Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use adit94/sentenceTest_kbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adit94/sentenceTest_kbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adit94/sentenceTest_kbert") 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 adit94/sentenceTest_kbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("adit94/sentenceTest_kbert") model = AutoModel.from_pretrained("adit94/sentenceTest_kbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2e8988e32ea689bbea3978ece9d888d1aa71749394b2f1479569b5b922ecd98
- Size of remote file:
- 438 MB
- SHA256:
- abf7ee6d9cfa7e92703abd8d5fee707ecceb83a8ef3d7638f5cc939c527f8ddc
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