---
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: 0.7632
- Accuracy: 0.7385
- Precision: 0.2012
- Recall: 0.2384
- F1: 0.215
- Weighted F1: 0.7009

## 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   | 1.2345          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 2.0   | 50   | 1.0379          | 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  | 1.0228          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 5.0   | 125  | 1.0144          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 6.0   | 150  | 0.9994          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 7.0   | 175  | 0.9681          | 0.7114   | 0.1016    | 0.1429 | 0.1188 | 0.5914      |
| No log        | 8.0   | 200  | 0.8869          | 0.7147   | 0.2167    | 0.1487 | 0.1303 | 0.6007      |
| No log        | 9.0   | 225  | 0.8511          | 0.7242   | 0.2064    | 0.1716 | 0.1598 | 0.6298      |
| No log        | 10.0  | 250  | 0.8187          | 0.7287   | 0.157     | 0.1991 | 0.1754 | 0.653       |
| No log        | 11.0  | 275  | 0.8046          | 0.7317   | 0.1581    | 0.2035 | 0.1775 | 0.6581      |
| No log        | 12.0  | 300  | 0.7900          | 0.732    | 0.1935    | 0.2126 | 0.1887 | 0.6688      |
| No log        | 13.0  | 325  | 0.7865          | 0.734    | 0.2312    | 0.2129 | 0.1828 | 0.6664      |
| No log        | 14.0  | 350  | 0.7758          | 0.7346   | 0.1604    | 0.2148 | 0.1819 | 0.6672      |
| No log        | 15.0  | 375  | 0.7958          | 0.7376   | 0.2086    | 0.2141 | 0.1884 | 0.6697      |
| No log        | 16.0  | 400  | 0.7757          | 0.733    | 0.2002    | 0.2347 | 0.2122 | 0.6904      |
| No log        | 17.0  | 425  | 0.7874          | 0.7393   | 0.2067    | 0.2196 | 0.2119 | 0.6828      |
| No log        | 18.0  | 450  | 0.7915          | 0.735    | 0.2043    | 0.2391 | 0.2197 | 0.6959      |


### Framework versions

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