# dragon-multiturn-context-encoder by NVIDIA: Open Model Source: https://savrn.com/models/dragon-multiturn-context-encoder Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Model Card We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the Dragon retriever. The details of Dragon-multiturn can be found in here. Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the context encoder of Dragon-multiturn for getting the context embeddings, and you also need the query encoder to get query embeddings, which can be found here. Both query encoder and context encoder share the same tokenizer. Retrieval results across five multi-turn QA… Excerpt from the card by NVIDIA, licensed other. ## Configuration Architecture BertModel Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 30,522 Stored precision float32 Model type bert ## Identity and Version Repository nvidia/dragon-multiturn-context-encoder Publisher NVIDIA Task Feature extraction Modality Text Library transformers Parameters Not stated by the source Languages en Revision 14ba90bb7472741e2a12ac4c0455110966c3f69d First published 2024-04-30 Last updated 2024-05-24 ## Files and Weights 7 files, 438.2 MB in total. The weights are 1 file totalling 438.0 MB in bin. Weights1 file · 438.0 MB Configuration2 files · 789 B Tokenizer2 files · 231.5 KB Documentation1 file · 9.0 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | pytorch_model.bin | Weights | 438.0 MB | dfae6270ccce | | config.json | Configuration | 677 B | — | | special_tokens_map.json | Configuration | 112 B | — | | README.md | Documentation | 9.0 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer_config.json | Tokenizer | 28 B | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License other Access Open weights, no gate Download size 438.0 MB [Download from NVIDIA](https://huggingface.co/nvidia/dragon-multiturn-context-encoder) Released by NVIDIA through its official repository on Hugging Face. ## Built From - Described by arXiv:2302.07452 - Described by arXiv:2401.10225 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 438.0 MB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About dragon-multiturn-context-encoder ### What license is dragon-multiturn-context-encoder released under? other, as its publisher declares it. Read the license text before commercial use. ### What is dragon-multiturn-context-encoder's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Feature extraction ### [all-MiniLM-L6-v2](https://savrn.com/models/all-minilm-l6-v2-2) [Joshua](https://savrn.com/model-publishers/xenova) https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings like this: You can convert this Tensor to a nested JavaScript array using.tolist(): Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. 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