Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use nikcheerla/nooks-amd-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nikcheerla/nooks-amd-detection with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nikcheerla/nooks-amd-detection") 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 nikcheerla/nooks-amd-detection with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nikcheerla/nooks-amd-detection") model = AutoModel.from_pretrained("nikcheerla/nooks-amd-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nikcheerla/nooks-amd-detection: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/nikcheerla/nooks-amd-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nikcheerla/nooks-amd-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nikcheerla/nooks-amd-detection/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 56bece22d5f325fd36aaf4119e51b8234085fd0473c07a22a571944e5d6cb213
- Size of remote file:
- 438 MB
- SHA256:
- b0f7d7d13c0121e3630d94243dc04967e423b0109315376e79010837a91ba64e
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