**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output.
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**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models." * Our fine-tuning code can be found here. You can use the CAMeLBERT-DA SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT-Mix NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. You can use the CAMeLBERT Mix SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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**CAMeLBERT MSA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT MSA NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools NER component:
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This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark. New! We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model. See twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.
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This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. More details in the reference paper.
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This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. This model has integrated into the TweetNLP Python library.
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This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
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This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see XLM-T). - Reference Paper: _TweetEval_ (Findings of EMNLP 2020). - Git Repo: Tweeteval official repository. New! We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. See twitter-roberta-base-sentiment-latest and TweetNLP for more details.
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