
<!-- 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

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-arabert-BioNER-EN-AR", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

bert-base-arabert-BioNER-EN-AR
==============================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2