Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use emersoftware/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use emersoftware/test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("emersoftware/test") 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 emersoftware/test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emersoftware/test") model = AutoModel.from_pretrained("emersoftware/test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba4ecded32ac66f8ffffa1a1c74195ec7544d78e4ef0b2b0bb50ccd8408f276f
- Size of remote file:
- 439 MB
- SHA256:
- 45fe023aae7ca58ce5db784e9bdd6c2af933f0eae5f17d874867be88177bd478
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