Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:20554
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use HeyDunaX/Tay_Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HeyDunaX/Tay_Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HeyDunaX/Tay_Embedding") sentences = [ "bon", "cây mon", "đổ chậu nước", "yên phận làm ăn" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": true, | |
| "mask_token": "<mask>", | |
| "model_max_length": 8192, | |
| "model_specific_special_tokens": {}, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |