---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v4
  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: 0.7571112095702259
    - name: Recall
      type: recall
      value: 0.7853663020498207
    - name: F1
      type: f1
      value: 0.770979967514889
    - name: Accuracy
      type: accuracy
      value: 0.9906153616114308
---

<!-- 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-v4

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: 0.0362
- Precision: 0.7571
- Recall: 0.7854
- F1: 0.7710
- Accuracy: 0.9906

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0143        | 1.0   | 1838  | 0.1451          | 0.3136    | 0.3463 | 0.3291 | 0.9700   |
| 0.0033        | 2.0   | 3676  | 0.0940          | 0.4293    | 0.4861 | 0.4559 | 0.9758   |
| 0.0014        | 3.0   | 5514  | 0.0725          | 0.4906    | 0.5766 | 0.5301 | 0.9799   |
| 0.0007        | 4.0   | 7352  | 0.0568          | 0.6824    | 0.7022 | 0.6921 | 0.9860   |
| 0.0112        | 5.0   | 9190  | 0.0497          | 0.6966    | 0.7400 | 0.7177 | 0.9870   |
| 0.0002        | 6.0   | 11028 | 0.0442          | 0.7126    | 0.7549 | 0.7332 | 0.9878   |
| 0.0002        | 7.0   | 12866 | 0.0404          | 0.7581    | 0.7591 | 0.7586 | 0.9896   |
| 0.0002        | 8.0   | 14704 | 0.0376          | 0.7540    | 0.7804 | 0.7670 | 0.9904   |
| 0.0002        | 9.0   | 16542 | 0.0367          | 0.7548    | 0.7825 | 0.7684 | 0.9905   |
| 0.0001        | 10.0  | 18380 | 0.0362          | 0.7571    | 0.7854 | 0.7710 | 0.9906   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1