Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:784827
loss:ContrastiveLoss
text-embeddings-inference
Instructions to use noystl/recomb-pred-bge-large-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use noystl/recomb-pred-bge-large-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("noystl/recomb-pred-bge-large-en") sentences = [ "Represent this sentence for searching relevant passages: Existing methods for anomaly detection on dynamic graphs struggle with capturing complex time information in graph structures and generating effective negative samples for unsupervised learning. These challenges highlight the need for improved methodologies that can address the limitations of current approaches in this field.We suggest combining 'a message-passing framework' and ", "a single global model", "videos", "sequential polygon generation" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update model card metadata and add links to project page and code repository
#1
by nielsr HF Staff - opened
This PR changes the pipeline_tag to the more appropriate text-ranking to enhance discoverability on the Hub. It also adds a link to the project page and Github repository, and links the model to the paper page at https://huggingface.co/papers/2505.20779.