 0.0089 | 5.56 | 100 | 0.0164 | 0.7895 | 0.8919 | 0.8376 | 0.9960 |
| 0.0036 | 6.94 | 125 | 0.0138 | 0.7826 | 0.8757 | 0.8265 | 0.9967 |
| 0.0023 | 8.33 | 150 | 0.0148 | 0.8462 | 0.8919 | 0.8684 | 0.9969 |
| 0.0012 | 9.72 | 175 | 0.0159 | 0.7890 | 0.9297 | 0.8536 | 0.9966 |
| 0.0012 | 11.11 | 200 | 0.0163 | 0.845 | 0.9135 | 0.8779 | 0.9970 |
| 0.001 | 12.5 | 225 | 0.0165 | 0.8534 | 0.8811 | 0.8670 | 0.9967 |
| 0.0012 | 13.89 | 250 | 0.0215 | 0.8020 | 0.8757 | 0.8372 | 0.9961 |
| 0.0008 | 15.28 | 275 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 |
| 0.0007 | 16.67 | 300 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 |
| 0.0005 | 18.06 | 325 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 |
| 0.0009 | 19.44 | 350 | 0.0230 | 0.8677 | 0.8865 | 0.8770 | 0.9964 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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