Sentence Similarity
Transformers
PyTorch
ONNX
sentence-transformers
Hindi
bert
feature-extraction
miniMiracle
passage-retrieval
knowledge-distillation
middle-training
text-embeddings-inference
Instructions to use prithivida/miniDense_hindi_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/miniDense_hindi_v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prithivida/miniDense_hindi_v1") model = AutoModel.from_pretrained("prithivida/miniDense_hindi_v1") - sentence-transformers
How to use prithivida/miniDense_hindi_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prithivida/miniDense_hindi_v1") 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
- Xet hash:
- 303c2c1342c3411d08077fdbae9b292c26f1d198acc653311731c7b7d9734f38
- Size of remote file:
- 14.8 MB
- SHA256:
- 71b44701d7efd054205115acfa6ef126c5d2f84bd3affe0c59e48163674d19a6
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