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 .gitattributes from facebook/meta-encoder-v1: direct link, hf CLI and curl.
- Browser
- Download file 152 Bytes
-
https://huggingface.co/facebook/meta-encoder-v1/resolve/main/.gitattributes
- Command line
-
hf download hf://facebook/meta-encoder-v1/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/facebook/meta-encoder-v1/resolve/main/.gitattributes
152 Bytes
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| benchmarks.png filter=lfs diff=lfs merge=lfs -text | |