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#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:3
msgid "贡献预训练模型权重"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:6
msgid "1. 模型网络结构类型"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:7
msgid ""
"PaddleNLP目前已支持绝大多数主流的预训练模型网络结构，既包括百度自研的预训练模型（如ERNIE系列）， "
"也涵盖业界主流的预训练模型（如BERT，ALBERT，GPT，RoBERTa，XLNet等）。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:10
msgid ""
"PaddleNLP目前支持的预训练模型结构类型汇总可见 `Transformer预训练模型汇总 "
"<https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers.html>`_"
" （持续增加中，也非常欢迎进行新模型贡献：`如何贡献新模型 "
"<https://paddlenlp.readthedocs.io/zh/latest/community/contribute_models/contribute_new_models.html>`_"
" ）。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:15
msgid "2. 模型参数权重类型"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:16
msgid "非常欢迎大家贡献优质模型参数权重。 参数权重类型包括但不限于（以BERT模型网络为例）："
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:19
msgid ""
"PaddleNLP还未收录的BERT预训练模型参数权重 （如 `bert-base-japanese-char "
"<https://huggingface.co/cl-tohoku/bert-base-japanese-char>`_ ，`danish-"
"bert-botxo <https://huggingface.co/Maltehb/danish-bert-botxo>`_ 等）；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:21
msgid ""
"BERT模型在其他垂类领域（如数学，金融，法律，医学等）的预训练模型参数权重 （如 `MathBERT "
"<https://huggingface.co/tbs17/MathBERT>`_ ，`finbert "
"<https://huggingface.co/ProsusAI/finbert>`_ 等）；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:23
msgid ""
"基于BERT在下游具体任务进行fine-tuning后的模型参数权重 （如 `bert-base-multilingual-uncased-"
"sentiment <https://huggingface.co/nlptown/bert-base-multilingual-uncased-"
"sentiment>`_ ， `bert-base-NER <https://huggingface.co/dslim/bert-base-"
"NER>`_ 等）；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:26
msgid "其他模型参数权重（任何你觉得有价值的模型参数权重）；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:29
msgid "3. 参数权重格式转换"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:30
msgid ""
"当我们想要贡献github上开源的某模型权重时，但是发现该权重保存为其他的深度学习框架（PyTorch，TensorFlow等）的格式， "
"这就需要我们进行不同深度学习框架间的模型格式转换，下面的链接给出了一份详细的关于Pytorch到Paddle模型格式转换的教程： "
"`Pytorch到Paddle模型格式转换文档 <./convert_pytorch_to_paddle.rst>`_ 。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:35
msgid "4. 进行贡献"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:37
msgid "4.1 准备权重相关文件"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:38
msgid ""
"一般来说，我们需要准备 **model_state.pdparams** "
"，**vocab.txt**，**tokenizer_config.json** 以及 **model_config.json** "
"这四个文件进行参数权重贡献。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:41
msgid "model_state.pdparams 文件可以通过上述的参数权重格式转换过程得到；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:42
msgid ""
"vocab.txt "
"文件可以直接使用原始模型对应的vocab文件（根据模型对应tokenizer类型的不同，该文件名可能为spiece.model等）；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:43
msgid ""
"model_config.json 文件可以参考对应 model.save_pretrained() "
"接口保存的model_config.json文件；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:44
msgid ""
"tokenizer_config.json 文件可以参考对应 tokenizer.save_pretrained() "
"接口保存的model_config.json文件；"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:47
msgid "4.2 创建个人目录"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:48
msgid ""
"如果你是首次进行权重贡献，那么你需要在 ``PaddleNLP/community/`` 下新建一个目录。 "
"目录名称使用你的github名称，比如新建目录 ``PaddleNLP/community/yingyibiao/`` 。 "
"如果已有个人目录，则可以跳过此步骤。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:53
msgid "4.3 创建权重目录"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:54
msgid ""
"在步骤4.2的个人目录下新建一个权重目录，权重目录名为本次贡献的模型权重名称。 比如我想贡献 ``bert-base-uncased-"
"sst-2-finetuned`` 这个模型， 则新建权重目录 ``PaddleNLP/community/yingyibiao/bert-"
"base-uncased-sst-2-finetuned/`` 。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:59
msgid "4.4 在权重目录下添加PR（pull request）相关文件"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:60
msgid "在步骤4.3的目录下加入两个文件，分别为 ``README.md`` 和 ``files.json`` 。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:62
msgid "``README.md`` 是对你贡献的权重的详细介绍，使用示例，权重来源等。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:63
msgid ""
"``files.json`` 为步骤4.1所得的权重相关文件以及对应地址。files.json文件内容示例如下，只需将地址中的 "
"*yingyibiao* 和 *bert-base-uncased-sst-2-finetuned* 分别更改为你的github用户名和权重名称。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:76
msgid "4.5 在github上提PR进行贡献"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:77
msgid ""
"第一次进行开源贡献的同学可以参考 `first-contributions "
"<https://github.com/firstcontributions/first-contributions>`_ 。"
msgstr ""

#: ../community/contribute_models/contribute_awesome_pretrained_models.rst:78
msgid "模型权重贡献PR示例请参考 `bert-base-uncased-sst-2-finetuned PR <.>`_ 。"
msgstr ""

