---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_2e-05_ES12
  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_ES12

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: 0.0173
- Precision: 0.8446
- Recall: 0.8811
- F1: 0.8624
- Accuracy: 0.9969

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5018        | 1.47  | 25   | 0.1002          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0852        | 2.94  | 50   | 0.0509          | 0.9286    | 0.0703 | 0.1307 | 0.9852   |
| 0.0373        | 4.41  | 75   | 0.0283          | 0.5485    | 0.6108 | 0.5780 | 0.9918   |
| 0.0256        | 5.88  | 100  | 0.0204          | 0.6429    | 0.7297 | 0.6835 | 0.9938   |
| 0.0123        | 7.35  | 125  | 0.0188          | 0.8063    | 0.8324 | 0.8191 | 0.9956   |
| 0.008         | 8.82  | 150  | 0.0171          | 0.7979    | 0.8324 | 0.8148 | 0.9958   |
| 0.0047        | 10.29 | 175  | 0.0158          | 0.8010    | 0.8919 | 0.8440 | 0.9962   |
| 0.0037        | 11.76 | 200  | 0.0171          | 0.8511    | 0.8649 | 0.8579 | 0.9964   |
| 0.0025        | 13.24 | 225  | 0.0184          | 0.8368    | 0.8595 | 0.848  | 0.9962   |
| 0.002         | 14.71 | 250  | 0.0180          | 0.8223    | 0.8757 | 0.8482 | 0.9961   |
| 0.0018        | 16.18 | 275  | 0.0176          | 0.8571    | 0.8757 | 0.8663 | 0.9966   |
| 0.0014        | 17.65 | 300  | 0.0170          | 0.8402    | 0.8811 | 0.8602 | 0.9968   |
| 0.0011        | 19.12 | 325  | 0.0180          | 0.8438    | 0.8757 | 0.8594 | 0.9968   |
| 0.001         | 20.59 | 350  | 0.0197          | 0.8482    | 0.8757 | 0.8617 | 0.9968   |
| 0.001         | 22.06 | 375  | 0.0161          | 0.8402    | 0.8811 | 0.8602 | 0.9969   |
| 0.0009        | 23.53 | 400  | 0.0161          | 0.8316    | 0.8811 | 0.8556 | 0.9968   |
| 0.0008        | 25.0  | 425  | 0.0191          | 0.8663    | 0.8757 | 0.8710 | 0.9969   |
| 0.0009        | 26.47 | 450  | 0.0155          | 0.8639    | 0.8919 | 0.8777 | 0.9972   |
| 0.0008        | 27.94 | 475  | 0.0140          | 0.8737    | 0.9351 | 0.9034 | 0.9977   |
| 0.0008        | 29.41 | 500  | 0.0171          | 0.8534    | 0.8811 | 0.8670 | 0.9970   |
| 0.0007        | 30.88 | 525  | 0.0170          | 0.8632    | 0.8865 | 0.8747 | 0.9971   |
| 0.0007        | 32.35 | 550  | 0.0162          | 0.8601    | 0.8973 | 0.8783 | 0.9973   |
| 0.0006        | 33.82 | 575  | 0.0162          | 0.8601    | 0.8973 | 0.8783 | 0.9973   |
| 0.0006        | 35.29 | 600  | 0.0170          | 0.8534    | 0.8811 | 0.8670 | 0.9971   |
| 0.0006        | 36.76 | 625  | 0.0167          | 0.8557    | 0.8973 | 0.8760 | 0.9971   |
| 0.0005        | 38.24 | 650  | 0.0166          | 0.8549    | 0.8919 | 0.8730 | 0.9970   |
| 0.0005        | 39.71 | 675  | 0.0163          | 0.8513    | 0.8973 | 0.8737 | 0.9970   |
| 0.0005        | 41.18 | 700  | 0.0171          | 0.8497    | 0.8865 | 0.8677 | 0.9969   |
| 0.0005        | 42.65 | 725  | 0.0190          | 0.8526    | 0.8757 | 0.8640 | 0.9969   |
| 0.0005        | 44.12 | 750  | 0.0178          | 0.8490    | 0.8811 | 0.8647 | 0.9969   |
| 0.0005        | 45.59 | 775  | 0.0173          | 0.8446    | 0.8811 | 0.8624 | 0.9969   |


### Framework versions

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