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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-arabert-BioNER-EN-AR
results: []
---
<!-- 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. -->
# bert-base-arabert-BioNER-EN-AR
This model is a fine-tuned version of [StivenLancheros/bert-base-arabert-BioNER-EN](https://huggingface.co/StivenLancheros/bert-base-arabert-BioNER-EN) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4250
- Precision: 0.7143
- Recall: 0.8209
- F1: 0.7639
- Accuracy: 0.9197
## 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: 3e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6376 | 1.0 | 680 | 0.7457 | 0.4379 | 0.6384 | 0.5195 | 0.8242 |
| 0.4549 | 2.0 | 1360 | 0.7120 | 0.4878 | 0.7113 | 0.5787 | 0.8346 |
| 0.3214 | 3.0 | 2040 | 0.5576 | 0.5676 | 0.7529 | 0.6473 | 0.8749 |
| 0.2883 | 4.0 | 2720 | 0.5304 | 0.5916 | 0.7745 | 0.6708 | 0.8808 |
| 0.2596 | 5.0 | 3400 | 0.4942 | 0.6117 | 0.7884 | 0.6889 | 0.8906 |
| 0.2168 | 6.0 | 4080 | 0.5229 | 0.6204 | 0.7977 | 0.6979 | 0.8898 |
| 0.2105 | 7.0 | 4760 | 0.4630 | 0.6501 | 0.7935 | 0.7147 | 0.8999 |
| 0.1889 | 8.0 | 5440 | 0.5048 | 0.6407 | 0.8066 | 0.7141 | 0.8958 |
| 0.1714 | 9.0 | 6120 | 0.4538 | 0.6909 | 0.7986 | 0.7409 | 0.9105 |
| 0.1626 | 10.0 | 6800 | 0.4433 | 0.6912 | 0.8070 | 0.7446 | 0.9130 |
| 0.1559 | 11.0 | 7480 | 0.4282 | 0.7006 | 0.8054 | 0.7493 | 0.9144 |
| 0.1451 | 12.0 | 8160 | 0.4475 | 0.6978 | 0.8150 | 0.7519 | 0.9135 |
| 0.1384 | 13.0 | 8840 | 0.4535 | 0.6928 | 0.8215 | 0.7517 | 0.9145 |
| 0.1331 | 14.0 | 9520 | 0.4250 | 0.7143 | 0.8209 | 0.7639 | 0.9197 |
| 0.1282 | 15.0 | 10200 | 0.4350 | 0.7108 | 0.8237 | 0.7631 | 0.9200 |
| 0.1216 | 16.0 | 10880 | 0.4385 | 0.7096 | 0.8231 | 0.7621 | 0.9188 |
| 0.1195 | 17.0 | 11560 | 0.4376 | 0.7134 | 0.8275 | 0.7662 | 0.9204 |
| 0.1187 | 18.0 | 12240 | 0.4461 | 0.7092 | 0.8297 | 0.7647 | 0.9183 |
| 0.1159 | 19.0 | 12920 | 0.4359 | 0.7215 | 0.8264 | 0.7704 | 0.9219 |
| 0.1121 | 20.0 | 13600 | 0.4358 | 0.7198 | 0.8264 | 0.7694 | 0.9217 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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