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
Download tokenizer_config.json from Tien09/tiny_bert_ft_sim_score_1: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/Tien09/tiny_bert_ft_sim_score_1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Tien09/tiny_bert_ft_sim_score_1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Tien09/tiny_bert_ft_sim_score_1/resolve/main/tokenizer_config.json
1.3 kB
| { | |
| "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]" | |
| } | |