---
language:
- en
license: mit
base_model: mobilebert-uncased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/mobilebert-uncased-biored-finetuned
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Dagobert42/mobilebert-uncased-biored-finetuned

This model is a fine-tuned version of [mobilebert-uncased](https://huggingface.co/mobilebert-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7686
- Accuracy: 0.7387
- Precision: 0.2041
- Recall: 0.2219
- F1: 0.1908
- Weighted F1: 0.683

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log        | 1.0   | 25   | 1.2311          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 2.0   | 50   | 1.0356          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 3.0   | 75   | 1.0300          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 4.0   | 100  | 1.0246          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 5.0   | 125  | 1.0162          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 6.0   | 150  | 1.0039          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 7.0   | 175  | 0.9806          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 8.0   | 200  | 0.9148          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 9.0   | 225  | 0.8715          | 0.7187   | 0.2116    | 0.1604 | 0.1484 | 0.6172      |
| No log        | 10.0  | 250  | 0.8303          | 0.7261   | 0.1555    | 0.1972 | 0.1737 | 0.6508      |
| No log        | 11.0  | 275  | 0.8216          | 0.7292   | 0.1572    | 0.2018 | 0.1764 | 0.6554      |
| No log        | 12.0  | 300  | 0.8044          | 0.7299   | 0.2295    | 0.2081 | 0.1786 | 0.6605      |
| No log        | 13.0  | 325  | 0.8108          | 0.732    | 0.2304    | 0.2091 | 0.1797 | 0.662       |
| No log        | 14.0  | 350  | 0.7920          | 0.7306   | 0.2062    | 0.22   | 0.1877 | 0.6711      |
| No log        | 15.0  | 375  | 0.8025          | 0.7332   | 0.2164    | 0.2153 | 0.1836 | 0.6674      |
| No log        | 16.0  | 400  | 0.7937          | 0.7335   | 0.1982    | 0.2248 | 0.2039 | 0.6813      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0