Sentence Similarity
sentence-transformers
Safetensors
Turkish
xlm-roberta
turkish
turkce
academic
retrieval
feature-extraction
text-embeddings-inference
Instructions to use hakansabunis/trakad-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hakansabunis/trakad-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hakansabunis/trakad-embed-v2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
trakad-embed-v2 — Turkish academic SimCSE fine-tune (633K theses, MultipleNegativesRankingLoss + subject-aware hard negatives)
f1a23a6 verified | { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 128, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |