Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:8959
loss:CoSENTLoss
text-embeddings-inference
Instructions to use Tien09/tiny_bert_ft_sim_score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Tien09/tiny_bert_ft_sim_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tien09/tiny_bert_ft_sim_score") sentences = [ "When this card is Normal Summoned: You can Special Summon 1 \"Crashbug X\" from your Deck. You must control a face-up \"Crashbug Z\" to activate and to resolve this effect.", "You can remove from play 1 Tuner monster in your GY to Special Summon this card from your hand.", "This spirit emerges from the mystic lamp and obeys the wishes of its summoner.", "When your opponent activates a monster effect, while you control a \"Beetrooper\" monster: Negate the activation, and if you do, destroy it. During your End Phase, if this card is in your GY and you control an Insect monster with 3000 or more ATK: You can banish 1 Insect monster from your GY; Set this card. You can only use 1 \"Beetrooper Fly & Sting\" effect per turn, and only once that turn." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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