Initial commit

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 :brazil: MS MARCO
+## Introduction
+ptt5-base-msmarco-pt-10k is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. This model was finetuned for 10k steps. 
+Further information about the dataset or the translation method can be found on our [Cross-Lingual repository](https://github.com/unicamp-dl/cross-lingual-analysis).
+
+## Usage
+```python
+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_name = 'unicamp-dl/ptt5-base-msmarco-pt-10k'
+tokenizer  = T5Tokenizer.from_pretrained(model_name)
+model      = T5ForConditionalGeneration.from_pretrained(model_name)
+
+```
+# Citation
+If you use ptt5-base-msmarco-pt-10k, please cite:
+    @article{rosa2021cost,
+    title={A cost-benefit analysis of cross-lingual transfer methods},
+    author={Rosa, Guilherme Moraes and Bonifacio, Luiz Henrique and de Souza, Leandro Rodrigues and Lotufo, Roberto and Nogueira, Rodrigo},
+    journal={arXiv preprint arXiv:2105.06813},
+    year={2021}
+}

config.json

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+{
+  "_name_or_path": "../ptt5-base-msmarco-pt-10k/",
+  "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",
+  "gradient_checkpointing": false,
+  "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,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.8.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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special_tokens_map.json

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