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https://github.com/kpe/bert-for-tf2
A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.
https://github.com/kpe/bert-for-tf2
bert keras tensorflow transformer
Last synced: 19 days ago
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A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.
- Host: GitHub
- URL: https://github.com/kpe/bert-for-tf2
- Owner: kpe
- License: mit
- Created: 2019-05-22T07:51:33.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-13T10:12:34.000Z (almost 2 years ago)
- Last Synced: 2024-10-09T12:32:15.522Z (about 1 month ago)
- Topics: bert, keras, tensorflow, transformer
- Language: Python
- Homepage: https://github.com/kpe/bert-for-tf2
- Size: 283 KB
- Stars: 803
- Watchers: 35
- Forks: 193
- Open Issues: 33
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorflow-2 - A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT
- awesome-bert - kpe/bert-for-tf2 - BERT. https://github.com/kpe/bert-for-tf2, (improvement over BERT:)
- Awesome-Tensorflow2 - kpe/bert-for-tf2
README
BERT for TensorFlow v2
======================
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
This repo contains a `TensorFlow 2.0`_ `Keras`_ implementation of `google-research/bert`_
with support for loading of the original `pre-trained weights`_,
and producing activations **numerically identical** to the one calculated by the original model.
`ALBERT`_ and `adapter-BERT`_ are also supported by setting the corresponding
configuration parameters (``shared_layer=True``, ``embedding_size`` for `ALBERT`_
and ``adapter_size`` for `adapter-BERT`_). Setting both will result in an adapter-ALBERT
by sharing the BERT parameters across all layers while adapting every layer with layer specific adapter.
The implementation is build from scratch using only basic tensorflow operations,
following the code in `google-research/bert/modeling.py`_
(but skipping dead code and applying some simplifications). It also utilizes `kpe/params-flow`_ to reduce
common Keras boilerplate code (related to passing model and layer configuration arguments).
`bert-for-tf2`_ should work with both `TensorFlow 2.0`_ and `TensorFlow 1.14`_ or newer.
NEWS
----
- **30.Jul.2020** - `VERBOSE=0` env variable for suppressing stdout output.
- **06.Apr.2020** - using latest ``py-params`` introducing ``WithParams`` base for ``Layer``
and ``Model``. See news in `kpe/py-params`_ for how to update (``_construct()`` signature has change and
requires calling ``super().__construct()``).
- **06.Jan.2020** - support for loading the tar format weights from `google-research/ALBERT`.
- **18.Nov.2019** - ALBERT tokenization added (make sure to import as ``from bert import albert_tokenization`` or ``from bert import bert_tokenization``).
- **08.Nov.2019** - using v2 per default when loading the `TFHub/albert`_ weights of `google-research/ALBERT`_.
- **05.Nov.2019** - minor ALBERT word embeddings refactoring (``word_embeddings_2`` -> ``word_embeddings_projector``) and related parameter freezing fixes.
- **04.Nov.2019** - support for extra (task specific) token embeddings using negative token ids.
- **29.Oct.2019** - support for loading of the pre-trained ALBERT weights released by `google-research/ALBERT`_ at `TFHub/albert`_.
- **11.Oct.2019** - support for loading of the pre-trained ALBERT weights released by `brightmart/albert_zh ALBERT for Chinese`_.
- **10.Oct.2019** - support for `ALBERT`_ through the ``shared_layer=True``
and ``embedding_size=128`` params.
- **03.Sep.2019** - walkthrough on fine tuning with adapter-BERT and storing the
fine tuned fraction of the weights in a separate checkpoint (see ``tests/test_adapter_finetune.py``).
- **02.Sep.2019** - support for extending the token type embeddings of a pre-trained model
by returning the mismatched weights in ``load_stock_weights()`` (see ``tests/test_extend_segments.py``).
- **25.Jul.2019** - there are now two colab notebooks under ``examples/`` showing how to
fine-tune an IMDB Movie Reviews sentiment classifier from pre-trained BERT weights
using an `adapter-BERT`_ model architecture on a GPU or TPU in Google Colab.
- **28.Jun.2019** - v.0.3.0 supports `adapter-BERT`_ (`google-research/adapter-bert`_)
for "Parameter-Efficient Transfer Learning for NLP", i.e. fine-tuning small overlay adapter
layers over BERT's transformer encoders without changing the frozen BERT weights.
LICENSE
-------
MIT. See `License File `_.
Install
-------
``bert-for-tf2`` is on the Python Package Index (PyPI):
::
pip install bert-for-tf2
Usage
-----
BERT in `bert-for-tf2` is implemented as a Keras layer. You could instantiate it like this:
.. code:: python
from bert import BertModelLayer
l_bert = BertModelLayer(**BertModelLayer.Params(
vocab_size = 16000, # embedding params
use_token_type = True,
use_position_embeddings = True,
token_type_vocab_size = 2,
num_layers = 12, # transformer encoder params
hidden_size = 768,
hidden_dropout = 0.1,
intermediate_size = 4*768,
intermediate_activation = "gelu",
adapter_size = None, # see arXiv:1902.00751 (adapter-BERT)
shared_layer = False, # True for ALBERT (arXiv:1909.11942)
embedding_size = None, # None for BERT, wordpiece embedding size for ALBERT
name = "bert" # any other Keras layer params
))
or by using the ``bert_config.json`` from a `pre-trained google model`_:
.. code:: python
import bert
model_dir = ".models/uncased_L-12_H-768_A-12"
bert_params = bert.params_from_pretrained_ckpt(model_dir)
l_bert = bert.BertModelLayer.from_params(bert_params, name="bert")
now you can use the BERT layer in your Keras model like this:
.. code:: python
from tensorflow import keras
max_seq_len = 128
l_input_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32')
l_token_type_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32')
# using the default token_type/segment id 0
output = l_bert(l_input_ids) # output: [batch_size, max_seq_len, hidden_size]
model = keras.Model(inputs=l_input_ids, outputs=output)
model.build(input_shape=(None, max_seq_len))
# provide a custom token_type/segment id as a layer input
output = l_bert([l_input_ids, l_token_type_ids]) # [batch_size, max_seq_len, hidden_size]
model = keras.Model(inputs=[l_input_ids, l_token_type_ids], outputs=output)
model.build(input_shape=[(None, max_seq_len), (None, max_seq_len)])
if you choose to use `adapter-BERT`_ by setting the `adapter_size` parameter,
you would also like to freeze all the original BERT layers by calling:
.. code:: python
l_bert.apply_adapter_freeze()
and once the model has been build or compiled, the original pre-trained weights
can be loaded in the BERT layer:
.. code:: python
import bert
bert_ckpt_file = os.path.join(model_dir, "bert_model.ckpt")
bert.load_stock_weights(l_bert, bert_ckpt_file)
**N.B.** see `tests/test_bert_activations.py`_ for a complete example.
FAQ
---
0. In all the examlpes bellow, **please note** the line:
.. code:: python
# use in a Keras Model here, and call model.build()
for a quick test, you can replace it with something like:
.. code:: python
model = keras.models.Sequential([
keras.layers.InputLayer(input_shape=(128,)),
l_bert,
keras.layers.Lambda(lambda x: x[:, 0, :]),
keras.layers.Dense(2)
])
model.build(input_shape=(None, 128))
1. How to use BERT with the `google-research/bert`_ pre-trained weights?
.. code:: python
model_name = "uncased_L-12_H-768_A-12"
model_dir = bert.fetch_google_bert_model(model_name, ".models")
model_ckpt = os.path.join(model_dir, "bert_model.ckpt")
bert_params = bert.params_from_pretrained_ckpt(model_dir)
l_bert = bert.BertModelLayer.from_params(bert_params, name="bert")
# use in a Keras Model here, and call model.build()
bert.load_bert_weights(l_bert, model_ckpt) # should be called after model.build()
2. How to use ALBERT with the `google-research/ALBERT`_ pre-trained weights (fetching from TFHub)?
see `tests/nonci/test_load_pretrained_weights.py `_:
.. code:: python
model_name = "albert_base"
model_dir = bert.fetch_tfhub_albert_model(model_name, ".models")
model_params = bert.albert_params(model_name)
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
# use in a Keras Model here, and call model.build()
bert.load_albert_weights(l_bert, albert_dir) # should be called after model.build()
3. How to use ALBERT with the `google-research/ALBERT`_ pre-trained weights (non TFHub)?
see `tests/nonci/test_load_pretrained_weights.py `_:
.. code:: python
model_name = "albert_base_v2"
model_dir = bert.fetch_google_albert_model(model_name, ".models")
model_ckpt = os.path.join(albert_dir, "model.ckpt-best")
model_params = bert.albert_params(model_dir)
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
# use in a Keras Model here, and call model.build()
bert.load_albert_weights(l_bert, model_ckpt) # should be called after model.build()
4. How to use ALBERT with the `brightmart/albert_zh`_ pre-trained weights?
see `tests/nonci/test_albert.py `_:
.. code:: python
model_name = "albert_base"
model_dir = bert.fetch_brightmart_albert_model(model_name, ".models")
model_ckpt = os.path.join(model_dir, "albert_model.ckpt")
bert_params = bert.params_from_pretrained_ckpt(model_dir)
l_bert = bert.BertModelLayer.from_params(bert_params, name="bert")
# use in a Keras Model here, and call model.build()
bert.load_albert_weights(l_bert, model_ckpt) # should be called after model.build()
5. How to tokenize the input for the `google-research/bert`_ models?
.. code:: python
do_lower_case = not (model_name.find("cased") == 0 or model_name.find("multi_cased") == 0)
bert.bert_tokenization.validate_case_matches_checkpoint(do_lower_case, model_ckpt)
vocab_file = os.path.join(model_dir, "vocab.txt")
tokenizer = bert.bert_tokenization.FullTokenizer(vocab_file, do_lower_case)
tokens = tokenizer.tokenize("Hello, BERT-World!")
token_ids = tokenizer.convert_tokens_to_ids(tokens)
6. How to tokenize the input for `brightmart/albert_zh`?
.. code:: python
import params_flow pf
# fetch the vocab file
albert_zh_vocab_url = "https://raw.githubusercontent.com/brightmart/albert_zh/master/albert_config/vocab.txt"
vocab_file = pf.utils.fetch_url(albert_zh_vocab_url, model_dir)
tokenizer = bert.albert_tokenization.FullTokenizer(vocab_file)
tokens = tokenizer.tokenize("你好世界")
token_ids = tokenizer.convert_tokens_to_ids(tokens)
7. How to tokenize the input for the `google-research/ALBERT`_ models?
.. code:: python
import sentencepiece as spm
spm_model = os.path.join(model_dir, "assets", "30k-clean.model")
sp = spm.SentencePieceProcessor()
sp.load(spm_model)
do_lower_case = True
processed_text = bert.albert_tokenization.preprocess_text("Hello, World!", lower=do_lower_case)
token_ids = bert.albert_tokenization.encode_ids(sp, processed_text)
8. How to tokenize the input for the Chinese `google-research/ALBERT`_ models?
.. code:: python
import bert
vocab_file = os.path.join(model_dir, "vocab.txt")
tokenizer = bert.albert_tokenization.FullTokenizer(vocab_file=vocab_file)
tokens = tokenizer.tokenize(u"你好世界")
token_ids = tokenizer.convert_tokens_to_ids(tokens)
Resources
---------
- `BERT`_ - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- `adapter-BERT`_ - adapter-BERT: Parameter-Efficient Transfer Learning for NLP
- `ALBERT`_ - ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
- `google-research/bert`_ - the original `BERT`_ implementation
- `google-research/ALBERT`_ - the original `ALBERT`_ implementation by Google
- `google-research/albert(old)`_ - the old location of the original `ALBERT`_ implementation by Google
- `brightmart/albert_zh`_ - pre-trained `ALBERT`_ weights for Chinese
- `kpe/params-flow`_ - A Keras coding style for reducing `Keras`_ boilerplate code in custom layers by utilizing `kpe/py-params`_
.. _`kpe/params-flow`: https://github.com/kpe/params-flow
.. _`kpe/py-params`: https://github.com/kpe/py-params
.. _`bert-for-tf2`: https://github.com/kpe/bert-for-tf2
.. _`Keras`: https://keras.io
.. _`pre-trained weights`: https://github.com/google-research/bert#pre-trained-models
.. _`google-research/bert`: https://github.com/google-research/bert
.. _`google-research/bert/modeling.py`: https://github.com/google-research/bert/blob/master/modeling.py
.. _`BERT`: https://arxiv.org/abs/1810.04805
.. _`pre-trained google model`: https://github.com/google-research/bert
.. _`tests/test_bert_activations.py`: https://github.com/kpe/bert-for-tf2/blob/master/tests/test_compare_activations.py
.. _`TensorFlow 2.0`: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf
.. _`TensorFlow 1.14`: https://www.tensorflow.org/versions/r1.14/api_docs/python/tf
.. _`google-research/adapter-bert`: https://github.com/google-research/adapter-bert/
.. _`adapter-BERT`: https://arxiv.org/abs/1902.00751
.. _`ALBERT`: https://arxiv.org/abs/1909.11942
.. _`brightmart/albert_zh ALBERT for Chinese`: https://github.com/brightmart/albert_zh
.. _`brightmart/albert_zh`: https://github.com/brightmart/albert_zh
.. _`google ALBERT weights`: https://github.com/google-research/google-research/tree/master/albert
.. _`google-research/albert(old)`: https://github.com/google-research/google-research/tree/master/albert
.. _`google-research/ALBERT`: https://github.com/google-research/ALBERT
.. _`TFHub/albert`: https://tfhub.dev/google/albert_base/2
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