Sentence Similarity
sentence-transformers
Safetensors
English
pawan_embd
embedding
knowledge-distillation
custom_code
Instructions to use dmedhi/PawanEmbd-68M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dmedhi/PawanEmbd-68M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dmedhi/PawanEmbd-68M", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -52,8 +52,8 @@ import torch
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import torch.nn.functional as F
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# Load model and tokenizer
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model = AutoModel.from_pretrained("dmedhi/
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tokenizer = AutoTokenizer.from_pretrained("dmedhi/
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# Encode sentences
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sentences = ["This is an example sentence", "Each sentence is converted to a vector"]
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import torch.nn.functional as F
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# Load model and tokenizer
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model = AutoModel.from_pretrained("dmedhi/PawanEmbd-68M")
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tokenizer = AutoTokenizer.from_pretrained("dmedhi/PawanEmbd-68M")
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# Encode sentences
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sentences = ["This is an example sentence", "Each sentence is converted to a vector"]
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