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
File size: 1,083 Bytes
d5de844 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"image_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "MuseGlimmerImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_tokens": 4096,
"merge_size": 2,
"patch_size": 14,
"resample": 1,
"rescale_factor": 0.00392156862745098,
"temporal_patch_size": 2
},
"processor_class": "MuseGlimmerProcessor",
"video_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": true,
"fps": 2.0,
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"max_video_frame_tokens": 144,
"merge_size": 2,
"num_frames": 96,
"patch_size": 14,
"resample": 1,
"rescale_factor": 0.00392156862745098,
"return_metadata": true,
"temporal_patch_size": 2,
"video_processor_type": "MuseGlimmerVideoProcessor"
}
} |