Instructions to use salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa") model.load_adapter("salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2", set_active=True) - Notebooks
- Google Colab
- Kaggle
Adapter salohnana2018/ABSA-SentencePair-corrected-domainAdapt-Stack-HARD50-Adapter-parallel-run2 for CAMeL-Lab/bert-base-arabic-camelbert-msa
An 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.
Usage
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)
Architecture & Training
Evaluation results
Citation
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