ken11
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Browse files- README.md +46 -0
- config.json +54 -0
- pytorch_model.bin +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
README.md
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---
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tags:
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- translation
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- japanese
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language:
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- ja
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- en
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license: mit
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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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## 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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## 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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tokenizer = MBartTokenizer.from_pretrained("ken11/mbart-ja-en")
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model = MBartForConditionalGeneration.from_pretrained("ken11/mbart-ja-en")
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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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## 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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## Tokenizer
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The tokenizer uses the [sentencepiece](https://github.com/google/sentencepiece) trained on the JESC dataset.
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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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## Licenese
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[The MIT license](https://opensource.org/licenses/MIT)
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config.json
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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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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:4e00cd3e783c9386041a78e2cdc410f75308449a1289ea001e7263751c4ec091
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size 1681999289
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:fca26417c3321e76f2243b15ce126c239ce564c6ffb8e5a1bccb18c53ffbaad5
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size 1427810
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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}, "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"]}
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tokenizer_config.json
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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"}
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