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/bge-micro-v2-mtg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use philipp-zettl/bge-micro-v2-mtg with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("philipp-zettl/bge-micro-v2-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
Training in progress, step 41000
Browse files
eval/similarity_evaluation_sts-dev_results.csv
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0.5634310698429155,25000,0.6076603031406499,0.6642228922828504
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0.5634310698429155,25000,0.6076603031406499,0.6642228922828504
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0.7888034977800816,35000,0.6058150184022874,0.6603840103511748
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0.9014897117486647,40000,0.6088841930069427,0.6631234959726412
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model.safetensors
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