Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use Parveshiiii/Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Parveshiiii/Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Parveshiiii/Embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: | |
| - FacebookAI/xlm-roberta-large | |
| library_name: sentence-transformers | |
| tags: | |
| - sentence-transformers | |
| - sentence-similarity | |
| - feature-extraction | |
| - text-embeddings-inference | |
| This is a converted XLM‑RoBERTa large model using CLS pooling for embeddings. It’s not trained yet, but it can be effectively trained to make a cool embedding model | |