metaclip-2-worldwide-m16-384
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metaclip-2-worldwide-s16
facebookDistilled MetaCLIP 2 (worldwide) was presented in MetaCLIP 2: A Worldwide Scaling Recipe. This checkpoint corresponds to "ViT-S-16-worldwide" of the original implementation. In case you want to perform pre- and postprocessing yourself, you can use the `AutoModel` API:
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View details ↗metaclip-2-worldwide-s16-384
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View details ↗metaclip-b16-400m
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View details ↗metaclip-b16-fullcc2.5b
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View details ↗metaclip-b32-400m
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View details ↗metaclip-b32-fullcc2.5b
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View details ↗metaclip-h14-fullcc2.5b
facebookMetaCLIP model applied to 2.5 billion data points of CommonCrawl (CC). It was introduced in the paper Demystifying CLIP Data by Xu et al. and first released in this repository. Disclaimer: The team releasing MetaCLIP did not write a model card for this model so this model card has been written by the Hugging Face team. The Demystifying CLIP Data paper aims to reveal CLIP’s method around training data curation. OpenAI never open-sourced code regarding their data preparation pipeline.
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View details ↗metaclip-l14-400m
facebook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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View details ↗metaclip-l14-fullcc2.5b
facebook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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View details ↗metamotivo-M-1
facebookMeta Motivo is a behavioral foundation model pre-trained with a novel unsupervised reinforcement learning algorithm to control the movements of a complex virtual humanoid agent. At test time, our model can be prompted to solve unseen tasks such as motion tracking, pose reaching, and reward optimization without any additional learning or fine-tuning. Meta Motivo M is our largest and most performant model. It is pretrained as described in the paper "Zero-shot Whole-Body Humanoid Control via Behavioral Foundation Models", and it can be interactively tested in our demo. Meta Motivo is composed of multiple networks - forward net \\(F(s,a,z)\\) - backward net \\(B(s)\\) - actor net \\(\pi(s,z)\\) - discriminator net \\(D(s,z)\\) - critic net \\(Q(s,a,z)\\)
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View details ↗metamotivo-S-1
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View details ↗metamotivo-S-2
facebook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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View details ↗metamotivo-S-3
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View details ↗metamotivo-S-4
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View details ↗metamotivo-S-5
facebook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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View details ↗MEXMA
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View details ↗mgenre-wiki
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View details ↗MMS
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View details ↗mms-1b
facebookMMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). It is pretrained in with Wav2Vec2's self-supervised training objective on about 500,000 hours of speech data in over 1,400 languages. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out the **How-to-fine section or **this blog** for more information about ASR. @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} }
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View details ↗mms-1b-all
facebookThis checkpoint is a model fine-tuned for multi-lingual ASR and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 1162 languages. - Example - Supported Languages - Model details - Additional links This MMS checkpoint can be used with Transformers to transcribe audio of 1107 different languages. Let's look at a simple example.
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View details ↗mms-1b-fl102
facebookThis checkpoint is a model fine-tuned for multi-lingual ASR and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 100+ languages. The checkpoint consists of **1 billion parameters** and has been fine-tuned from facebook/mms-1b on 102 languages of Fleurs. This MMS checkpoint can be used with Transformers to transcribe audio of 1107 different languages. Let's look at a simple example. **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available on PyPI make sure to install `transformers` from source:
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View details ↗mms-1b-l1107
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View details ↗mms-300m
facebookMMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). It is pretrained in with Wav2Vec2's self-supervised training objective on about 500,000 hours of speech data in over 1,400 languages. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out the **How-to-fine section or **this blog** for more information about ASR. - How to Finetune - Model details - Additional links
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