---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_5e-05_0404_ES6_strict_2
  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. -->

# SETH_5e-05_0404_ES6_strict_2

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: 0.0578
- Precision: 0.7121
- Recall: 0.8812
- F1: 0.7877
- Accuracy: 0.9827

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.38          | 0.96  | 25   | 0.1107          | 0.4376    | 0.4768 | 0.4563 | 0.9653   |
| 0.0752        | 1.92  | 50   | 0.0615          | 0.6796    | 0.8468 | 0.7540 | 0.9797   |
| 0.0437        | 2.88  | 75   | 0.0502          | 0.7317    | 0.8589 | 0.7902 | 0.9820   |
| 0.0334        | 3.85  | 100  | 0.0523          | 0.7228    | 0.8933 | 0.7991 | 0.9820   |
| 0.0273        | 4.81  | 125  | 0.0486          | 0.7668    | 0.8657 | 0.8133 | 0.9838   |
| 0.0223        | 5.77  | 150  | 0.0474          | 0.7949    | 0.8606 | 0.8264 | 0.9855   |
| 0.0152        | 6.73  | 175  | 0.0524          | 0.8569    | 0.7831 | 0.8183 | 0.9855   |
| 0.0143        | 7.69  | 200  | 0.0578          | 0.7121    | 0.8812 | 0.7877 | 0.9827   |


### Framework versions

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