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
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README.md
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#### With Sentence Transformers:
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```python
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import scipy.spatial
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corpus = [
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'एक आदमी खाना खा रहा है।',
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'लोग ब्रेड का एक टुकड़ा खा रहे हैं।',
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#### With Sentence Transformers:
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```python
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from sentence_transformers import SentenceTransformer
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import scipy.spatial
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model = SentenceTransformer('prithivida/miniMiracle_hi_v1')
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corpus = [
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'एक आदमी खाना खा रहा है।',
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'लोग ब्रेड का एक टुकड़ा खा रहे हैं।',
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