---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_5e-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_5e-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: 0.0165
- Precision: 0.8814
- Recall: 0.9243
- F1: 0.9024
- Accuracy: 0.9977

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2905        | 1.47  | 25   | 0.0978          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0551        | 2.94  | 50   | 0.0382          | 0.3893    | 0.6270 | 0.4803 | 0.9887   |
| 0.0239        | 4.41  | 75   | 0.0192          | 0.5915    | 0.7514 | 0.6619 | 0.9947   |
| 0.0111        | 5.88  | 100  | 0.0153          | 0.8564    | 0.8703 | 0.8633 | 0.9964   |
| 0.0031        | 7.35  | 125  | 0.0126          | 0.8731    | 0.9297 | 0.9005 | 0.9975   |
| 0.002         | 8.82  | 150  | 0.0129          | 0.865     | 0.9351 | 0.8987 | 0.9978   |
| 0.0013        | 10.29 | 175  | 0.0163          | 0.8830    | 0.8973 | 0.8901 | 0.9968   |
| 0.0011        | 11.76 | 200  | 0.0171          | 0.9       | 0.9243 | 0.912  | 0.9970   |
| 0.001         | 13.24 | 225  | 0.0165          | 0.8808    | 0.9189 | 0.8995 | 0.9973   |
| 0.0008        | 14.71 | 250  | 0.0138          | 0.8923    | 0.9405 | 0.9158 | 0.9981   |
| 0.0007        | 16.18 | 275  | 0.0165          | 0.8763    | 0.9189 | 0.8971 | 0.9975   |
| 0.0005        | 17.65 | 300  | 0.0170          | 0.8854    | 0.9189 | 0.9019 | 0.9974   |
| 0.0005        | 19.12 | 325  | 0.0148          | 0.8731    | 0.9297 | 0.9005 | 0.9979   |
| 0.0005        | 20.59 | 350  | 0.0171          | 0.8848    | 0.9135 | 0.8989 | 0.9973   |
| 0.0005        | 22.06 | 375  | 0.0176          | 0.8848    | 0.9135 | 0.8989 | 0.9973   |
| 0.0005        | 23.53 | 400  | 0.0167          | 0.8860    | 0.9243 | 0.9048 | 0.9975   |
| 0.0004        | 25.0  | 425  | 0.0166          | 0.8860    | 0.9243 | 0.9048 | 0.9976   |
| 0.0004        | 26.47 | 450  | 0.0165          | 0.8814    | 0.9243 | 0.9024 | 0.9977   |
| 0.0004        | 27.94 | 475  | 0.0165          | 0.8814    | 0.9243 | 0.9024 | 0.9977   |


### Framework versions

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