---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.8144458281444583
    - name: Recall
      type: recall
      value: 0.8605263157894737
    - name: F1
      type: f1
      value: 0.836852207293666
    - name: Accuracy
      type: accuracy
      value: 0.9804873249598268
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0735
- Precision: 0.8144
- Recall: 0.8605
- F1: 0.8369
- Accuracy: 0.9805

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0761          | 0.7560    | 0.8316 | 0.7920 | 0.9758   |
| 0.1236        | 2.0   | 680  | 0.0719          | 0.8105    | 0.8355 | 0.8228 | 0.9794   |
| 0.0397        | 3.0   | 1020 | 0.0735          | 0.8144    | 0.8605 | 0.8369 | 0.9805   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2