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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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+ - t5
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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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+ # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
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+ ## Introduction
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+ ptt5-base-msmarco-en-pt-10k is a T5-based model pretrained in the BrWac corpus, finetuned on both English and Portuguese translated version of MS MARCO passage dataset. This model was finetuned for 10k steps.
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+ 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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+
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+ ## Usage
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+ ```python
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+
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+ from transformers import T5Tokenizer, T5ForConditionalGeneration
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+
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+ model_name = 'unicamp-dl/ptt5-base-msmarco-en-pt-10k'
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+ tokenizer = T5Tokenizer.from_pretrained(model_name)
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+ model = T5ForConditionalGeneration.from_pretrained(model_name)
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+
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+ ```
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+ # Citation
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+ If you use ptt5-base-msmarco-en-pt-10k, 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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+ {
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+ "_name_or_path": "mptt5-base-msmarco-en-pt-10k/",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 3072,
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+ "d_kv": 64,
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+ "d_model": 768,
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+ "decoder_start_token_id": 0,
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "relu",
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+ "gradient_checkpointing": false,
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 12,
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+ "num_heads": 12,
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+ "num_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ "length_penalty": 2.0,
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+ "max_length": 200,
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+ "min_length": 30,
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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 4,
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+ "prefix": "summarize: "
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "transformers_version": "4.8.2",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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