transformers safetensors distilbert token-classification low-resource NER token_classification biomedicine medical NER generated_from_trainer en dataset:medicine base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-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/distilbert-base-uncased-biored-augmented
  results: []
---

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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: 0.5692
- Accuracy: 0.7978
- Precision: 0.5993
- Recall: 0.5337
- F1: 0.5536
- Weighted F1: 0.7929

## 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   | 0.6037          | 0.7824   | 0.5931    | 0.4937 | 0.5272 | 0.7719      |
| No log        | 2.0   | 50   | 0.5858          | 0.7932   | 0.6023    | 0.5298 | 0.5511 | 0.7849      |
| No log        | 3.0   | 75   | 0.5887          | 0.795    | 0.5757    | 0.5283 | 0.544  | 0.7842      |
| No log        | 4.0   | 100  | 0.5890          | 0.7937   | 0.5911    | 0.5331 | 0.5466 | 0.7864      |


### Framework versions

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