Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:97043
loss:DenoisingAutoEncoderLoss
text-embeddings-inference
Instructions to use T-Blue/tsdae_pro_mbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use T-Blue/tsdae_pro_mbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("T-Blue/tsdae_pro_mbert") sentences = [ "ढचणच𑀟च𑀟", "ढ𑀢ढल𑀢𑁣ब𑀪चध𑀫ण ढचणच𑀟च𑀟 𑀞नलच𑀠च𑀟च𑀤च𑀪पच𑀯", " णच 𑀪𑀢𑀞𑁦 𑀱च𑀟𑀟च𑀟 𑀠न𑀞च𑀠𑀢𑀟 𑀫च𑀪 𑀤न𑀱च 𑀭थ𑁢𑀰𑀯", " च त𑀢𑀞𑀢𑀟 𑀠च𑀘चल𑀢𑀳च𑀪𑀠च𑀟च𑀤च𑀪पच𑀯" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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