Sentence Similarity
sentence-transformers
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use ohsuz/k-finance-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ohsuz/k-finance-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ohsuz/k-finance-sentence-transformer") 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] - Transformers
How to use ohsuz/k-finance-sentence-transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ohsuz/k-finance-sentence-transformer") model = AutoModel.from_pretrained("ohsuz/k-finance-sentence-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b82608bc2484b22d1b598c8f1a48ca7c0d664a2004010840d7a6f6a22e27942b
- Size of remote file:
- 442 MB
- SHA256:
- f04016f7eca1e244071230317b8c2b5b4a54ed9540eec314937bd7ff0c8444a5
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