fasttext-vls-vectors
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fasttext-vo-vectors
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View details ↗fasttext-wa-vectors
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View details ↗fasttext-war-vectors
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View details ↗fasttext-xmf-vectors
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View details ↗fasttext-yi-vectors
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View details ↗fasttext-yo-vectors
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View details ↗fasttext-zea-vectors
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View details ↗fasttext-zh-vectors
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View details ↗flava-full
facebookFLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total and thus fully reproducible. Unimodal datasets ImageNet and BookCorpus + CCNews were also used to provide unimodal data to the model. The model (i) similar to CLIP can be used for arbitrary image classification tasks in a zero-shot manner (ii) used for image or text retrieval in a zero-shot manner (iii) can also be fine-tuned for natural language understanding (NLU) tasks such as GLUE and vision-and-language reasoning tasks such as VQA v2. The model is able to use the data available as images, text corpus and image-text pairs. In the original paper, the authors evaluate FLAVA on 32 tasks from computer vision, NLU and vision-and-language domains and show impressive performance across the board scoring higher micro-average than CLIP while being open. The FLAVA model uses a ViT-B/32 transformer for both image encoder and text encoder. FLAVA also employs a multimodal encoder on top for multimodal tasks such as vision-and-language tasks (VQA) which is a 6-layer encoder. Each component of FLAVA model can be loaded individually from `facebook/flava-full` checkpoint. If you need complete heads used for pretraining, please use `FlavaForPreTraining` model class otherwise `FlavaModel` should suffice for most use case. This repository also contains code to pretrain the FLAVA model from scratch. FLAVA model supports vision, language and multimodal inputs. You can pass inputs corresponding to the domain you are concerned with to get losses and outputs related to that domain.
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View details ↗flava-image-codebook
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View details ↗Flow_LLM_testmodel
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View details ↗galactica-1.3b
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View details ↗galactica-120b
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View details ↗galactica-125m
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View details ↗galactica-30b
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View details ↗galactica-6.7b
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View details ↗genre-kilt
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View details ↗genre-linking-aidayago2
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View details ↗genre-linking-blink
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View details ↗hf-seamless-m4t-large
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View details ↗hf-seamless-m4t-medium
facebookSeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. This repository hosts 🤗 Hugging Face's implementation of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories (large and medium checkpoints). **🌟 SeamlessM4T v2, an improved version of this version with a novel architecture, has been released here. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks.**
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View details ↗hiera_base_16x224.mae_k400
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View details ↗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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