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salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2
ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2 is a machine learning model from salohnana2018. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for adapter-transformers.
An adapter for the CAMeL-Lab/bert-base-arabic-camelbert-msa model that was trained on the Arabic ABSA/SemEvalHotelReview dataset and includes a prediction head for classification.
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From the Hugging Face model README
salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2 for CAMeL-Lab/bert-base-arabic-camelbert-msaAn adapter for the CAMeL-Lab/bert-base-arabic-camelbert-msa model that was trained on the [Arabic ABSA/SemEvalHotelReview](https://adapterhub.ml/explore/Arabic ABSA/SemEvalHotelReview/) dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
First, install adapter-transformers:
pip install -U adapter-transformers
Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More
Now, the adapter can be loaded and activated like this:
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa")
adapter_name = model.load_adapter("salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2", source="hf", set_active=True)