Sentence Similarity
sentence-transformers
Safetensors
Transformers
Korean
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use SamilPwC-AXNode-GenAI/PwC-Embedding_expr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SamilPwC-AXNode-GenAI/PwC-Embedding_expr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SamilPwC-AXNode-GenAI/PwC-Embedding_expr") 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] - Transformers
How to use SamilPwC-AXNode-GenAI/PwC-Embedding_expr with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SamilPwC-AXNode-GenAI/PwC-Embedding_expr") model = AutoModel.from_pretrained("SamilPwC-AXNode-GenAI/PwC-Embedding_expr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff8b9bdcc479265cec80d238d2973ff2606f1def903162f10ec99a7984cbc996
- Size of remote file:
- 2.24 GB
- SHA256:
- 7885c86ec3e367d8050844502d36af2db5c4599f4b5a3a771a761177bc7c594d
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