---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: i2b22014
      type: i2b22014
      config: i2b22014-deid
      split: train
      args: i2b22014-deid
    metrics:
    - name: Precision
      type: precision
      value: 0.7776378519384726
    - name: Recall
      type: recall
      value: 0.7946502435885652
    - name: F1
      type: f1
      value: 0.7860520094562647
    - name: Accuracy
      type: accuracy
      value: 0.9908687950002661
---

<!-- 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. -->

# electramed-small-deid2014-ner-v3

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0354
- Precision: 0.7776
- Recall: 0.7947
- F1: 0.7861
- Accuracy: 0.9909

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0125        | 1.0   | 1838  | 0.1338          | 0.3514    | 0.3812 | 0.3657 | 0.9715   |
| 0.0032        | 2.0   | 3676  | 0.0856          | 0.4444    | 0.5156 | 0.4774 | 0.9778   |
| 0.0012        | 3.0   | 5514  | 0.0678          | 0.5222    | 0.5994 | 0.5581 | 0.9819   |
| 0.0006        | 4.0   | 7352  | 0.0547          | 0.6900    | 0.7025 | 0.6962 | 0.9865   |
| 0.018         | 5.0   | 9190  | 0.0466          | 0.7227    | 0.7468 | 0.7345 | 0.9881   |
| 0.0002        | 6.0   | 11028 | 0.0419          | 0.7396    | 0.7664 | 0.7528 | 0.9891   |
| 0.0002        | 7.0   | 12866 | 0.0390          | 0.7730    | 0.7693 | 0.7712 | 0.9901   |
| 0.0002        | 8.0   | 14704 | 0.0368          | 0.7778    | 0.7822 | 0.7800 | 0.9906   |
| 0.0001        | 9.0   | 16542 | 0.0359          | 0.7765    | 0.7898 | 0.7831 | 0.9907   |
| 0.0001        | 10.0  | 18380 | 0.0354          | 0.7776    | 0.7947 | 0.7861 | 0.9909   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1