---
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-finetuned
  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-finetuned

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: 0.6868
- Accuracy: 0.7768
- Precision: 0.5392
- Recall: 0.4561
- F1: 0.4898
- Weighted F1: 0.764

## 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   | 0.9323          | 0.7124   | 0.3944    | 0.1486 | 0.1309 | 0.5993      |
| No log        | 2.0   | 50   | 0.8737          | 0.7248   | 0.5187    | 0.2132 | 0.2341 | 0.6271      |
| No log        | 3.0   | 75   | 0.8157          | 0.7353   | 0.4968    | 0.2886 | 0.3314 | 0.6804      |
| No log        | 4.0   | 100  | 0.7927          | 0.7452   | 0.5213    | 0.3185 | 0.3686 | 0.6883      |
| No log        | 5.0   | 125  | 0.7601          | 0.7507   | 0.5119    | 0.3734 | 0.4161 | 0.7116      |
| No log        | 6.0   | 150  | 0.7480          | 0.7555   | 0.5381    | 0.3829 | 0.4285 | 0.718       |
| No log        | 7.0   | 175  | 0.7393          | 0.7588   | 0.5393    | 0.4031 | 0.4479 | 0.7272      |
| No log        | 8.0   | 200  | 0.7342          | 0.7655   | 0.5512    | 0.4143 | 0.4614 | 0.7363      |
| No log        | 9.0   | 225  | 0.7391          | 0.7591   | 0.5262    | 0.4425 | 0.4709 | 0.7395      |
| No log        | 10.0  | 250  | 0.7264          | 0.7644   | 0.5332    | 0.4539 | 0.4849 | 0.7484      |
| No log        | 11.0  | 275  | 0.7350          | 0.7694   | 0.5419    | 0.452  | 0.4852 | 0.7483      |
| No log        | 12.0  | 300  | 0.7389          | 0.77     | 0.5341    | 0.4641 | 0.4921 | 0.752       |


### Framework versions

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