预先训练的模型
==============

We are releasing the ``BERT-Base`` and ``BERT-Large`` models from the
paper. ``Uncased`` means that the text has been lowercased before
WordPiece tokenization, e.g., ``John Smith`` becomes ``john smith``. The
``Uncased`` model also strips out any accent markers. ``Cased`` means
that the true case and accent markers are preserved. Typically, the
``Uncased`` model is better unless you know that case information is
important for your task (e.g., Named Entity Recognition or
Part-of-Speech tagging).

These models are all released under the same license as the source code
(Apache 2.0).

For information about the Multilingual and Chinese model, see the
`Multilingual
README <https://github.com/google-research/bert/blob/master/multilingual.md>`__.

**When using a cased model, make sure to pass ``--do_lower=False`` to
the training scripts. (Or pass ``do_lower_case=False`` directly to
``FullTokenizer`` if you’re using your own script.)**

The links to the models are here (right-click, ‘Save link as…’ on the
name):

-  ```BERT-Base, Uncased`` <https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip>`__:
   12-layer, 768-hidden, 12-heads, 110M parameters
-  ```BERT-Large, Uncased`` <https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip>`__:
   24-layer, 1024-hidden, 16-heads, 340M parameters
-  ```BERT-Base, Cased`` <https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip>`__:
   12-layer, 768-hidden, 12-heads , 110M parameters
-  ```BERT-Large, Cased`` <https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip>`__:
   24-layer, 1024-hidden, 16-heads, 340M parameters
-  ```BERT-Base, Multilingual Cased (New, recommended)`` <https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip>`__:
   104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
-  ```BERT-Base, Multilingual Uncased (Orig, not recommended)`` <https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip>`__\ **(Not
   recommended, use ``Multilingual Cased`` instead)**: 102 languages,
   12-layer, 768-hidden, 12-heads, 110M parameters
-  ```BERT-Base, Chinese`` <https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip>`__:
   Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads,
   110M parameters

Each .zip file contains three items:

-  A TensorFlow checkpoint (``bert_model.ckpt``) containing the
   pre-trained weights (which is actually 3 files).
-  A vocab file (``vocab.txt``) to map WordPiece to word id.
-  A config file (``bert_config.json``) which specifies the
   hyperparameters of the model.