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:
# pip install -U transformers accelerate # 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
Download tokenizer.json from Ngit/fail-detect: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/Ngit/fail-detect/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://Ngit/fail-detect@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Ngit/fail-detect/resolve/refs%2Fpr%2F1/tokenizer.json
712 kB
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