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README.md ADDED
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+ ---
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+ language: pt
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+ license: mit
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+ tags:
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+ - msmarco
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+ - miniLM
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+ - pytorch
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+ - tensorflow
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+ - pt
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+ - pt-br
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+ datasets:
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+ - msmarco
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+ widget:
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+ - text: "Texto de exemplo em português"
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+ inference: false
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+ ---
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+ # multilingual-MiniLM-L6-v2-pt-msmarco Reranker finetuned on mMARCO
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+ ## Introduction
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+ multilingual-MiniLM-L6-v2-pt-msmarco is a multilingual miniLM-based model finetuned on a Portuguese trasnlated version of MS MARCO passage dataset.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).
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+ ## Usage
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+
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+ model_name = 'unicamp-dl/multilingual-MiniLM-L6-v2-pt-msmarco'
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModel.from_pretrained(model_name)
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+
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+ ```
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+ # Citation
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+ If you use mt5-base-en-pt-msmarco, please cite:
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+
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+ @article{rosa2021cost,
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+ title={A cost-benefit analysis of cross-lingual transfer methods},
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+ author={Rosa, Guilherme Moraes and Bonifacio, Luiz Henrique and de Souza, Leandro Rodrigues and Lotufo, Roberto and Nogueira, Rodrigo},
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+ journal={arXiv preprint arXiv:2105.06813},
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+ year={2021}
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+ }
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+
config.json ADDED
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+ {
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+ "_name_or_path": "mMiniLM-L6-H384-distilled-from-XLMR-Large/",
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+ "architectures": [
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+ "XLMRobertaForSequenceClassification"
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+ ],
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+ "bos_token_id": 0,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "id2label": {
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "xlm-roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
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+ "transformers_version": "4.8.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 250002
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+ }
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