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# SADAF_test3

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0061
- Macro F1: 0.7951
- Precision: 0.7874
- Recall: 0.8073
- Kappa: 0.7169
- Accuracy: 0.8073

## Model description

Relation identification for explicit dataset

## 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: 32
- eval_batch_size: 32
- seed: 25
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 76   | 1.0304          | 0.6632   | 0.6222    | 0.7436 | 0.5881 | 0.7436   |
| No log        | 2.0   | 152  | 0.8135          | 0.7191   | 0.6933    | 0.7800 | 0.6519 | 0.7800   |
| No log        | 3.0   | 228  | 0.7417          | 0.7715   | 0.7663    | 0.8007 | 0.6973 | 0.8007   |
| No log        | 4.0   | 304  | 0.7449          | 0.7807   | 0.7704    | 0.7957 | 0.6999 | 0.7957   |
| No log        | 5.0   | 380  | 0.7447          | 0.7874   | 0.7770    | 0.8089 | 0.7128 | 0.8089   |
| No log        | 6.0   | 456  | 0.8034          | 0.7654   | 0.7599    | 0.7750 | 0.6761 | 0.7750   |
| 0.7186        | 7.0   | 532  | 0.8874          | 0.7672   | 0.7669    | 0.7750 | 0.6785 | 0.7750   |
| 0.7186        | 8.0   | 608  | 0.8737          | 0.7830   | 0.7729    | 0.7974 | 0.7030 | 0.7974   |
| 0.7186        | 9.0   | 684  | 0.8964          | 0.7785   | 0.7675    | 0.7924 | 0.6978 | 0.7924   |
| 0.7186        | 10.0  | 760  | 0.9368          | 0.7863   | 0.7761    | 0.7998 | 0.7071 | 0.7998   |
| 0.7186        | 11.0  | 836  | 0.9717          | 0.7897   | 0.7803    | 0.8040 | 0.7119 | 0.8040   |
| 0.7186        | 12.0  | 912  | 0.9876          | 0.7883   | 0.7810    | 0.8007 | 0.7086 | 0.8007   |
| 0.7186        | 13.0  | 988  | 0.9893          | 0.7893   | 0.7812    | 0.8023 | 0.7106 | 0.8023   |
| 0.1542        | 14.0  | 1064 | 0.9999          | 0.7917   | 0.7841    | 0.8023 | 0.7109 | 0.8023   |
| 0.1542        | 15.0  | 1140 | 1.0061          | 0.7951   | 0.7874    | 0.8073 | 0.7169 | 0.8073   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy"], "model-index": [{"name": "SADAF_test3", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

SADAF\_test3
============


This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0061
* Macro F1: 0.7951
* Precision: 0.7874
* Recall: 0.8073
* Kappa: 0.7169
* Accuracy: 0.8073


Model description
-----------------


Relation identification for explicit dataset


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: 32
* eval\_batch\_size: 32
* seed: 25
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 25
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-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: 32
* eval\_batch\_size: 32
* seed: 25
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-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: 32
* eval\_batch\_size: 32
* seed: 25
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3