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: 1,300 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"added_tokens_decoder": {
"0": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"100": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"101": {
"content": "[CLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"102": {
"content": "[SEP]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"103": {
"content": "[MASK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"extra_special_tokens": {},
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|