# mt5-small-german-query-generation 

## Model description:
This model was created with the purpose to generate possible queries for a german input article.

For this model, we finetuned a multilingual T5 model [mt5-small](https://huggingface.co/google/mt5-small) on the [MMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco) the machine translated version of the MS MARCO dataset. 


The model was trained for 1 epoch, on 200,000 unique queries of the dataset. We trained the model on one K80 GPU for 25,000 iterations with following parameters:
  - learning rate: 1e-3
  - train batch size: 8
  - max input sequence length: 512
  - max target sequence length: 64


## Model Performance:

Model evaluation was done on 2000 evaluation paragraphs of the dataset. Mean [f1 ROUGE scores](https://github.com/pltrdy/rouge) were calculated for the model. 

| Rouge-1 | Rouge-2 | Rouge-L |
|---|---|---|
|0.162 | 0.052 | 0.161 |

{"language": ["de"], "license": "apache-2.0", "tags": ["pytorch", "query-generation"], "metrics": ["Rouge-Score"], "widget": [{"text": "Das Lama (Lama glama) ist eine Art der Kamele. Es ist in den s\u00fcdamerikanischen Anden verbreitet und eine vom Guanako abstammende Haustierform.", "example_title": "Article 1"}]}
transformers pytorch mt5 text2text-generation query-generation de license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #query-generation #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-german-query-generation
=================================


Model description:
------------------


This model was created with the purpose to generate possible queries for a german input article.


For this model, we finetuned a multilingual T5 model mt5-small on the MMARCO dataset the machine translated version of the MS MARCO dataset.


The model was trained for 1 epoch, on 200,000 unique queries of the dataset. We trained the model on one K80 GPU for 25,000 iterations with following parameters:


* learning rate: 1e-3
* train batch size: 8
* max input sequence length: 512
* max target sequence length: 64


Model Performance:
------------------


Model evaluation was done on 2000 evaluation paragraphs of the dataset. Mean f1 ROUGE scores were calculated for the model.


Rouge-1: 0.162, Rouge-2: 0.052, Rouge-L: 0.161



TAGS
#transformers #pytorch #mt5 #text2text-generation #query-generation #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #query-generation #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
