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:
- 3cda8e514131d9ed8f27e38a19abe281708eccfdd5d0ba0222b0f7d47e9e6de3
- Size of remote file:
- 471 MB
- SHA256:
- c876f6e179fd8e147eb5171dc246bc5a6cedfa731f4f9ff4505f30d616a5405f
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