Adding v2

README.md

@@ -16,7 +16,7 @@ inference: false
 ---
 # PTT5-base Reranker finetuned on Portuguese MS MARCO
 ## Introduction
-ptt5-base-msmarco-pt-10k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 10k steps. 
+ptt5-base-msmarco-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. 
 Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository.
 
 ## Usage
@@ -24,13 +24,13 @@ Further information about the dataset or the translation method can be found on
 
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-model_name = 'unicamp-dl/ptt5-base-msmarco-pt-10k-v2'
+model_name = 'unicamp-dl/ptt5-base-msmarco-pt-100k-v2'
 tokenizer  = T5Tokenizer.from_pretrained(model_name)
 model      = T5ForConditionalGeneration.from_pretrained(model_name)
 
 ```
 # Citation
-If you use ptt5-base-msmarco-pt-10k-v2, please cite:
+If you use ptt5-base-msmarco-pt-100k-v2, please cite:
 
     @misc{bonifacio2021mmarco,
       title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
@@ -41,3 +41,4 @@ If you use ptt5-base-msmarco-pt-10k-v2, please cite:
       primaryClass={cs.CL}
 }
 
+