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

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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5141
- Accuracy: 0.8189
- Precision: 0.6146
- Recall: 0.5864
- F1: 0.5983
- Weighted F1: 0.8169

## 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   | 0.5409          | 0.807    | 0.6881    | 0.5326 | 0.5615 | 0.7971      |
| No log        | 2.0   | 50   | 0.5368          | 0.8108   | 0.7021    | 0.5447 | 0.5781 | 0.8012      |
| No log        | 3.0   | 75   | 0.5383          | 0.8161   | 0.6921    | 0.5484 | 0.5835 | 0.8057      |
| No log        | 4.0   | 100  | 0.5349          | 0.8131   | 0.6408    | 0.5885 | 0.6008 | 0.8103      |
| No log        | 5.0   | 125  | 0.5436          | 0.8157   | 0.6275    | 0.606  | 0.6097 | 0.8136      |
| No log        | 6.0   | 150  | 0.5488          | 0.8201   | 0.6805    | 0.5826 | 0.6043 | 0.8146      |


### Framework versions

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