---

# SentimentArEng

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.502831
- Accuracy: 0.798512

# inference with pipeline

```
from transformers import pipeline
model_path = "Noor0/SentimentArEng"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("تعامل الموظفين كان أقل من المتوقع")

```

 - output:
 - [{'label': 'negative', 'score': 0.9905518293380737}]


## Training and evaluation data

 
 - Training set: 114,885 records 
 - evaluation data: 12,765 records


## Training procedure



| Training Loss | Epoch |Validation Loss | Accuracy  |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.4511        | 2.0   |0.502831         | 0.7985   |
| 0.3655        | 3.0   |0.576118         | 0.7954   |
| 0.3019        | 4.0   |0.625391         | 0.7985   |
| 0.2466        | 5.0   |0.835689         | 0.7979   |



### Training hyperparameters

- The following hyperparameters were used during training:
    - learning_rate=2e-5
    - num_train_epochs=20
    - weight_decay=0.01
    - batch_size=16,  
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.14.1
{"language": ["ar", "en"], "library_name": "transformers", "metrics": ["accuracy"], "base_model": "cardiffnlp/twitter-xlm-roberta-base-sentiment", "pipeline_tag": "text-classification", "model-index": [{"name": "result", "results": []}]}
transformers safetensors xlm-roberta text-classification ar en base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment autotrain_compatible endpoints_compatible region:us