# BERT
**\*\*\*\*\* New November 3rd, 2018: Multilingual and Chinese models avalable
\*\*\*\*\***
We have made two new BERT models available:
* **[`BERT-Base, Multilingual`](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip)**:
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
We use character-based tokenization for Chinese, and WordPiece tokenization for
all other languages. Both models should work out-of-the-box without any code
changes. We did update the implementation of `BasicTokenizer` in
`tokenization.py` to support Chinese character tokenization, so please update if
you forked it. However, we did not change the tokenization API.
For more, see the
[Multilingual README](https://github.com/google-research/bert/blob/master/multilingual.md).
**\*\*\*\*\* End new information \*\*\*\*\***
## Introduction
**BERT**, or **B**idirectional **E**ncoder **R**epresentations from
**T**ransformers, is a new method of pre-training language representations which
obtains state-of-the-art results on a wide array of Natural Language Processing
(NLP) tasks.
Our academic paper which describes BERT in detail and provides full results on a
number of tasks can be found here:
[https://arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805).
To give a few numbers, here are the results on the
[SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/) question answering
task:
SQuAD v1.1 Leaderboard (Oct 8th 2018) | Test EM | Test F1
------------------------------------- | :------: | :------:
1st Place Ensemble - BERT | **87.4** | **93.2**
2nd Place Ensemble - nlnet | 86.0 | 91.7
1st Place Single Model - BERT | **85.1** | **91.8**
2nd Place Single Model - nlnet | 83.5 | 90.1
And several natural language inference tasks:
System | MultiNLI | Question NLI | SWAG
----------------------- | :------: | :----------: | :------:
BERT | **86.7** | **91.1** | **86.3**
OpenAI GPT (Prev. SOTA) | 82.2 | 88.1 | 75.0
Plus many other tasks.
Moreover, these results were all obtained with almost no task-specific neural
network architecture design.
If you already know what BERT is and you just want to get started, you can
[download the pre-trained models](#pre-trained-models) and
[run a state-of-the-art fine-tuning](#fine-tuning-with-bert) in only a few
minutes.
## What is BERT?
BERT is method of pre-training language representations, meaning that we train a
general-purpose "language understanding" model on a large text corpus (like
Wikipedia), and then use that model for downstream NLP tasks that we care about
(like question answering). BERT outperforms previous methods because it is the
first *unsupervised*, *deeply bidirectional* system for pre-training NLP.
*Unsupervised* means that BERT was trained using only a plain text corpus, which
is important because an enormous amount of plain text data is publicly available
on the web in many languages.
Pre-trained representations can also either be *context-free* or *contextual*,
and contextual representations can further be *unidirectional* or
*bidirectional*. Context-free models such as
[word2vec](https://www.tensorflow.org/tutorials/representation/word2vec) or
[GloVe](https://nlp.stanford.edu/projects/glove/) generate a single "word
embedding" representation for each word in the vocabulary, so `bank` would have
the same representation in `bank deposit` and `river bank`. Contextual models
instead generate a representation of each word that is based on the other words
in the sentence.
BERT was built upon recent work in pre-training contextual representations —
including [Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432),
[Generative Pre-Training](https://blog.openai.com/language-unsupervised/),
[ELMo](https://allennlp.org/elmo), and
[ULMFit](http://nlp.fast.ai/classification/2018/05/15/introducting-ulmfit.html)
— but crucially these models are all *unidirectional* or *shallowly
bidirectional*. This means that each word is only contextualized using the words
to its left (or right). For example, in the sentence `I made a bank deposit` the
unidirectional representation of `bank` is only based on `I made a` but not
`deposit`. Some previous work does combine the representations from separate
left-context and right-context models, but only in a "shallow" manner. BERT
represents "bank" using both its left and right context — `I made a ... deposit`
— starting from the very bottom of a deep neural network, so it is *deeply
bidirectional*.
BERT uses a simple approach for this: We mask out 15% of the words in the input,
run the entire sequence through a deep bidirectional
[Transformer](https://arxiv.org/abs/1706.03762) encoder, and then predict only
the masked words. For example:
```
Input: the man went to the [MASK1] . he bought a [MASK2] of milk.
Labels: [MASK1] = store; [MASK2] = gallon
```
In order to learn relationships between sentences, we also train on a simple
task which can be generated from any monolingual corpus: Given two sentences `A`
and `B`, is `B` the actual next sentence that comes after `A`, or just a random
sentence from the corpus?
```
Sentence A: the man went to the store .
Sentence B: he bought a gallon of milk .
Label: IsNextSentence
```
```
Sentence A: the man went to the store .
Sentence B: penguins are flightless .
Label: NotNextSentence
```
We then train a large model (12-layer to 24-layer Transformer) on a large corpus
(Wikipedia + [BookCorpus](http://yknzhu.wixsite.com/mbweb)) for a long time (1M
update steps), and that's BERT.
Using BERT has two stages: *Pre-training* and *fine-tuning*.
**Pre-training** is fairly expensive (four days on 4 to 16 Cloud TPUs), but is a
one-time procedure for each language (current models are English-only, but
multilingual models will be released in the near future). We are releasing a
number of pre-trained models from the paper which were pre-trained at Google.
Most NLP researchers will never need to pre-train their own model from scratch.
**Fine-tuning** is inexpensive. All of the results in the paper can be
replicated in at most 1 hour on a single Cloud TPU, or a few hours on a GPU,
starting from the exact same pre-trained model. SQuAD, for example, can be
trained in around 30 minutes on a single Cloud TPU to achieve a Dev F1 score of
91.0%, which is the single system state-of-the-art.
The other important aspect of BERT is that it can be adapted to many types of
NLP tasks very easily. In the paper, we demonstrate state-of-the-art results on
sentence-level (e.g., SST-2), sentence-pair-level (e.g., MultiNLI), word-level
(e.g., NER), and span-level (e.g., SQuAD) tasks with almost no task-specific
modifications.
## What has been released in this repository?
We are releasing the following:
* TensorFlow code for the BERT model architecture (which is mostly a standard
[Transformer](https://arxiv.org/abs/1706.03762) architecture).
* Pre-trained checkpoints for both the lowercase and cased version of
`BERT-Base` and `BERT-Large` from the paper.
* TensorFlow code for push-button replication of the most important
fine-tuning experiments from the paper, including SQuAD, MultiNLI, and MRPC.
All of the code in this repository works out-of-the-box with CPU, GPU, and Cloud
TPU.
## Pre-trained models
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 tha
没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
收起资源包目录
深度之眼比赛总结整理资料 (2055个子文件)
_stats.c 922KB
glove.c 25KB
cooccur.c 19KB
shuffle.c 8KB
vocab_count.c 7KB
common.c 5KB
.DS_Store 14KB
.DS_Store 14KB
.DS_Store 10KB
.DS_Store 10KB
.DS_Store 10KB
.DS_Store 10KB
.DS_Store 8KB
.DS_Store 8KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
normalization_rule.h 6.87MB
sentencepiece_model.pb.h 185KB
unicode_script_map.h 104KB
repeated_field.h 99KB
descriptor.h 94KB
wire_format_lite.h 82KB
extension_set.h 77KB
coded_stream.h 68KB
darts.h 51KB
map.h 47KB
sentencepiece.pb.h 40KB
strutil.h 39KB
parse_context.h 32KB
map_type_handler.h 31KB
generated_message_table_driven_lite.h 31KB
map_util.h 30KB
arena.h 29KB
message_lite.h 27KB
map_entry_lite.h 24KB
string_view.h 20KB
sentencepiece_processor.h 19KB
arena_impl.h 18KB
stringpiece.h 17KB
callback.h 17KB
zero_copy_stream_impl_lite.h 17KB
arenastring.h 16KB
unknown_field_set.h 14KB
port.h 12KB
zero_copy_stream_impl.h 12KB
generated_message_table_driven.h 12KB
extension_set_inl.h 12KB
int128.h 12KB
bytestream.h 11KB
util.h 11KB
spec_parser.h 10KB
zero_copy_stream.h 10KB
generated_message_util.h 10KB
model_interface.h 9KB
logging.h 9KB
共 2055 条
- 1
- 2
- 3
- 4
- 5
- 6
- 21
资源评论
宣晨光
- 粉丝: 1249
- 资源: 6
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功