Feature Extraction
Transformers
Safetensors
muse_glimmer
metaencoder
multimodal-embedding
image-text-retrieval
video-retrieval
document-retrieval
Instructions to use facebook/meta-encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/meta-encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/meta-encoder-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/meta-encoder-v1") model = AutoModel.from_pretrained("facebook/meta-encoder-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from facebook/meta-encoder-v1: direct link, hf CLI and curl.
- Browser
- Download file 28.1 MB
-
https://huggingface.co/facebook/meta-encoder-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://facebook/meta-encoder-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/facebook/meta-encoder-v1/resolve/main/tokenizer.json
28.1 MB
- Xet hash:
- f89930c709b5f7103e260b59259e1197c070b4905f44d102d859b81c3d662f83
- Size of remote file:
- 28.1 MB
- SHA256:
- c9dbee66967b58f31a7c27f723c3760da3526ccd0427578e8905b0abb0031c4d
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