
<!-- 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. -->

# feedback-classification

This model is a fine-tuned version of [aubmindlab/bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3881
- Macro F1: 0.8586
- Accuracy: 0.8586

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log        | 1.0   | 339  | 0.4434          | 0.8448   | 0.8468   |
| 0.6508        | 2.0   | 678  | 0.3881          | 0.8586   | 0.8586   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "aubmindlab/bert-large-arabertv2", "model-index": [{"name": "feedback-classification", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer base_model:aubmindlab/bert-large-arabertv2 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #base_model-aubmindlab/bert-large-arabertv2 #autotrain_compatible #endpoints_compatible #region-us 

feedback-classification
=======================


This model is a fine-tuned version of aubmindlab/bert-large-arabertv2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3881
* Macro F1: 0.8586
* Accuracy: 0.8586


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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.34.0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.14.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.34.0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.14.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #base_model-aubmindlab/bert-large-arabertv2 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.34.0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.14.1