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README.md ADDED
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+ ---
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+ tags:
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+ - translation
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+ - japanese
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+
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+ language:
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+ - ja
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+ - en
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+
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+ license: mit
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+
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+ widget:
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+ - text: "今日もご安全に"
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+
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+ ---
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+ ## mbart-ja-en
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+ このモデルは[facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)をベースに[JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html)でファインチューニングしたものです。
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+ This model is based on [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) and fine-tuned with [JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html).
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+
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+ ## How to use
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+ ```py
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+ from transformers import (
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+ MBartForConditionalGeneration, MBartTokenizer
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+ )
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+
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+ tokenizer = MBartTokenizer.from_pretrained("ken11/mbart-ja-en")
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+ model = MBartForConditionalGeneration.from_pretrained("ken11/mbart-ja-en")
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+
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+ inputs = tokenizer("こんにちは", return_tensors="pt")
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+ translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["en_XX"], early_stopping=True, max_length=48)
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+ pred = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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+ print(pred)
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+ ```
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+
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+ ## Training Data
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+ I used the [JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html) for training.
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+ Thank you for publishing such a large dataset.
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+
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+ ## Tokenizer
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+ The tokenizer uses the [sentencepiece](https://github.com/google/sentencepiece) trained on the JESC dataset.
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+
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+ ## Note
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+ The result of evaluating the sacrebleu score for [JEC Basic Sentence Data of Kyoto University](https://nlp.ist.i.kyoto-u.ac.jp/EN/?JEC+Basic+Sentence+Data#i0163896) was `18.18` .
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+
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+ ## Licenese
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+ [The MIT license](https://opensource.org/licenses/MIT)
config.json ADDED
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+ {
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+ "_num_labels": 3,
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+ "activation_dropout": 0.0,
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+ "activation_function": "gelu",
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+ "add_bias_logits": false,
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+ "add_final_layer_norm": true,
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+ "architectures": [
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+ "MBartForConditionalGeneration"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 0,
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+ "classif_dropout": 0.0,
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+ "classifier_dropout": 0.0,
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+ "d_model": 1024,
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+ "decoder_attention_heads": 16,
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+ "decoder_ffn_dim": 4096,
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+ "decoder_layerdrop": 0.0,
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+ "decoder_layers": 12,
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+ "dropout": 0.1,
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+ "encoder_attention_heads": 16,
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+ "encoder_ffn_dim": 4096,
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+ "encoder_layerdrop": 0.0,
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+ "encoder_layers": 12,
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+ "eos_token_id": 2,
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+ "forced_eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ },
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+ "init_std": 0.02,
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+ "is_encoder_decoder": true,
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+ "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_1": 1,
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+ "LABEL_2": 2
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+ },
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+ "max_length": 1024,
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+ "max_position_embeddings": 1024,
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+ "model_type": "mbart",
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+ "normalize_before": true,
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+ "normalize_embedding": true,
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+ "num_beams": 5,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 1,
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+ "scale_embedding": true,
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+ "static_position_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.10.2",
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+ "use_cache": true,
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+ "vocab_size": 64027
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+ }
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special_tokens_map.json ADDED
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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}, "additional_special_tokens": ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]}
tokenizer_config.json ADDED
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+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "src_lang": "ja_XX", "tgt_lang": "en_XX", "additional_special_tokens": null, "max_length": 32, "max_target_length": 32, "model_max_length": 1024, "special_tokens_map_file": null, "tokenizer_class": "MBartTokenizer"}