GODEL-v1_1-large-seq2seq
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graphcodebert-base
MicrosoftGraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages. More details can be found in the paper by Guo et. al. **Disclaimer:** The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members.
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View details ↗Graphormer
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View details ↗GridSFM_Open
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View details ↗GRIN-MoE
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View details ↗GUI-Actor-2B-Qwen2-VL
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View details ↗GUI-Actor-3B-Qwen2.5-VL
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View details ↗GUI-Actor-7B-Qwen2.5-VL
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View details ↗GUI-Actor-7B-Qwen2-VL
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View details ↗GUI-Actor-Verifier-2B
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View details ↗HARC
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View details ↗HARC-Llama-3.1-8B-Instruct
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View details ↗HARC-Qwen2.5-7B-Instruct
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View details ↗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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View details ↗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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View details ↗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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View details ↗InfoAgent
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View details ↗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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View details ↗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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View details ↗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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View details ↗kosmos-2.5-chat
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View details ↗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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View details ↗latent-zoning-networks
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View details ↗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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