Feature Extraction
Transformers
Safetensors
sentence-transformers
English
nvembed
mteb
custom_code
Eval Results (legacy)
Instructions to use nvidia/NV-Embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NV-Embed-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/NV-Embed-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/NV-Embed-v2", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use nvidia/NV-Embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-Embed-v2", trust_remote_code=True) 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
| { | |
| "ClimateFEVER": | |
| { | |
| "query": "Given a claim about climate change, retrieve documents that support or refute the claim", | |
| "corpus": "" | |
| }, | |
| "HotpotQA": | |
| { | |
| "query": "Given a multi-hop question, retrieve documents that can help answer the question", | |
| "corpus": "" | |
| }, | |
| "FEVER": | |
| { | |
| "query": "Given a claim, retrieve documents that support or refute the claim", | |
| "corpus": "" | |
| }, | |
| "MSMARCO": | |
| { | |
| "query": "Given a web search query, retrieve relevant passages that answer the query", | |
| "corpus": "" | |
| }, | |
| "DBPedia": | |
| { | |
| "query": "Given a query, retrieve relevant entity descriptions from DBPedia", | |
| "corpus": "" | |
| }, | |
| "NQ": | |
| { | |
| "query": "Given a question, retrieve passages that answer the question", | |
| "corpus": "" | |
| }, | |
| "QuoraRetrieval": | |
| { | |
| "query": "Given a question, retrieve questions that are semantically equivalent to the given question", | |
| "corpus": "Given a question, retrieve questions that are semantically equivalent to the given question" | |
| }, | |
| "SCIDOCS": | |
| { | |
| "query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper", | |
| "corpus": "" | |
| }, | |
| "TRECCOVID": | |
| { | |
| "query": "Given a query on COVID-19, retrieve documents that answer the query", | |
| "corpus": "" | |
| }, | |
| "Touche2020": | |
| { | |
| "query": "Given a question, retrieve passages that answer the question", | |
| "corpus": "" | |
| }, | |
| "SciFact": | |
| { | |
| "query": "Given a scientific claim, retrieve documents that support or refute the claim", | |
| "corpus": "" | |
| }, | |
| "NFCorpus": | |
| { | |
| "query": "Given a question, retrieve relevant documents that answer the question", | |
| "corpus": "" | |
| }, | |
| "ArguAna": | |
| { | |
| "query": "Given a claim, retrieve documents that support or refute the claim", | |
| "corpus": "" | |
| }, | |
| "FiQA2018": | |
| { | |
| "query": "Given a financial question, retrieve relevant passages that answer the query", | |
| "corpus": "" | |
| }, | |
| "STS": | |
| { | |
| "text": "Retrieve semantically similar text" | |
| }, | |
| "SUMM": | |
| { | |
| "text": "Given a news summary, retrieve other semantically similar summaries" | |
| } | |
| } |