SentimentArEng / README.md
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metadata
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
metrics:
  - accuracy
model-index:
  - name: result
    results: []
language:
  - ar
  - en
library_name: transformers
pipeline_tag: text-classification

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:

  • 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("ุชุนุงู…ู„ ุงู„ู…ูˆุธููŠู† ูƒุงู† ุฃู‚ู„ ู…ู† ุงู„ู…ุชูˆู‚ุน")
  • 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