Adding v2

README.md

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+---
+language: pt
+license: mit
+tags:
+- msmarco
+- t5
+- pytorch
+- tensorflow
+- pt
+- pt-br
+datasets:
+- msmarco
+widget:
+- text: "Texto de exemplo em português"
+inference: false
+---
+# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
+## Introduction
+ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and 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
+```python
+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_name = 'unicamp-dl/ptt5-base-msmarco-en-pt-100k-v2'
+tokenizer  = T5Tokenizer.from_pretrained(model_name)
+model      = T5ForConditionalGeneration.from_pretrained(model_name)
+
+```
+# Citation
+If you use ptt5-base-msmarco-en-pt-100k-v2, please cite:
+
+    @misc{bonifacio2021mmarco,
+      title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, 
+      author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and  and Roberto Lotufo and Rodrigo Nogueira},
+      year={2021},
+      eprint={2108.13897},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+

config.json

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+{
+  "_name_or_path": "temp/",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.12.5",
+  "use_cache": true,
+  "vocab_size": 32128
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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