---
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