---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: arabic2023_ner_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: ar
      split: validation
      args: ar
    metrics:
    - name: Precision
      type: precision
      value: 0.825519413120349
    - name: Recall
      type: recall
      value: 0.8312960600907029
    - name: F1
      type: f1
      value: 0.8283976661870758
    - name: Accuracy
      type: accuracy
      value: 0.9048229813780053
---

<!-- 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. -->

# arabic2023_ner_model

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: 0.3950
- Precision: 0.8255
- Recall: 0.8313
- F1: 0.8284
- Accuracy: 0.9048

## 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: 5e-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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1594        | 1.0   | 1250 | 0.4149          | 0.8145    | 0.8133 | 0.8139 | 0.8974   |
| 0.116         | 2.0   | 2500 | 0.3950          | 0.8255    | 0.8313 | 0.8284 | 0.9048   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3