Visit the NLP Town website for an updated version of this model, with a 40% error reduction on product reviews. This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks.

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nomic-embed-text-v1

nomic-ai

`nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. **Exciting Update!**: `nomic-embed-text-v1` is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal! **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed.

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nomic-embed-text-v1.5

nomic-ai

**Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! nomic-embed-vision-v1.5 is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal! **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. For example, if you are implementing a RAG application, you embed your documents as `search_document: ` and embed your user queries as `search_query: `.

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nomic-embed-text-v2-moe

nomic-ai

This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. `nomic-embed-text-v2-moe` is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval: - **High Performance**: SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size - **Multilinguality**: Supports ~100 languages and trained on over 1.6B pairs - **Flexible Embedding Dimension**: Trained with Matryoshka Embeddings with 3x reductions in storage cost with minimal performance degradations - **Fully Open-Source**: Model weights, code, and training data (see code repo) released

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armiger-ultra

NorviTech

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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Minos-v1

NousResearch

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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nerkor-cars-onpp-hubert

novakat

The underlying corpus, NerKor+CARS-OntoNotes++, was derived from NYTK-NerKor, a Hungarian gold standard named entity annotated corpus containing about 1 million tokens. It includes a small addition of 12k tokens of text (individual sentences) concerning motor vehicles (cars, buses, motorcycles) from the news archive of hvg.hu. While the annotation in NYTK-NerKor followed the CoNLL2002 labelling standard with just four NE categories (`PER`, `LOC`, `MISC`, `ORG`), this version of the corpus features over 30 entity types, including all entity types used in the [OntoNotes 5.0] English NER annotation. The new annotation elaborates on subtypes of the `LOC` and `MISC` entity types, and includes annotation for non-names like times and dates, quantities, languages and nationalities or religious or political groups. The annotation was elaborated with further entity subtypes not present in the Ontonotes 5 annotation (see below).

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