Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:500
loss:MarginDistillationLoss
text-embeddings-inference
Instructions to use CrazyDave53/OpenCVModelDemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CrazyDave53/OpenCVModelDemo with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CrazyDave53/OpenCVModelDemo") sentences = [ "qualify leads job description", "job description Qualify leads – can the customers use our solutions and do they have budget", "job description QUALIFICATION STANDARDS:", "job description Has passion and convictions and the innate ability to inspire passion in others" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7faf96693dea3cf5f972faed4ccbf6719a27d83ccfd30233edee06494b65657a
- Size of remote file:
- 1.11 GB
- SHA256:
- 7e7266d172acca6f10502e45b408dcc21dc6afb4446e2af4cdbf22d83656c328
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