---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_2e-05_0404_ES6_strict_tok1
  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_0404_ES6_strict_tok1

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: 0.0330
- Precision: 0.8213
- Recall: 0.8629
- F1: 0.8416
- Accuracy: 0.9916

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7315        | 0.49  | 25   | 0.2102          | 0.0       | 0.0    | 0.0    | 0.9555   |
| 0.1371        | 0.98  | 50   | 0.1021          | 0.0       | 0.0    | 0.0    | 0.9698   |
| 0.0836        | 1.47  | 75   | 0.0960          | 0.0       | 0.0    | 0.0    | 0.9725   |
| 0.0666        | 1.96  | 100  | 0.0526          | 0.0       | 0.0    | 0.0    | 0.9804   |
| 0.0391        | 2.45  | 125  | 0.0521          | 0.7294    | 0.3147 | 0.4397 | 0.9843   |
| 0.0252        | 2.94  | 150  | 0.0382          | 0.8630    | 0.6396 | 0.7347 | 0.9899   |
| 0.016         | 3.43  | 175  | 0.0452          | 0.6496    | 0.7716 | 0.7053 | 0.9872   |
| 0.0145        | 3.92  | 200  | 0.0272          | 0.8730    | 0.8376 | 0.8549 | 0.9923   |
| 0.0082        | 4.41  | 225  | 0.0301          | 0.8804    | 0.8223 | 0.8504 | 0.9920   |
| 0.0058        | 4.9   | 250  | 0.0330          | 0.8213    | 0.8629 | 0.8416 | 0.9916   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3