# BERT (Bidirectional Encoder Representations from Transformers)

The 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.

This repository contains TensorFlow 2.x implementation for BERT.

## Contents
  * [Contents](#contents)
  * [Pre-trained Models](#pre-trained-models)
    * [Restoring from Checkpoints](#restoring-from-checkpoints)
  * [Set Up](#set-up)
  * [Process Datasets](#process-datasets)
  * [Fine-tuning with BERT](#fine-tuning-with-bert)
    * [Cloud GPUs and TPUs](#cloud-gpus-and-tpus)
    * [Sentence and Sentence-pair Classification Tasks](#sentence-and-sentence-pair-classification-tasks)
    * [SQuAD 1.1](#squad-1.1)


## Pre-trained Models

We released both checkpoints and tf.hub modules as the pretrained models for
fine-tuning. They are TF 2.x compatible and are converted from the checkpoints
released in TF 1.x official BERT repository
[google-research/bert](https://github.com/google-research/bert)
in order to keep consistent with BERT paper.


### Access to Pretrained Checkpoints

Pretrained checkpoints can be found in the following links:

**Note: We have switched BERT implementation
to use Keras functional-style networks in [nlp/modeling](../modeling).
The new checkpoints are:**

*   **[`BERT-Large, Uncased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/wwm_uncased_L-24_H-1024_A-16.tar.gz)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Large, Cased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/wwm_cased_L-24_H-1024_A-16.tar.gz)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Base, Uncased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12.tar.gz)**:
    12-layer, 768-hidden, 12-heads, 110M parameters
*   **[`BERT-Large, Uncased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16.tar.gz)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Base, Cased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/cased_L-12_H-768_A-12.tar.gz)**:
    12-layer, 768-hidden, 12-heads , 110M parameters
*   **[`BERT-Large, Cased`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/keras_bert/cased_L-24_H-1024_A-16.tar.gz)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters

We recommend to host checkpoints on Google Cloud storage buckets when you use
Cloud GPU/TPU.

### Restoring from Checkpoints

`tf.train.Checkpoint` is used to manage model checkpoints in TF 2. To restore
weights from provided pre-trained checkpoints, you can use the following code:

```python
init_checkpoint='the pretrained model checkpoint path.'
model=tf.keras.Model() # Bert pre-trained model as feature extractor.
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(init_checkpoint)
```

Checkpoints featuring native serialized Keras models
(i.e. model.load()/load_weights()) will be available soon.

### Access to Pretrained hub modules.

Pretrained tf.hub modules in TF 2.x SavedModel format can be found in the
following links:

*   **[`BERT-Large, Uncased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_uncased_L-24_H-1024_A-16/1)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Large, Cased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_cased_L-24_H-1024_A-16/1)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Base, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1)**:
    12-layer, 768-hidden, 12-heads, 110M parameters
*   **[`BERT-Large, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Base, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/1)**:
    12-layer, 768-hidden, 12-heads , 110M parameters
*   **[`BERT-Large, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-24_H-1024_A-16/1)**:
    24-layer, 1024-hidden, 16-heads, 340M parameters
*   **[`BERT-Base, Multilingual Cased`](https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/1)**:
    104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
*   **[`BERT-Base, Chinese`](https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/1)**:
    Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads,
    110M parameters

## Set Up

```shell
export PYTHONPATH="$PYTHONPATH:/path/to/models"
```

Install `tf-nightly` to get latest updates:

```shell
pip install tf-nightly-gpu
```

With TPU, GPU support is not necessary. First, you need to create a `tf-nightly`
TPU with [ctpu tool](https://github.com/tensorflow/tpu/tree/master/tools/ctpu):

```shell
ctpu up -name <instance name> --tf-version=”nightly”
```

Second, you need to install TF 2 `tf-nightly` on your VM:

```shell
pip install tf-nightly
```

Warning: More details TPU-specific set-up instructions and tutorial should come
along with official TF 2.x release for TPU. Note that this repo is not
officially supported by Google Cloud TPU team yet until TF 2.1 released.

## Process Datasets

### Pre-training

There is no change to generate pre-training data. Please use the script
[`../data/create_pretraining_data.py`](../data/create_pretraining_data.py)
which is essentially branched from [BERT research repo](https://github.com/google-research/bert)
to get processed pre-training data and it adapts to TF2 symbols and python3
compatibility.


### Fine-tuning

To prepare the fine-tuning data for final model training, use the
[`../data/create_finetuning_data.py`](../data/create_finetuning_data.py) script.
Resulting datasets in `tf_record` format and training meta data should be later
passed to training or evaluation scripts. The task-specific arguments are
described in following sections:

* GLUE

Users can download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.

```shell
export GLUE_DIR=~/glue
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16

export TASK_NAME=MNLI
export OUTPUT_DIR=gs://some_bucket/datasets
python ../data/create_finetuning_data.py \
 --input_data_dir=${GLUE_DIR}/${TASK_NAME}/ \
 --vocab_file=${BERT_DIR}/vocab.txt \
 --train_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_train.tf_record \
 --eval_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_eval.tf_record \
 --meta_data_file_path=${OUTPUT_DIR}/${TASK_NAME}_meta_data \
 --fine_tuning_task_type=classification --max_seq_length=128 \
 --classification_task_name=${TASK_NAME}
```

* SQUAD

The [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/) contains
detailed information about the SQuAD datasets and evaluation.

The necessary files can be found here:

*   [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
*   [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
*   [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
*   [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
*   [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
*   [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)

```shell
export SQUAD_DIR=~/squad
export SQUAD_VERSION=v1.1
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export OUTPUT_DIR=gs://some_bucket/datasets

python ../data/create_finetuning_data.py \
 --squad_data_file=${SQUAD_DIR}/train-${SQUAD_VERSION}.json \
 --vocab_file=${BERT_DIR}/vocab.txt \
 --train_data_output_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
 --meta_data_file_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_meta_data \
 --fine_tuning_task_type=squad --max_seq_length=384
```

## Fine-tuning with BERT

### Cloud GPUs and TPUs

* Cloud Storage

The unzipped pre-trained model files can also be found in the Google Cloud
Storage folder `gs://cloud-tpu-checkpoints/bert/keras_bert`. For example:

```shell
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export MODEL_DIR=gs://some_bucket/my_output_dir
```

Currently, users are able to access to `tf-nightly` TPUs and the following TPU
script should run with `tf-nightly`.

* GPU -> TPU

Just add the following flags to `run_classifier.py` or `run_squad.py`:

```shell
  --distribution_strategy=tpu
  --tpu=grpc://${TPU_IP_ADDRESS}:8470
```

### Sentence and Sentence-pair Classification Tasks

This example code fine-tunes `BERT-Large` on the Microsoft Research Paraphrase
Corpus (MRPC) corpus, which only contains 3,600 examples and can fine-tune in a
few minutes on most GPUs.

We use the `BERT-Large` (uncased_L-24_H-1024_A-16) as an example throughout the
workflow.
For GPU memory of 16GB or smaller, you may try to use `BERT-Base`
(uncased_L-12_H-768_A-12).

```shell
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export MODEL_DIR=gs://some_bucket/my_output_dir
export GLUE_DIR=gs://some_bucket/datasets
export TASK=MRPC

python run_classifier.py \
  --mode='train_and_eval' \
  --input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
  --train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
  --eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
  --bert_config_file=${BERT_DIR}/bert_config.json \
  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
  --train_batch_size=4 \
  --eval_batch_size=4 \
  --steps_per_loop=1 \
  --learning_rate=2e-5 \
  --num_train_epochs=3 \
  --model_dir=${MODEL_DIR} \
  --distribution_strategy=mirrored
```

Alternatively, instead of specifying `init_checkpoint`, you can specify
`hub_module_url` to employ a pretraind BERT hub module, e.g.,
` --hub_module_url=https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1`.

To use TPU, you only need to switch distribution strategy type to `tpu` with TPU
information and use remote storage for model checkpoints.

```shell
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export TPU_IP_ADDRESS='???'
export MODEL_DIR=gs://some_bucket/my_output_dir
export GLUE_DIR=gs://some_bucket/datasets
export TASK=MRPC

python run_classifier.py \
  --mode='train_and_eval' \
  --input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
  --train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
  --eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
  --bert_config_file=${BERT_DIR}/bert_config.json \
  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
  --train_batch_size=32 \
  --eval_batch_size=32 \
  --steps_per_loop=1000 \
  --learning_rate=2e-5 \
  --num_train_epochs=3 \
  --model_dir=${MODEL_DIR} \
  --distribution_strategy=tpu \
  --tpu=grpc://${TPU_IP_ADDRESS}:8470
```

Note that, we specify `steps_per_loop=1000` for TPU, because running a loop of
training steps inside a `tf.function` can significantly increase TPU utilization
and callbacks will not be called inside the loop.

### SQuAD 1.1

The Stanford Question Answering Dataset (SQuAD) is a popular question answering
benchmark dataset. See more in [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/).

We use the `BERT-Large` (uncased_L-24_H-1024_A-16) as an example throughout the
workflow.
For GPU memory of 16GB or smaller, you may try to use `BERT-Base`
(uncased_L-12_H-768_A-12).

```shell
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export SQUAD_DIR=gs://some_bucket/datasets
export MODEL_DIR=gs://some_bucket/my_output_dir
export SQUAD_VERSION=v1.1

python run_squad.py \
  --input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
  --train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
  --predict_file=${SQUAD_DIR}/dev-v1.1.json \
  --vocab_file=${BERT_DIR}/vocab.txt \
  --bert_config_file=${BERT_DIR}/bert_config.json \
  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
  --train_batch_size=4 \
  --predict_batch_size=4 \
  --learning_rate=8e-5 \
  --num_train_epochs=2 \
  --model_dir=${MODEL_DIR} \
  --distribution_strategy=mirrored
```

Similarily, you can replace `init_checkpoint` FLAG with `hub_module_url` to
specify a hub module path.

To use TPU, you need switch distribution strategy type to `tpu` with TPU
information.

```shell
export BERT_DIR=gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-24_H-1024_A-16
export TPU_IP_ADDRESS='???'
export MODEL_DIR=gs://some_bucket/my_output_dir
export SQUAD_DIR=gs://some_bucket/datasets
export SQUAD_VERSION=v1.1

python run_squad.py \
  --input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
  --train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
  --predict_file=${SQUAD_DIR}/dev-v1.1.json \
  --vocab_file=${BERT_DIR}/vocab.txt \
  --bert_config_file=${BERT_DIR}/bert_config.json \
  --init_checkpoint=${BERT_DIR}/bert_model.ckpt \
  --train_batch_size=32 \
  --learning_rate=8e-5 \
  --num_train_epochs=2 \
  --model_dir=${MODEL_DIR} \
  --distribution_strategy=tpu \
  --tpu=grpc://${TPU_IP_ADDRESS}:8470
```

The dev set predictions will be saved into a file called predictions.json in the
model_dir:

```shell
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ./squad/predictions.json
```


