Sentence Similarity
sentence-transformers
Safetensors
Turkish
xlm-roberta
feature-extraction
turkish
turkish-literature
bge-m3
text-embeddings-inference
Instructions to use yusufekorman/turkce-edebiyat-embedding-bge-m3-basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yusufekorman/turkce-edebiyat-embedding-bge-m3-basic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yusufekorman/turkce-edebiyat-embedding-bge-m3-basic") sentences = [ "Deniz, dalgaların kıyıya vuruşuyla huzur veren bir ses çıkarıyordu.", "Dalgaların sahile çarptığında çıkardığı ses, insana dinginlik veriyordu.", "Bugün marketten alışveriş yapmam gerekiyor, akşama misafir gelecek." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - 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": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "max_length": 8192, | |
| "model_max_length": 256, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "<pad>", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "stride": 0, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "<unk>" | |
| } | |