Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use omarelsayeed/first_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use omarelsayeed/first_qa_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omarelsayeed/first_qa_model") 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/first_qa_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/first_qa_model") model = AutoModel.from_pretrained("omarelsayeed/first_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9222adf3d167023251465d34ed588aa0ddec60338618b09871faa2d1b58a562
- Size of remote file:
- 46.2 MB
- SHA256:
- 3ac2d363686c7dc812a64ad0afdaf2b3f2e12ec7bf6d7f2b6895a46618537dbe
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