README.md

---
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: []
---

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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: 1.0264
- Accuracy: 0.7163
- Precision: 0.1023
- Recall: 0.1429
- F1: 0.1192
- Weighted F1: 0.5979

## 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 | 13 | 1.6911 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 2.0 | 26 | 1.2080 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 3.0 | 39 | 1.0905 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 4.0 | 52 | 1.0400 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 5.0 | 65 | 1.0439 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 6.0 | 78 | 1.0288 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 7.0 | 91 | 1.0259 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 8.0 | 104 | 1.0180 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 9.0 | 117 | 1.0229 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |
| No log | 10.0 | 130 | 1.0186 | 0.7114 | 0.1016 | 0.1429 | 0.1188 | 0.5914 |


### Framework versions

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