doc2query
/

msmarco-14langs-mt5-base-v1

Text2Text Generation

Inference Endpoints

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nreimers commited on May 2, 2022

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@@ -8,7 +8,7 @@ widget:
 license: apache-2.0
 ---
-# doc2query/msmarco-14-langs-mt5-base-v1
 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It was trained on all 14 languages of [mMARCO dataset](https://github.com/unicamp-dl/mMARCO), i.e. you can input a passage in any of the 14 languages, and it will generate a query in the same language.
@@ -21,7 +21,7 @@ It can be used for:
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
-model_name = 'doc2query/msmarco-14-langs-mt5-base-v1'
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

 license: apache-2.0
 ---
+# doc2query/msmarco-14langs-mt5-base-v1
 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It was trained on all 14 languages of [mMARCO dataset](https://github.com/unicamp-dl/mMARCO), i.e. you can input a passage in any of the 14 languages, and it will generate a query in the same language.
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
+model_name = 'doc2query/msmarco-14langs-mt5-base-v1'
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)