Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:2839738
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use philipp-zettl/gte-micro-v4-mtg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use philipp-zettl/gte-micro-v4-mtg with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("philipp-zettl/gte-micro-v4-mtg") sentences = [ "314d5e89-55f7-42b4-af19-d4d0f499a265_c808a8ec-895c-4777-9e11-e83ce34eddef", "https://cards.scryfall.io/normal/front/3/1/314d5e89-55f7-42b4-af19-d4d0f499a265.jpg?1710406384", "https://cards.scryfall.io/normal/front/c/8/c808a8ec-895c-4777-9e11-e83ce34eddef.jpg?1593272714", "Title: Killer Instinct\nCost: {4}{R}{G}\nColors: ['G', 'R']\nType: Enchantment\nDesc: At the beginning of your upkeep, reveal the top card of your library. If it's a creature card, put it onto the battlefield. That creature gains haste until end of turn. Sacrifice it at the beginning of the next end step.", "Title: Ixidor, Reality Sculptor\nCost: {3}{U}{U}\nColors: ['U']\nType: Legendary Creature — Human Wizard\nDesc: Face-down creatures get +1/+1.\n{2}{U}: Turn target face-down creature face up." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
File size: 1,566 Bytes
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