Sentence Similarity
sentence-transformers
ONNX
Safetensors
Turkish
English
modernbert
feature-extraction
information-retrieval
dense-retrieval
turkish
legal
turkish-legal
mecellem
TRUBA
MN5
text-embeddings-inference
Instructions to use newmindai/Mursit-Base-TR-Retrieval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use newmindai/Mursit-Base-TR-Retrieval with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("newmindai/Mursit-Base-TR-Retrieval") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from newmindai/Mursit-Base-TR-Retrieval: direct link, hf CLI and curl.
- Browser
- Download file 58 Bytes
-
https://huggingface.co/newmindai/Mursit-Base-TR-Retrieval/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://newmindai/Mursit-Base-TR-Retrieval/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/newmindai/Mursit-Base-TR-Retrieval/resolve/main/sentence_bert_config.json
58 Bytes
| { | |
| "max_seq_length": 1024, | |
| "do_lower_case": false | |
| } |