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_2 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_2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tien09/tiny_bert_ft_sim_score_2") sentences = [ "This card is treated as a Normal Monster while face-up on the field or in the GY. While this card is face-up on the field, you can Normal Summon it to have it become an Effect Monster with this effect. During your End Phase: You can target 1 Equip Spell Card in your GY; add that target to your hand. You can only use this effect of \"Knight Day Grepher\" once per turn.", "You can Ritual Summon this card with \"Primal Cry\". Once per turn: You can reveal 1 monster in your hand, then target 1 face-up monster on the field; that target's Level becomes equal to the Level the revealed monster had, until the end of this turn. Once per turn, if another monster is Tributed from your hand or field (except during the Damage Step): You can target 1 monster in your GY; add it to your hand.", "If you control an Insect monster: You can Special Summon this card from your hand. During your Main Phase: You can inflict 200 damage to your opponent for each \"Battlewasp - Pin the Bullseye\" you control. You can only use each effect of \"Battlewasp - Pin the Bullseye\" once per turn.", "Add 1 \"Great Sand Sea - Gold Golgonda\" from your Deck to your hand. If \"Great Sand Sea - Gold Golgonda\" is in your Field Zone, you can apply this effect instead.\r\n● Add 1 \"Springans\" monster from your Deck to your hand, and if you do, send 1 \"Springans\" monster from your Deck to the GY.\r\nYou can only activate 1 \"Springans Watch\" per turn." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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