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_1 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_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tien09/tiny_bert_ft_sim_score_1") 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.", "2 Beast-Warrior monsters, including a WIND \"Ancient Warriors\" monster\r\nAll \"Ancient Warriors\" monsters you control gain 500 ATK/DEF. You can only use each of the following effects of \"Ancient Warriors Oath - Double Dragon Lords\" once per turn. If this card is Link Summoned: You can add 1 \"Ancient Warriors\" card from your Deck to your hand. (Quick Effect): You can send 1 card from your hand or field to the GY, then target 1 face-up card your opponent controls; return it to the hand.", "Place 1 Ocean Counter on this card during each player's Standby Phases. When this card is removed from the field, all Fish-Type and Sea Serpent-Type monsters you control gain 200 ATK for each Ocean Counter on this card, until the End Phase.", "If you have no cards in your GY (Quick Effect): You can send this card from your hand to the GY; until the end of the next turn, any card sent to the GY is banished instead." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 622 Bytes
2c0529c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"_name_or_path": "prajjwal1/bert-tiny",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 128,
"initializer_range": 0.02,
"intermediate_size": 512,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.47.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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