Text Classification
sentence-transformers
Safetensors
English
modernbert
ColBERT
multi-vector
PyLate
typed-decisions
zero-shot-classification
text-embeddings-inference
Instructions to use tasksource/tasksource-jev-nano-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tasksource/tasksource-jev-nano-v0 with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="tasksource/tasksource-jev-nano-v0") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tasksource/tasksource-jev-nano-v0: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/tasksource/tasksource-jev-nano-v0/resolve/main/tokenizer.json
- Command line
-
hf download hf://tasksource/tasksource-jev-nano-v0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tasksource/tasksource-jev-nano-v0/resolve/main/tokenizer.json
3.58 MB
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