Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use omarelsayeed/QA_Search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use omarelsayeed/QA_Search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omarelsayeed/QA_Search") 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 omarelsayeed/QA_Search with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/QA_Search") model = AutoModel.from_pretrained("omarelsayeed/QA_Search", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omarelsayeed/QA_Search: direct link, hf CLI and curl.
- Browser
- Download file 46.2 MB
-
https://huggingface.co/omarelsayeed/QA_Search/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omarelsayeed/QA_Search/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omarelsayeed/QA_Search/resolve/main/pytorch_model.bin
46.2 MB
- Xet hash:
- 6a53d7ef77dddbf8a847acce9e3c950ff20d964ccdbf58806e3e3bde936cb1bb
- Size of remote file:
- 46.2 MB
- SHA256:
- 66c29c933c48ded4cae655419fc8525f2c45baa6e3003041d98ad1a4e19f79b3
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