transformers safetensors mobilebert token-classification low-resource NER token_classification biomedicine medical NER generated_from_trainer en dataset:medicine license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: mobilebert-uncased
datasets:
- medicine
language:
- en
license: mit
metrics:
- accuracy
- precision
- recall
- f1
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
model-index:
- name: Dagobert42/mobilebert-uncased-biored-augmented
  results: []
---

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# Dagobert42/mobilebert-uncased-biored-augmented

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: 0.5602
- Accuracy: 0.8057
- Precision: 0.6022
- Recall: 0.4577
- F1: 0.5007
- Weighted F1: 0.7931

## 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   | 0.6641          | 0.7644   | 0.5563    | 0.3256 | 0.3768 | 0.7254      |
| No log        | 2.0   | 50   | 0.6506          | 0.7728   | 0.5735    | 0.369  | 0.4057 | 0.7457      |
| No log        | 3.0   | 75   | 0.6352          | 0.7779   | 0.5671    | 0.413  | 0.4527 | 0.768       |
| No log        | 4.0   | 100  | 0.6147          | 0.7886   | 0.6221    | 0.4133 | 0.4523 | 0.7723      |
| No log        | 5.0   | 125  | 0.6223          | 0.7885   | 0.6176    | 0.4051 | 0.4416 | 0.7684      |
| No log        | 6.0   | 150  | 0.6020          | 0.7913   | 0.584     | 0.4496 | 0.4928 | 0.7811      |
| No log        | 7.0   | 175  | 0.6081          | 0.7913   | 0.5508    | 0.4964 | 0.5183 | 0.7876      |
| No log        | 8.0   | 200  | 0.5935          | 0.799    | 0.6333    | 0.4461 | 0.4926 | 0.7826      |
| No log        | 9.0   | 225  | 0.6110          | 0.7941   | 0.5998    | 0.4656 | 0.5078 | 0.7825      |
| No log        | 10.0  | 250  | 0.5990          | 0.798    | 0.6332    | 0.4564 | 0.5099 | 0.7777      |


### Framework versions

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