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README.md
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---
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base_model: SI2M-Lab/DarijaBERT-arabizi
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: ner-DarijaBERT-arabizi
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ner-DarijaBERT-arabizi
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This model is a fine-tuned version of [SI2M-Lab/DarijaBERT-arabizi](https://huggingface.co/SI2M-Lab/DarijaBERT-arabizi) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1011
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- Precision: 0.7475
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- Recall: 0.7753
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- F1: 0.7611
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- Accuracy: 0.9681
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 32 | 0.3240 | 0.5397 | 0.3251 | 0.4058 | 0.8996 |
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| No log | 2.0 | 64 | 0.2541 | 0.5593 | 0.4720 | 0.5120 | 0.9203 |
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| No log | 3.0 | 96 | 0.2062 | 0.5697 | 0.5828 | 0.5762 | 0.9350 |
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| No log | 4.0 | 128 | 0.1791 | 0.6162 | 0.6313 | 0.6236 | 0.9426 |
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| No log | 5.0 | 160 | 0.1528 | 0.6504 | 0.6803 | 0.6650 | 0.9509 |
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| No log | 6.0 | 192 | 0.1308 | 0.6880 | 0.7262 | 0.7066 | 0.9582 |
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| No log | 7.0 | 224 | 0.1189 | 0.7126 | 0.7270 | 0.7198 | 0.9612 |
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| No log | 8.0 | 256 | 0.1100 | 0.7307 | 0.7661 | 0.7480 | 0.9651 |
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| No log | 9.0 | 288 | 0.1037 | 0.7423 | 0.7567 | 0.7494 | 0.9667 |
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| No log | 10.0 | 320 | 0.1011 | 0.7475 | 0.7753 | 0.7611 | 0.9681 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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