Instructions to use nvidia/dragon-multiturn-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/dragon-multiturn-query-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/dragon-multiturn-query-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nvidia/dragon-multiturn-query-encoder") model = AutoModel.from_pretrained("nvidia/dragon-multiturn-query-encoder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
|
Download evaluation/README.md from nvidia/dragon-multiturn-query-encoder: direct link, hf CLI and curl.
- Browser
- Download file 398 Bytes
-
https://huggingface.co/nvidia/dragon-multiturn-query-encoder/resolve/refs%2Fpr%2F6/evaluation/README.md
- Command line
-
hf download hf://nvidia/dragon-multiturn-query-encoder@refs/pr/6/evaluation/README.md
-
curl -L -o README.md https://huggingface.co/nvidia/dragon-multiturn-query-encoder/resolve/refs%2Fpr%2F6/evaluation/README.md
398 Bytes
Commands for running evaluation
python evaluate.py --eval-dataset doc2dial
python evaluate.py --eval-dataset quac
python evaluate.py --eval-dataset qrecc
For the evaluations of topiocqa and inscit, the Wikipedia corpora need to be downloaded from their original repositories.
- topiocqa: https://github.com/McGill-NLP/topiocqa
- inscit: https://github.com/ellenmellon/INSCIT