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Yah216
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Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD

Text Classification
Transformers
TensorFlow
Arabic
bert
text-embeddings-inference
Model card Files
xet
 Community
How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")
model = AutoModelForSequenceClassification.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")
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Model description

This model is an Arabic language sentiment analysis pretrained model. The model is built on top of the CAMelBERT_msa_sixteenth BERT-based model. We used the HARD dataset of hotels review to fine tune the model. The dataset original labels based on a five-star rating were modified to a 3 label data:

  • POSITIVE: for ratings > 3 stars
  • NEUTRAL: for a 3 star rating
  • NEGATIVE: for ratings < 3 stars

This first prototype was trained on 3 epochs for 1 hours using Colab and a TPU acceleration.

Examples

Here are some examples in Arabic to test :

  • Excellent -> ممتاز(Happy)
  • I'am sad -> أنا حزين (Sad)
  • Nothing -> لا شيء (Neutral)

Contact

If you have questions or improvement remarks, feel free to contact me on my LinkedIn profile: https://www.linkedin.com/in/yahya-ghrab/

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