---
tags:
- generated_from_trainer
datasets:
- ade_drug_effect_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-DRUG-EFFECT-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ade_drug_effect_ner
      type: ade_drug_effect_ner
      config: ade
      split: train
      args: ade
    metrics:
    - name: Precision
      type: precision
      value: 0.7745054945054946
    - name: Recall
      type: recall
      value: 0.6555059523809523
    - name: F1
      type: f1
      value: 0.7100544025790851
    - name: Accuracy
      type: accuracy
      value: 0.9310355073540336
---

<!-- 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-ADE-DRUG-EFFECT-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_effect_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
- Precision: 0.7745
- Recall: 0.6555
- F1: 0.7101
- Accuracy: 0.9310

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4498        | 1.0   | 336  | 0.3042          | 0.5423    | 0.6295 | 0.5826 | 0.9114   |
| 0.2572        | 2.0   | 672  | 0.2146          | 0.7596    | 0.6194 | 0.6824 | 0.9276   |
| 0.1542        | 3.0   | 1008 | 0.1894          | 0.7806    | 0.6168 | 0.6891 | 0.9299   |
| 0.1525        | 4.0   | 1344 | 0.1771          | 0.7832    | 0.625  | 0.6952 | 0.9309   |
| 0.1871        | 5.0   | 1680 | 0.1723          | 0.7271    | 0.6920 | 0.7091 | 0.9304   |
| 0.1425        | 6.0   | 2016 | 0.1683          | 0.7300    | 0.6979 | 0.7136 | 0.9297   |
| 0.1638        | 7.0   | 2352 | 0.1654          | 0.7432    | 0.6771 | 0.7086 | 0.9306   |
| 0.1592        | 8.0   | 2688 | 0.1635          | 0.7613    | 0.6585 | 0.7062 | 0.9305   |
| 0.1882        | 9.0   | 3024 | 0.1625          | 0.7858    | 0.6373 | 0.7038 | 0.9309   |
| 0.1339        | 10.0  | 3360 | 0.1630          | 0.7745    | 0.6555 | 0.7101 | 0.9310   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1