Sentence Similarity
sentence-transformers
PyTorch
Transformers
Oriya
bert
feature-extraction
text-embeddings-inference
Instructions to use l3cube-pune/odia-sentence-bert-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use l3cube-pune/odia-sentence-bert-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("l3cube-pune/odia-sentence-bert-nli") sentences = [ "লোকটি কুড়াল দিয়ে একটি গাছ কেটে ফেলল", "একজন লোক কুড়াল দিয়ে একটি গাছের নিচে চপ করে", "একজন লোক গিটার বাজছে", "একজন মহিলা ঘোড়ায় চড়ে" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use l3cube-pune/odia-sentence-bert-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/odia-sentence-bert-nli") model = AutoModel.from_pretrained("l3cube-pune/odia-sentence-bert-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f7671f6ad47b2bae8d7ad44ef9bef2f4a7c7351ffd1093f92c9f4f44cb48022
- Size of remote file:
- 950 MB
- SHA256:
- 2700a63238222e8240d205770750bbaf792a44ddd383446b5dd22b704d3ac55c
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