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- Classify text with BERT - Google Colab
Classify text with BERT. This tutorial contains complete...
- Classify text with BERT - Google Colab
- Overview
- Introduction
- What is BERT?
- What has been released in this repository?
- Pre-trained models
- Fine-tuning with BERT
- Using BERT to extract fixed feature vectors (like ELMo)
- Tokenization
- Pre-training with BERT
- Using BERT in Colab
- GeneratedCaptionsTabForHeroSec
***** New March 11th, 2020: Smaller BERT Models *****
This is a release of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models.
We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.
Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.
You can download all 24 from here, or individually from the table below:
Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.
BERT, or Bidirectional Encoder Representations from Transformers, 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.
To give a few numbers, here are the results on the SQuAD v1.1 question answering task:
And several natural language inference tasks:
Plus many other tasks.
Moreover, these results were all obtained with almost no task-specific neural network architecture design.
BERT is a 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 or 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, Generative Pre-Training, ELMo, and ULMFit — 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 encoder, and then predict only the masked words. For example:
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?
We are releasing the following:
•TensorFlow code for the BERT model architecture (which is mostly a standard Transformer 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.
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.
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-Large, Uncased (Whole Word Masking): 24-layer, 1024-hidden, 16-heads, 340M parameters
Important: All results on the paper were fine-tuned on a single Cloud TPU, which has 64GB of RAM. It is currently not possible to re-produce most of the BERT-Large results on the paper using a GPU with 12GB - 16GB of RAM, because the maximum batch size that can fit in memory is too small. We are working on adding code to this repository which allows for much larger effective batch size on the GPU. See the section on out-of-memory issues for more details.
This code was tested with TensorFlow 1.11.0. It was tested with Python2 and Python3 (but more thoroughly with Python2, since this is what's used internally in Google).
In certain cases, rather than fine-tuning the entire pre-trained model end-to-end, it can be beneficial to obtained pre-trained contextual embeddings, which are fixed contextual representations of each input token generated from the hidden layers of the pre-trained model. This should also mitigate most of the out-of-memory issues.
As an example, we include the script extract_features.py which can be used like this:
This will create a JSON file (one line per line of input) containing the BERT activations from each Transformer layer specified by layers (-1 is the final hidden layer of the Transformer, etc.)
Note that this script will produce very large output files (by default, around 15kb for every input token).
If you need to maintain alignment between the original and tokenized words (for projecting training labels), see the Tokenization section below.
Note: You may see a message like Could not find trained model in model_dir: /tmp/tmpuB5g5c, running initialization to predict. This message is expected, it just means that we are using the init_from_checkpoint() API rather than the saved model API. If you don't specify a checkpoint or specify an invalid checkpoint, this script will complain.
For sentence-level tasks (or sentence-pair) tasks, tokenization is very simple. Just follow the example code in run_classifier.py and extract_features.py. The basic procedure for sentence-level tasks is:
1.Instantiate an instance of tokenizer = tokenization.FullTokenizer
2.Tokenize the raw text with tokens = tokenizer.tokenize(raw_text).
3.Truncate to the maximum sequence length. (You can use up to 512, but you probably want to use shorter if possible for memory and speed reasons.)
4.Add the [CLS] and [SEP] tokens in the right place.
Word-level and span-level tasks (e.g., SQuAD and NER) are more complex, since you need to maintain alignment between your input text and output text so that you can project your training labels. SQuAD is a particularly complex example because the input labels are character-based, and SQuAD paragraphs are often longer than our maximum sequence length. See the code in run_squad.py to show how we handle this.
We are releasing code to do "masked LM" and "next sentence prediction" on an arbitrary text corpus. Note that this is not the exact code that was used for the paper (the original code was written in C++, and had some additional complexity), but this code does generate pre-training data as described in the paper.
Here's how to run the data generation. The input is a plain text file, with one sentence per line. (It is important that these be actual sentences for the "next sentence prediction" task). Documents are delimited by empty lines. The output is a set of tf.train.Examples serialized into TFRecord file format.
You can perform sentence segmentation with an off-the-shelf NLP toolkit such as spaCy. The create_pretraining_data.py script will concatenate segments until they reach the maximum sequence length to minimize computational waste from padding (see the script for more details). However, you may want to intentionally add a slight amount of noise to your input data (e.g., randomly truncate 2% of input segments) to make it more robust to non-sentential input during fine-tuning.
This script stores all of the examples for the entire input file in memory, so for large data files you should shard the input file and call the script multiple times. (You can pass in a file glob to run_pretraining.py, e.g., tf_examples.tf_record*.)
The max_predictions_per_seq is the maximum number of masked LM predictions per sequence. You should set this to around max_seq_length * masked_lm_prob (the script doesn't do that automatically because the exact value needs to be passed to both scripts).
Here's how to run the pre-training. Do not include init_checkpoint if you are pre-training from scratch. The model configuration (including vocab size) is specified in bert_config_file. This demo code only pre-trains for a small number of steps (20), but in practice you will probably want to set num_train_steps to 10000 steps or more. The max_seq_length and max_predictions_per_seq parameters passed to run_pretraining.py must be the same as create_pretraining_data.py.
If you want to use BERT with Colab, you can get started with the notebook "BERT FineTuning with Cloud TPUs". At the time of this writing (October 31st, 2018), Colab users can access a Cloud TPU completely for free. Note: One per user, availability limited, requires a Google Cloud Platform account with storage (although storage may be purchased with...
This is the official repository for BERT, a state-of-the-art natural language processing model. It contains the code, pre-trained models, and fine-tuning examples for BERT and its variants.
BERT is a deep bidirectional transformer that pre-trains on unlabeled text and fine-tunes for various natural language processing tasks. Learn how BERT achieves state-of-the-art results on question answering, language inference and more.
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