---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v5-classweights
  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: 0.8832236842105263
    - name: Recall
      type: recall
      value: 0.6910561632502987
    - name: F1
      type: f1
      value: 0.7754112732711052
    - name: Accuracy
      type: accuracy
      value: 0.9883040491052534
---

<!-- 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-v5-classweights

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: 0.0009
- Precision: 0.8832
- Recall: 0.6911
- F1: 0.7754
- Accuracy: 0.9883

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0001        | 1.0   | 1838  | 0.0008          | 0.7702    | 0.3780 | 0.5071 | 0.9771   |
| 0.0           | 2.0   | 3676  | 0.0007          | 0.8753    | 0.5671 | 0.6883 | 0.9827   |
| 0.0           | 3.0   | 5514  | 0.0006          | 0.8074    | 0.4128 | 0.5463 | 0.9775   |
| 0.0           | 4.0   | 7352  | 0.0007          | 0.8693    | 0.6102 | 0.7170 | 0.9848   |
| 0.0           | 5.0   | 9190  | 0.0006          | 0.8710    | 0.6022 | 0.7121 | 0.9849   |
| 0.0           | 6.0   | 11028 | 0.0007          | 0.8835    | 0.6547 | 0.7521 | 0.9867   |
| 0.0           | 7.0   | 12866 | 0.0009          | 0.8793    | 0.6661 | 0.7579 | 0.9873   |
| 0.0           | 8.0   | 14704 | 0.0008          | 0.8815    | 0.6740 | 0.7639 | 0.9876   |
| 0.0           | 9.0   | 16542 | 0.0009          | 0.8812    | 0.6851 | 0.7709 | 0.9880   |
| 0.0           | 10.0  | 18380 | 0.0009          | 0.8832    | 0.6911 | 0.7754 | 0.9883   |


### Framework versions

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