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# MARBERT Sarcasm Detector

This model is fine-tuned UBC-NLP/MARBERTv2  was finetuned on ArSarcasT corpus training dataset.
It achieves the following results on the evaluation sets:

| Eval Datatset| Accuracy  |  F1	 | Precision	| Recall|
| :-----      | :---:     | :---:   | :---:        | :---: |
|ArSarcasT	   |0.844 	   | 0.735 	 |  0.754 	    | 0.718 |
|iSarcasmEVAL  |0.892 	   | 0.633 	 |	0.616 	    | 0.650 |
|ArSarcasmV2  |0.771 	   | 0.561   |  0.590 	    | 0.534 |



## Model description

Fine-tuned MARBERT-v2 model on Sarcastic tweets dataset for sarcasm detection text classification. 

## Intended uses & limitations

More information needed

## Training and evaluation data

- Training dataset: ArSarcasT development split.
- Evaluation Datasets: 
  - ArSarcasm-v2 test dataset.
  - iSarcasmEVAL test dataset.
  - ArSarcasT test dataset.
    
## Training procedure
Fine-tuning, 3 epochs
### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32

### Training results

### Framework versions

- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
{"language": ["ar"], "license": "afl-3.0", "tags": ["generated_from_keras_callback"], "metrics": ["Accuracy", "F1 score", "Precision", "Recall"], "pipeline_tag": "text-classification", "widget": [{"text": "\u0628\u0639\u062f \u0623\u0646 \u062d\u0635\u0644 \u0627\u0644\u0637\u0627\u0644\u0628 \u0639\u0644\u0649 \u0634\u0647\u0627\u062f\u0629 \u0627\u0644\u0644\u064a\u0633\u0627\u0646\u0633 \u0628\u062f\u0623 \u0641\u0649 \u062a\u062d\u0636\u064a\u0631 \u0627\u0644\u0645\u0627\u062c\u0633\u062a\u064a\u0631.", "example_title": "NonSarc 01"}, {"text": "\u0628\u0639\u062f \u0623\u0646 \u062d\u0635\u0644 \u0639\u0644\u0649 \u0627\u0644\u0644\u064a\u0633\u0627\u0646\u0633 \u0628\u062f\u0623 \u0641\u0649 \u062a\u062d\u0636\u064a\u0631\u0627\u0644\u0645\u0627\u062c\u0633\u062a\u064a\u0631 .\u0648\u0628\u0639\u062f \u0623\u0646 \u062d\u0635\u0644 \u0639\u0644\u0649 \u0627\u0644\u0645\u0627\u062c\u0633\u062a\u064a\u0631 \u0628\u062f\u0623 \u0641\u0649 \u00ab\u062a\u062d\u0636\u064a\u0631\u00bb \u0627\u0644\u0634\u0627\u0649 \u0644\u0644\u0632\u0628\u0627\u0626\u0646.", "example_title": "Sarc 01"}, {"text": " .\u062c\u0645\u0639 \u0643\u0644\u0645\u0629 \u0627\u0645\u0631\u0627\u0621\u0629 \u0647\u064a \u0643\u0644\u0645\u0629 \u0646\u0633\u0627\u0621", "example_title": "NonSarc 02"}, {"text": ".\u062c\u0645\u0639 \u0643\u0644\u0645\u0629 \u0627\u0645\u0631\u0627\u0621\u0629 \u0647\u064a \u0643\u0644\u0645\u0629 \u0646\u0633\u0627\u0621.   \u062d\u062a\u0649 \u0627\u0644\u0644\u063a\u0629 \u0645\u0634 \u0642\u0627\u062f\u0631\u0629 \u0639\u0644\u064a\u0647\u0645  \u0647\u0647\u0647\u0647\u0647\u0647\u0647\u0647", "example_title": "Sarc 02"}, {"text": ".\u0644\u0644\u062c\u0627\u0645\u0639\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0645\u0648\u0642\u0641\u0627 \u0643\u0628\u064a\u0631\u0627", "example_title": "NonSarc 03"}, {"text": " \u0644\u0644\u062c\u0627\u0645\u0639\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0644\u0644\u0627\u0646\u0635\u0627\u0641 \u0645\u0648\u0642\u0641\u0627\u064e \u0643\u0628\u064a\u0631\u0627\u064e !!!!! \u064a\u062a\u0633\u0639 \u0644\u0627\u0643\u062b\u0631 \u0645\u0646 \u0627\u0644\u0641 \u0633\u064a\u0627\u0631\u0629 \u0627\u0645\u0627\u0645 \u0627\u0644\u0645\u0628\u0646\u0649 ,  \u0647\u0627\u0647\u0627\u0647\u0627  ", "example_title": "Sarc 03"}, {"text": "!!\u0647\u0648 \u0623\u0646\u062a \u0643\u062f\u0629 \u0639\u0627\u064a\u0634\u061f \u064a\u0627 \u0628\u0646\u064a \u062f\u0627 \u0623\u0646\u062a \u0645\u064a\u062a \u0628\u0627\u0644\u062d\u064a\u0627\u0629", "example_title": "Sarc 04"}, {"text": "\u0634\u0647\u0631 \u0623\u0643\u062a\u0648\u0628\u0631 \u062f\u0647 \u0634\u0647\u0631 \u0639\u0633\u0644 \u0628\u062c\u062f \u0645\u0628\u064a\u062c\u0628\u0634 \u0644\u064a\u0627 \u0641\u064a\u0647 \u063a\u064a\u0631 \u0627\u0644\u0627\u0643\u062a\u0626\u0627\u0628 \u0643\u0644 \u0633\u0646\u0647", "example_title": "Sarc 05"}, {"text": " \u0641\u0649 \u0646\u0627\u0633 \u0632\u0649 \u0627\u0644\u0646\u0633\u0643\u0627\u0641\u064a\u0647 \u062b\u0644\u0627\u062b\u0629 \u0641\u0649 \u0648\u0627\u062d\u062f \u064a\u062a\u0643\u0644\u0645\u0648\u0627 \u0645\u0639\u0627\u0643 \u0648\u0639\u0646\u0643 \u0648\u0639\u0644\u064a\u0643", "example_title": "Sarc 06"}, {"text": "  \u0641\u064a \u0646\u0627\u0633 \u0632\u064a \u0627\u0644\u0646\u0633\u0645\u0629 \u0631\u0648\u062d\u0647\u0645 \u062e\u0641\u064a\u0641\u064a\u0629 \u0648 \u0648\u062c\u062f\u0647\u0645 \u064a\u0634\u0631\u062d \u0627\u0644\u0635\u062f\u0631. \u0627\u0647\u0644\u0627 \u0648 \u0633\u0647\u0644\u0627 \u0628\u0643\u0645 . \u0627\u0633\u0639\u062f\u0646\u0627 \u0645\u0631\u0648\u0631\u0643\u0645 \u0645\u0646 \u0647\u0646\u0627. ", "example_title": "NonSarc 06"}], "model-index": [{"name": "\u2019Marbert-sarcasm-detector", "results": []}]}
transformers pytorch tf bert text-classification generated_from_keras_callback ar license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #ar #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

MARBERT Sarcasm Detector
========================


This model is fine-tuned UBC-NLP/MARBERTv2 was finetuned on ArSarcasT corpus training dataset.
It achieves the following results on the evaluation sets:



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


Fine-tuned MARBERT-v2 model on Sarcastic tweets dataset for sarcasm detection text classification.


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


* Training dataset: ArSarcasT development split.
* Evaluation Datasets:
	+ ArSarcasm-v2 test dataset.
	+ iSarcasmEVAL test dataset.
	+ ArSarcasT test dataset.


Training procedure
------------------


Fine-tuning, 3 epochs


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: None
* training\_precision: float32


### Training results


### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: None
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #ar #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: None
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3