transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:ncbi_disease model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: electramed-small-NCBI-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: train
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.8083491461100569
      name: Precision
    - type: recall
      value: 0.8875
      name: Recall
    - type: f1
      value: 0.846077457795432
      name: F1
    - type: accuracy
      value: 0.9820794382985671
      name: Accuracy
---

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# electramed-small-NCBI-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.8083
- Recall: 0.8875
- F1: 0.8461
- Accuracy: 0.9821

## 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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4787        | 1.0   | 340  | 0.5090          | 0.6090    | 0.5062 | 0.5529 | 0.9608   |
| 0.2029        | 2.0   | 680  | 0.1890          | 0.7643    | 0.8208 | 0.7916 | 0.9774   |
| 0.1402        | 3.0   | 1020 | 0.1106          | 0.7839    | 0.8802 | 0.8292 | 0.9807   |
| 0.075         | 4.0   | 1360 | 0.0876          | 0.8162    | 0.8698 | 0.8422 | 0.9817   |
| 0.0408        | 5.0   | 1700 | 0.0776          | 0.8090    | 0.8781 | 0.8422 | 0.9818   |
| 0.0308        | 6.0   | 2040 | 0.0697          | 0.8044    | 0.8823 | 0.8415 | 0.9825   |
| 0.0405        | 7.0   | 2380 | 0.0680          | 0.8118    | 0.8854 | 0.8470 | 0.9830   |
| 0.0138        | 8.0   | 2720 | 0.0665          | 0.8111    | 0.8854 | 0.8466 | 0.9826   |
| 0.0223        | 9.0   | 3060 | 0.0675          | 0.8064    | 0.8896 | 0.8460 | 0.9821   |
| 0.0395        | 10.0  | 3400 | 0.0664          | 0.8083    | 0.8875 | 0.8461 | 0.9821   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
