GUI-Actor-7B-Qwen2.5-VL
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GUI-Actor-7B-Qwen2-VL
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詳細を見る ↗GUI-Actor-Verifier-2B
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詳細を見る ↗HARC
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詳細を見る ↗HARC-Llama-3.1-8B-Instruct
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詳細を見る ↗HARC-Qwen2.5-7B-Instruct
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詳細を見る ↗harrier-oss-v1-0.6b
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
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詳細を見る ↗harrier-oss-v1-270m
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
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詳細を見る ↗harrier-oss-v1-27b
Microsoftharrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `bitext_query`. You can also use a custom instruction directly via e.g. `model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ")`.
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詳細を見る ↗InfoAgent
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詳細を見る ↗infoxlm-base
Microsoft**InfoXLM** (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
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詳細を見る ↗infoxlm-large
Microsoft**InfoXLM** (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
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詳細を見る ↗kosmos-2.5
MicrosoftKosmos-2.5 is a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared decoder-only auto-regressive Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models. Since this is a generative model, there is a risk of **hallucination** during the generation process, and it **CAN NOT** guarantee the accuracy of all OCR/Markdown results in the images.
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詳細を見る ↗kosmos-2.5-chat
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詳細を見る ↗kosmos-2-patch14-224
MicrosoftThis Hub repository contains a HuggingFace's `transformers` implementation of the original Kosmos-2 model from Microsoft. This model is capable of performing different tasks through changing the prompts. Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image:
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詳細を見る ↗latent-zoning-networks
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詳細を見る ↗layoutlm-base-cased
MicrosoftLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: LayoutLM: Pre-training of Text and Layout for Document Image Understanding Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, KDD 2020 Note that LayoutLM-Cased requires a different tokenizer, based on RobertaTokenizer. You can initialize it as follows:
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詳細を見る ↗layoutlm-base-uncased
MicrosoftLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: We pre-train LayoutLM on IIT-CDIP Test Collection 1.0\* dataset with two settings. * LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters **(This Model)** * LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters
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詳細を見る ↗layoutlm-large-uncased
MicrosoftLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: LayoutLM: Pre-training of Text and Layout for Document Image Understanding Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, KDD 2020
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詳細を見る ↗layoutlmv2-base-uncased
MicrosoftThe documentation of this model in the Transformers library can be found here.
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詳細を見る ↗layoutlmv2-large-uncased
MicrosoftLayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including , including FUNSD (0.7895 → 0.8420), CORD (0.9493 → 0.9601), SROIE (0.9524 → 0.9781), Kleister-NDA (0.834 → 0.852), RVL-CDIP (0.9443 → 0.9564), and DocVQA (0.7295 → 0.8672). LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, ACL 2021
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詳細を見る ↗layoutlmv3-base
MicrosoftLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. The content of this project itself is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Portions of the source code are based on the transformers project. Microsoft Open Source Code of Conduct
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詳細を見る ↗layoutlmv3-base-chinese
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詳細を見る ↗layoutlmv3-large
MicrosoftLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. The content of this project itself is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Portions of the source code are based on the transformers project. Microsoft Open Source Code of Conduct
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