---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: wiki_hu_ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: hu
      split: validation
      args: hu
    metrics:
    - name: Precision
      type: precision
      value: 0.8669236159775753
    - name: Recall
      type: recall
      value: 0.8782479057219935
    - name: F1
      type: f1
      value: 0.872549019607843
    - name: Accuracy
      type: accuracy
      value: 0.9632061446977205
---

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# wiki_hu_ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1585
- Precision: 0.8669
- Recall: 0.8782
- F1: 0.8725
- Accuracy: 0.9632

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2429        | 1.0   | 1250 | 0.1849          | 0.8047    | 0.8153 | 0.8100 | 0.9448   |
| 0.1371        | 2.0   | 2500 | 0.1505          | 0.8455    | 0.8577 | 0.8516 | 0.9576   |
| 0.0986        | 3.0   | 3750 | 0.1516          | 0.8520    | 0.8708 | 0.8613 | 0.9603   |
| 0.0695        | 4.0   | 5000 | 0.1500          | 0.8656    | 0.8745 | 0.8700 | 0.9624   |
| 0.0489        | 5.0   | 6250 | 0.1585          | 0.8669    | 0.8782 | 0.8725 | 0.9632   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3