---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_2e-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_2e-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: 0.0136
- Precision: 0.8308
- Recall: 0.8757
- F1: 0.8526
- Accuracy: 0.9968

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5077        | 1.47  | 25   | 0.1015          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0834        | 2.94  | 50   | 0.0463          | 0.3581    | 0.4162 | 0.3850 | 0.9877   |
| 0.0348        | 4.41  | 75   | 0.0315          | 0.3846    | 0.4324 | 0.4071 | 0.9896   |
| 0.0285        | 5.88  | 100  | 0.0234          | 0.5157    | 0.6216 | 0.5637 | 0.9927   |
| 0.0149        | 7.35  | 125  | 0.0174          | 0.7801    | 0.8054 | 0.7926 | 0.9957   |
| 0.0104        | 8.82  | 150  | 0.0156          | 0.78      | 0.8432 | 0.8104 | 0.9959   |
| 0.0059        | 10.29 | 175  | 0.0160          | 0.8360    | 0.8541 | 0.8449 | 0.9960   |
| 0.005         | 11.76 | 200  | 0.0139          | 0.8333    | 0.8649 | 0.8488 | 0.9964   |
| 0.003         | 13.24 | 225  | 0.0164          | 0.8263    | 0.8486 | 0.8373 | 0.9961   |
| 0.0024        | 14.71 | 250  | 0.0146          | 0.7980    | 0.8541 | 0.8251 | 0.9964   |
| 0.0023        | 16.18 | 275  | 0.0132          | 0.8267    | 0.9027 | 0.8630 | 0.9969   |
| 0.0016        | 17.65 | 300  | 0.0133          | 0.8274    | 0.8811 | 0.8534 | 0.9971   |
| 0.0015        | 19.12 | 325  | 0.0129          | 0.8235    | 0.9081 | 0.8638 | 0.9971   |
| 0.0014        | 20.59 | 350  | 0.0163          | 0.8703    | 0.8703 | 0.8703 | 0.9968   |
| 0.0013        | 22.06 | 375  | 0.0141          | 0.8402    | 0.8811 | 0.8602 | 0.9969   |
| 0.0013        | 23.53 | 400  | 0.0145          | 0.8438    | 0.8757 | 0.8594 | 0.9968   |
| 0.0011        | 25.0  | 425  | 0.0149          | 0.8482    | 0.8757 | 0.8617 | 0.9969   |
| 0.0011        | 26.47 | 450  | 0.0138          | 0.8351    | 0.8757 | 0.8549 | 0.9968   |
| 0.0011        | 27.94 | 475  | 0.0136          | 0.8308    | 0.8757 | 0.8526 | 0.9968   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2