Sentence Similarity
sentence-transformers
Safetensors
clip
feature-extraction
dense
Generated from Trainer
dataset_size:70
loss:ContrastiveLoss
Instructions to use philipp-zettl/MTGEmb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use philipp-zettl/MTGEmb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("philipp-zettl/MTGEmb") sentences = [ "Wastewood Verge", "https://cards.scryfall.io/normal/front/0/0/00012bd8-ed68-4978-a22d-f450c8a6e048.jpg?1559596693", "https://cards.scryfall.io/normal/front/0/0/0001b4a6-10bf-4bdd-a9da-fe78d9d39a82.jpg?1739573394", "https://cards.scryfall.io/normal/front/0/0/0000a54c-a511-4925-92dc-01b937f9afad.jpg?1562701869" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from philipp-zettl/MTGEmb: direct link, hf CLI and curl.
- Browser
- Download file 111 Bytes
-
https://huggingface.co/philipp-zettl/MTGEmb/resolve/main/modules.json
- Command line
-
hf download hf://philipp-zettl/MTGEmb/modules.json
-
curl -L -o modules.json https://huggingface.co/philipp-zettl/MTGEmb/resolve/main/modules.json
111 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.CLIPModel" | |
| } | |
| ] |