---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_2e-05_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_2e-05_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: 0.0128
- Precision: 0.8756
- Recall: 0.9135
- F1: 0.8942
- Accuracy: 0.9974

## 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.486         | 1.0   | 25   | 0.0910          | 0.0       | 0.0    | 0.0    | 0.9858   |
| 0.0765        | 2.0   | 50   | 0.0410          | 0.6267    | 0.2541 | 0.3615 | 0.9889   |
| 0.0399        | 3.0   | 75   | 0.0230          | 0.6513    | 0.6865 | 0.6684 | 0.9941   |
| 0.0254        | 4.0   | 100  | 0.0176          | 0.7170    | 0.8216 | 0.7657 | 0.9957   |
| 0.0139        | 5.0   | 125  | 0.0129          | 0.8710    | 0.8757 | 0.8733 | 0.9968   |
| 0.0078        | 6.0   | 150  | 0.0107          | 0.9027    | 0.9027 | 0.9027 | 0.9974   |
| 0.0057        | 7.0   | 175  | 0.0110          | 0.8763    | 0.9189 | 0.8971 | 0.9975   |
| 0.0042        | 8.0   | 200  | 0.0113          | 0.8718    | 0.9189 | 0.8947 | 0.9971   |
| 0.003         | 9.0   | 225  | 0.0118          | 0.8802    | 0.9135 | 0.8966 | 0.9974   |
| 0.0022        | 10.0  | 250  | 0.0121          | 0.8877    | 0.8973 | 0.8925 | 0.9972   |
| 0.0019        | 11.0  | 275  | 0.0126          | 0.8756    | 0.9135 | 0.8942 | 0.9972   |
| 0.0016        | 12.0  | 300  | 0.0126          | 0.8802    | 0.9135 | 0.8966 | 0.9974   |
| 0.0015        | 13.0  | 325  | 0.0129          | 0.8769    | 0.9243 | 0.9    | 0.9974   |
| 0.0013        | 14.0  | 350  | 0.0128          | 0.8756    | 0.9135 | 0.8942 | 0.9974   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2