Initial commit

README.md

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+---
+language: pt
+license: mit
+tags:
+- msmarco
+- miniLM
+- pytorch
+- tensorflow
+- pt
+- pt-br
+datasets:
+- msmarco
+widget:
+- text: "Texto de exemplo em português"
+inference: false
+---
+# multilingual-MiniLM-L6-v2-en-pt-msmarco Reranker finetuned on mMARCO
+## Introduction
+multilingual-MiniLM-L6-v2-en-pt-msmarco is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version.
+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 AutoTokenizer, AutoModel
+
+model_name = 'unicamp-dl/multilingual-MiniLM-L6-v2-en-pt-msmarco'
+tokenizer  = AutoTokenizer.from_pretrained(model_name)
+model      = AutoModel.from_pretrained(model_name)
+
+```
+# Citation
+If you use mt5-base-en-pt-msmarco, 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": "mMiniLM-L6-H384-distilled-from-XLMR-Large/",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
+  "transformers_version": "4.8.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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+size 428023405

sentencepiece.bpe.model

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}

tokenizer_config.json

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+{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "special_tokens_map_file": null, "name_or_path": "mMiniLM-L6-H384-distilled-from-XLMR-Large/", "sp_model_kwargs": {}, "tokenizer_class": "XLMRobertaTokenizer"}