---
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: 0.5318
- Accuracy: 0.8135
- Precision: 0.6269
- Recall: 0.5274
- F1: 0.5645
- Weighted F1: 0.803

## 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   | 0.5636          | 0.7997   | 0.7329    | 0.4936 | 0.5295 | 0.7843      |
| No log        | 2.0   | 50   | 0.5561          | 0.8001   | 0.6425    | 0.5518 | 0.5689 | 0.7962      |
| No log        | 3.0   | 75   | 0.5495          | 0.8093   | 0.7031    | 0.5298 | 0.568  | 0.7974      |
| No log        | 4.0   | 100  | 0.5552          | 0.8036   | 0.6191    | 0.5854 | 0.5981 | 0.8002      |
| No log        | 5.0   | 125  | 0.5588          | 0.8069   | 0.6268    | 0.587  | 0.6008 | 0.8032      |


### Framework versions

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