---
tags:
- generated_from_trainer
datasets:
- species_800
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-SPECIES800-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: species_800
      type: species_800
      config: species_800
      split: train
      args: species_800
    metrics:
    - name: Precision
      type: precision
      value: 0.6221498371335505
    - name: Recall
      type: recall
      value: 0.7470664928292047
    - name: F1
      type: f1
      value: 0.6789099526066352
    - name: Accuracy
      type: accuracy
      value: 0.9831434110359828
---

<!-- 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-SPECIES800-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co./giacomomiolo/electramed_small_scivocab) on the species_800 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0513
- Precision: 0.6221
- Recall: 0.7471
- F1: 0.6789
- Accuracy: 0.9831

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0536        | 1.0   | 359  | 0.0971          | 0.6138    | 0.5554 | 0.5832 | 0.9795   |
| 0.0309        | 2.0   | 718  | 0.0692          | 0.6175    | 0.6063 | 0.6118 | 0.9808   |
| 0.0563        | 3.0   | 1077 | 0.0582          | 0.6424    | 0.6910 | 0.6658 | 0.9819   |
| 0.0442        | 4.0   | 1436 | 0.0553          | 0.5900    | 0.7523 | 0.6613 | 0.9814   |
| 0.0069        | 5.0   | 1795 | 0.0511          | 0.6291    | 0.7497 | 0.6841 | 0.9827   |
| 0.0141        | 6.0   | 2154 | 0.0505          | 0.6579    | 0.7471 | 0.6996 | 0.9837   |
| 0.0052        | 7.0   | 2513 | 0.0513          | 0.5965    | 0.7458 | 0.6628 | 0.9826   |
| 0.0573        | 8.0   | 2872 | 0.0509          | 0.6140    | 0.7445 | 0.6730 | 0.9828   |
| 0.0203        | 9.0   | 3231 | 0.0516          | 0.6118    | 0.7458 | 0.6722 | 0.9830   |
| 0.0101        | 10.0  | 3590 | 0.0513          | 0.6221    | 0.7471 | 0.6789 | 0.9831   |


### Framework versions

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