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NuExtract-2.0-2B-GGUF

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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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NuExtract-2.0-4B

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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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NuExtract-2.0-4B-GGUF

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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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NuExtract-2.0-8B

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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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NuExtract-2.0-8B-GGUF

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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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NuExtract-tiny

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> ⚠️ **_NOTE:_** This model is out-dated. Find the updated version here > NuExtract_tiny is a version of Qwen1.5-0.5, fine-tuned on a private high-quality synthetic dataset for information extraction. To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract. Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.

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NuExtract3

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🖥️ API / Platform    |    📑 Blog    |    🗣️ Discord    |    🛠️ GitHub **NuExtract3** is a unified **4B** vision-language reasoning model for document understanding. It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables.

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NuExtract3-FP8

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🖥️ API / Platform    |    📑 Blog    |    🗣️ Discord    |    🛠️ GitHub It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. We benchmarked NuExtract on NuMind's internal structured benchmark, measuring model's performances on ~600 documents of diverse types including invoices, movie posters or floor plans. These documents and their ground-truth cover diverse use-cases testing model visual understanding, OCR, reasoning and ability to handle long input and output contexts. We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction.

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NuExtract3-GGUF

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🖥️ API / Platform    |    📑 Blog    |    🗣️ Discord    |    🛠️ GitHub It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. We benchmarked NuExtract on NuMind's internal structured benchmark, measuring model's performances on ~600 documents of diverse types including invoices, movie posters or floor plans. These documents and their ground-truth cover diverse use-cases testing model visual understanding, OCR, reasoning and ability to handle long input and output contexts. We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction.

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NuExtract3-W4A16

numind

🖥️ API / Platform    |    📑 Blog    |    🗣️ Discord    |    🛠️ GitHub It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. We benchmarked NuExtract on NuMind's internal structured benchmark, measuring model's performances on ~600 documents of diverse types including invoices, movie posters or floor plans. These documents and their ground-truth cover diverse use-cases testing model visual understanding, OCR, reasoning and ability to handle long input and output contexts. We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction.

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입력 텍스트 · 이미지→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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NuExtract3-W8A8

numind

🖥️ API / Platform    |    📑 Blog    |    🗣️ Discord    |    🛠️ GitHub It combines strong **structured information extraction** with high-quality **image-to-Markdown** conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. We benchmarked NuExtract on NuMind's internal structured benchmark, measuring model's performances on ~600 documents of diverse types including invoices, movie posters or floor plans. These documents and their ground-truth cover diverse use-cases testing model visual understanding, OCR, reasoning and ability to handle long input and output contexts. We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction.

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NuMarkdown-8B-Thinking

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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。

이미지 설명

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NuNER-multilingual-v0.1

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**Checkout other models by NuMind:** * SOTA Entity Recognition Foundation Model in English: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual Multilingual BERT finetunned on an artificially annotated multilingual subset of Oscar dataset. This model provides domain & language independent embedding for Entity Recognition Task. We fine-tunned it only on 9 languages but the model can generalize over other languages that are supported by the Multilingual BERT.

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NuNER-v0.1

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**Checkout other models by NuMind:** * SOTA Multilingual Entity Recognition Foundation Model: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual

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NuNER-v2.0

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This model provides embeddings for the Entity Recognition task in English. It is an improved version of the model from our **paper**. **Checkout other models by NuMind:** * SOTA Zero-shot NER Model NuNER Zero * SOTA Multilingual Entity Recognition Foundation Model: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual Roberta-base fine-tuned on the expanded version of NuNER data using contrastive learning from **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**.

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NuNER_Zero

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NuNER Zero is a zero-shot Named Entity Recognition (NER) Model. (Check NuNER for the few-shot setting). NuNER Zero uses the GLiNER architecture: its input should be a concatenation of entity types and text. NuNER Zero was trained on NuNER v2.0 dataset, which combines subsets of Pile and C4 annotated via LLMs using NuNER's procedure.

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