---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-BC2GM-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: bc2gm_corpus
      type: bc2gm_corpus
      config: bc2gm_corpus
      split: train
      args: bc2gm_corpus
    metrics:
    - name: Precision
      type: precision
      value: 0.7652071701439906
    - name: Recall
      type: recall
      value: 0.823399209486166
    - name: F1
      type: f1
      value: 0.7932373771989948
    - name: Accuracy
      type: accuracy
      value: 0.9756735092182762
---

<!-- 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-BC2GM-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0720
- Precision: 0.7652
- Recall: 0.8234
- F1: 0.7932
- Accuracy: 0.9757

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.085         | 1.0   | 782  | 0.1112          | 0.6147    | 0.7777 | 0.6867 | 0.9634   |
| 0.0901        | 2.0   | 1564 | 0.0825          | 0.7141    | 0.8028 | 0.7559 | 0.9720   |
| 0.0303        | 3.0   | 2346 | 0.0759          | 0.7310    | 0.8049 | 0.7662 | 0.9724   |
| 0.0037        | 4.0   | 3128 | 0.0735          | 0.7430    | 0.8168 | 0.7781 | 0.9735   |
| 0.0325        | 5.0   | 3910 | 0.0723          | 0.7571    | 0.8142 | 0.7846 | 0.9748   |
| 0.0582        | 6.0   | 4692 | 0.0701          | 0.7664    | 0.8144 | 0.7897 | 0.9759   |
| 0.0073        | 7.0   | 5474 | 0.0701          | 0.7711    | 0.8212 | 0.7953 | 0.9761   |
| 0.1031        | 8.0   | 6256 | 0.0712          | 0.7602    | 0.8258 | 0.7916 | 0.9756   |
| 0.0248        | 9.0   | 7038 | 0.0722          | 0.7691    | 0.8231 | 0.7952 | 0.9759   |
| 0.0136        | 10.0  | 7820 | 0.0720          | 0.7652    | 0.8234 | 0.7932 | 0.9757   |


### Framework versions

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