---
tags:
- generated_from_trainer
datasets:
- bc4chemd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-BC4CHEMD-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: bc4chemd
      type: bc4chemd
      config: bc4chemd
      split: train
      args: bc4chemd
    metrics:
    - name: Precision
      type: precision
      value: 0.7715624436835465
    - name: Recall
      type: recall
      value: 0.6760888102832959
    - name: F1
      type: f1
      value: 0.7206773498518718
    - name: Accuracy
      type: accuracy
      value: 0.9770623458780496
---

<!-- 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-BC4CHEMD-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc4chemd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0655
- Precision: 0.7716
- Recall: 0.6761
- F1: 0.7207
- Accuracy: 0.9771

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0882        | 1.0   | 1918  | 0.1058          | 0.6615    | 0.3942 | 0.4940 | 0.9635   |
| 0.0555        | 2.0   | 3836  | 0.0820          | 0.7085    | 0.5133 | 0.5954 | 0.9689   |
| 0.0631        | 3.0   | 5754  | 0.0769          | 0.6892    | 0.5743 | 0.6266 | 0.9699   |
| 0.0907        | 4.0   | 7672  | 0.0682          | 0.7623    | 0.5923 | 0.6666 | 0.9740   |
| 0.0313        | 5.0   | 9590  | 0.0675          | 0.7643    | 0.6223 | 0.6860 | 0.9749   |
| 0.0306        | 6.0   | 11508 | 0.0662          | 0.7654    | 0.6398 | 0.6970 | 0.9754   |
| 0.0292        | 7.0   | 13426 | 0.0656          | 0.7694    | 0.6552 | 0.7077 | 0.9763   |
| 0.1025        | 8.0   | 15344 | 0.0658          | 0.7742    | 0.6687 | 0.7176 | 0.9769   |
| 0.0394        | 9.0   | 17262 | 0.0662          | 0.7741    | 0.6731 | 0.7201 | 0.9770   |
| 0.0378        | 10.0  | 19180 | 0.0655          | 0.7716    | 0.6761 | 0.7207 | 0.9771   |


### Framework versions

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