qandos0
qandos0/SentimentArEng
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Model Documentation
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This model is a fine-tuned version of 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
output:
[{'label': 'negative', 'score': 0.9905518293380737}]
Training set: 114,885 records
evaluation data: 12,765 records
| 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 |
The following hyperparameters were used during training:
learning_rate=2e-5
num_train_epochs=20
weight_decay=0.01
batch_size=16,
Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.11.0
Tokenizers 0.14.1
SentimentArEng
This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base-sentiment on the None dataset. It achieves the following results on the evaluation set:
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")
Training and evaluation data
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 |