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This is an English BERT fine-tuned with Entropy-based Attention Regularization to reduce lexical overfitting to specific words on the task of Misogyny Identification. Use this model if you want a debiased alternative to a BERT classifier. The model was fine-tuned on the Automatic Misogyny Identification dataset. This model is the fine-tuned version of the bert-base-uncased model. We trained a total of three versions for Italian and English.

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You can find the package that uses this model for emotion and sentiment classification **here** it is meant to be a very simple interface over HuggingFace models. Users should refer to the following license Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?

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You can find the package that uses this model for emotion and sentiment classification **here** it is meant to be a very simple interface over HuggingFace models. Users should refer to the following license Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?

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xlm-emo-t

MilaNLProc

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it. This model is the fine-tuned version of the XLM-T model. For models, restrictions may apply to the data (which are derived from existing datasets) or Twitter (main data source). We refer users to the original licenses accompanying each dataset and Twitter regulations.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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__Chonky__ is a transformer model that intelligently segments text into meaningful semantic chunks. This model can be used in the RAG systems. The model processes text and divides it into semantically coherent segments. These chunks can then be fed into embedding-based retrieval systems or language models as part of a RAG pipeline. I've made a small python library for this model: chonky

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__Chonky__ is a transformer model that intelligently segments text into meaningful semantic chunks. This model can be used in the RAG systems. The model processes text and divides it into semantically coherent segments. These chunks can then be fed into embedding-based retrieval systems or language models as part of a RAG pipeline. ⚠️This model was fine-tuned on sequence of length 1024 (by default ModernBERT supports sequence length up to 8192).

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LIDstral-Arabic

Mistral AI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports Matryoshka Representation Learning and binary quantization. Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages: ` for query if you want to use it for retrieval. Besides that you don't need any prompt.

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