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

This model is a fine-tuned version of [elnasharomar2/ANER_arabic_keyword_extraction](https://huggingface.co/elnasharomar2/ANER_arabic_keyword_extraction) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4047
- Precision: 0.6061
- Recall: 0.6492
- F1: 0.6269
- Accuracy: 0.9462

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0108        | 1.0   | 750   | 0.2997          | 0.5879    | 0.6302 | 0.6083 | 0.9451   |
| 0.0104        | 2.0   | 1500  | 0.2822          | 0.5699    | 0.6425 | 0.6040 | 0.9428   |
| 0.007         | 3.0   | 2250  | 0.3270          | 0.5965    | 0.6182 | 0.6072 | 0.9446   |
| 0.0053        | 4.0   | 3000  | 0.3436          | 0.5792    | 0.6439 | 0.6099 | 0.9437   |
| 0.0038        | 5.0   | 3750  | 0.3373          | 0.6063    | 0.6223 | 0.6142 | 0.9469   |
| 0.0039        | 6.0   | 4500  | 0.3518          | 0.5961    | 0.6503 | 0.6220 | 0.9462   |
| 0.0031        | 7.0   | 5250  | 0.3654          | 0.5887    | 0.6488 | 0.6173 | 0.9445   |
| 0.0029        | 8.0   | 6000  | 0.3985          | 0.5973    | 0.6492 | 0.6222 | 0.9446   |
| 0.0022        | 9.0   | 6750  | 0.3953          | 0.5927    | 0.6570 | 0.6232 | 0.9456   |
| 0.002         | 10.0  | 7500  | 0.3884          | 0.6145    | 0.6365 | 0.6253 | 0.9474   |
| 0.0015        | 11.0  | 8250  | 0.4170          | 0.5964    | 0.6566 | 0.6251 | 0.9446   |
| 0.0015        | 12.0  | 9000  | 0.4421          | 0.5918    | 0.6629 | 0.6253 | 0.9445   |
| 0.0016        | 13.0  | 9750  | 0.4313          | 0.6078    | 0.6480 | 0.6273 | 0.9465   |
| 0.0025        | 14.0  | 10500 | 0.4096          | 0.6066    | 0.6432 | 0.6244 | 0.9463   |
| 0.0023        | 15.0  | 11250 | 0.4047          | 0.6061    | 0.6492 | 0.6269 | 0.9462   |


### Framework versions

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