transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:elnasharomar2/ANER_arabic_keyword_extraction_dataset1 autotrain_compatible endpoints_compatible region:us
---
base_model: elnasharomar2/ANER_arabic_keyword_extraction_dataset1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ANER_arabic_keyword_extraction_dataset1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ANER_arabic_keyword_extraction_dataset1

This model is a fine-tuned version of [elnasharomar2/ANER_arabic_keyword_extraction_dataset1](https://huggingface.co/elnasharomar2/ANER_arabic_keyword_extraction_dataset1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1708
- Precision: 0.7713
- Recall: 0.7690
- F1: 0.7701
- Accuracy: 0.9767

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0062        | 1.0   | 675   | 0.1415          | 0.74      | 0.7404 | 0.7402 | 0.9737   |
| 0.0074        | 2.0   | 1350  | 0.1452          | 0.7086    | 0.7726 | 0.7392 | 0.9727   |
| 0.0061        | 3.0   | 2025  | 0.1356          | 0.7296    | 0.7447 | 0.7371 | 0.9742   |
| 0.0051        | 4.0   | 2700  | 0.1448          | 0.7456    | 0.7374 | 0.7415 | 0.9743   |
| 0.0038        | 5.0   | 3375  | 0.1437          | 0.7696    | 0.7453 | 0.7572 | 0.9763   |
| 0.0029        | 6.0   | 4050  | 0.1555          | 0.7702    | 0.7562 | 0.7632 | 0.9763   |
| 0.0028        | 7.0   | 4725  | 0.1500          | 0.7636    | 0.7483 | 0.7559 | 0.9757   |
| 0.0028        | 8.0   | 5400  | 0.1522          | 0.7648    | 0.7574 | 0.7611 | 0.9761   |
| 0.0019        | 9.0   | 6075  | 0.1585          | 0.7584    | 0.7671 | 0.7627 | 0.9757   |
| 0.002         | 10.0  | 6750  | 0.1637          | 0.7567    | 0.7659 | 0.7613 | 0.9754   |
| 0.0019        | 11.0  | 7425  | 0.1686          | 0.7783    | 0.7514 | 0.7646 | 0.9760   |
| 0.0013        | 12.0  | 8100  | 0.1659          | 0.7877    | 0.7538 | 0.7704 | 0.9770   |
| 0.0011        | 13.0  | 8775  | 0.1636          | 0.7759    | 0.7683 | 0.7721 | 0.9767   |
| 0.001         | 14.0  | 9450  | 0.1720          | 0.7733    | 0.7635 | 0.7684 | 0.9765   |
| 0.0009        | 15.0  | 10125 | 0.1708          | 0.7713    | 0.7690 | 0.7701 | 0.9767   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
