---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-ner
  results: []
---

<!-- 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-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1548
- Precision: 0.8358
- Recall: 0.9064
- F1: 0.8697
- Accuracy: 0.9581

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5587        | 1.0   | 201  | 0.4107          | 0.7291    | 0.7982 | 0.7621 | 0.8983   |
| 0.2114        | 2.0   | 402  | 0.2663          | 0.7716    | 0.8826 | 0.8234 | 0.9445   |
| 0.1421        | 3.0   | 603  | 0.2183          | 0.8033    | 0.9030 | 0.8502 | 0.9488   |
| 0.2204        | 4.0   | 804  | 0.1878          | 0.8279    | 0.9012 | 0.8630 | 0.9553   |
| 0.5825        | 5.0   | 1005 | 0.1712          | 0.8289    | 0.8967 | 0.8615 | 0.9566   |
| 0.0685        | 6.0   | 1206 | 0.1647          | 0.8333    | 0.9067 | 0.8685 | 0.9572   |
| 0.0973        | 7.0   | 1407 | 0.1593          | 0.8365    | 0.9049 | 0.8693 | 0.9578   |
| 0.1683        | 8.0   | 1608 | 0.1574          | 0.8367    | 0.9082 | 0.8710 | 0.9577   |
| 0.065         | 9.0   | 1809 | 0.1557          | 0.8397    | 0.9052 | 0.8712 | 0.9583   |
| 0.179         | 10.0  | 2010 | 0.1548          | 0.8358    | 0.9064 | 0.8697 | 0.9581   |


### Framework versions

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