
# 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/
{"language": "ar", "widget": [{"text": "\u0645\u0645\u062a\u0627\u0632"}, {"text": "\u0623\u0646\u0627 \u062d\u0632\u064a\u0646"}, {"text": "\u0644\u0627 \u0634\u064a\u0621"}]}
transformers tf bert text-classification ar autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #tf #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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: URL
# 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: URL
TAGS
#transformers #tf #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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: URL
passage: TAGS
#transformers #tf #bert #text-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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: URL