README.md

---
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
should probably proofread and complete it, then remove this comment. -->

# 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: 0.5163
- Accuracy: 0.8153
- Precision: 0.6449
- Recall: 0.5356
- F1: 0.5682
- Weighted F1: 0.8067

## 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   | 0.5489          | 0.8057   | 0.7074    | 0.5047 | 0.5477 | 0.7897      |
| No log        | 2.0   | 50   | 0.5456          | 0.811    | 0.6724    | 0.5482 | 0.5813 | 0.8018      |
| No log        | 3.0   | 75   | 0.5504          | 0.8148   | 0.6741    | 0.5468 | 0.5885 | 0.8029      |
| No log        | 4.0   | 100  | 0.5482          | 0.8123   | 0.644     | 0.5883 | 0.6115 | 0.8073      |


### Framework versions

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