---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-JNLPBA-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: jnlpba
      type: jnlpba
      config: jnlpba
      split: train
      args: jnlpba
    metrics:
    - name: Precision
      type: precision
      value: 0.8224512128396863
    - name: Recall
      type: recall
      value: 0.878188899707887
    - name: F1
      type: f1
      value: 0.8494066679223958
    - name: Accuracy
      type: accuracy
      value: 0.9620705451213926
---

<!-- 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-JNLPBA-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co./giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Precision: 0.8225
- Recall: 0.8782
- F1: 0.8494
- Accuracy: 0.9621

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.398         | 1.0   | 2087  | 0.1941          | 0.7289    | 0.7936 | 0.7599 | 0.9441   |
| 0.0771        | 2.0   | 4174  | 0.1542          | 0.7734    | 0.8348 | 0.8029 | 0.9514   |
| 0.1321        | 3.0   | 6261  | 0.1413          | 0.7890    | 0.8492 | 0.8180 | 0.9546   |
| 0.2302        | 4.0   | 8348  | 0.1326          | 0.8006    | 0.8589 | 0.8287 | 0.9562   |
| 0.0723        | 5.0   | 10435 | 0.1290          | 0.7997    | 0.8715 | 0.8340 | 0.9574   |
| 0.171         | 6.0   | 12522 | 0.1246          | 0.8115    | 0.8722 | 0.8408 | 0.9593   |
| 0.1058        | 7.0   | 14609 | 0.1204          | 0.8148    | 0.8757 | 0.8441 | 0.9604   |
| 0.1974        | 8.0   | 16696 | 0.1178          | 0.8181    | 0.8779 | 0.8470 | 0.9614   |
| 0.0663        | 9.0   | 18783 | 0.1168          | 0.8239    | 0.8781 | 0.8501 | 0.9620   |
| 0.1022        | 10.0  | 20870 | 0.1167          | 0.8225    | 0.8782 | 0.8494 | 0.9621   |


### Framework versions

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