transformers pytorch bert text-classification generated_from_trainer dataset:epi_classify4_gard model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- epi_classify4_gard
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: results
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: epi_classify4_gard
      type: epi_classify4_gard
      args: default
    metrics:
    - type: precision
      value: 0.875
      name: Precision
    - type: recall
      value: 0.9032258064516129
      name: Recall
    - type: f1
      value: 0.8888888888888888
      name: F1
    - type: accuracy
      value: 0.986
      name: Accuracy
---

<!-- 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. -->

# results

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
