Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Ngit/fail-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ngit/fail-detect with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ngit/fail-detect") 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 Ngit/fail-detect with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ngit/fail-detect") model = AutoModel.from_pretrained("Ngit/fail-detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6593644aba98bbd40233dc368ad6207726e1d9dcf9f51321b476a17c73e7a94f
- Size of remote file:
- 438 MB
- SHA256:
- 7c29bb7efdcb2744c9ea59a1cd103d3311c3d24e946073bb154b7a5054ef8164
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