Keras Implementation of Flair's Contextualized Embeddings
Bubs is a Keras/Tensorflow implementation of the Flair Contextualized Embeddings (https://alanakbik.github.io/papers/coling2018.pdf), limited only to
Bubs consists of two parts:
- ContextualizedEmbedding: a Keras custom layer, which computes the contextualized embeddings. It has two outputs corresponding to the
- InputEncoder: an object for constructing inputs to the ContextualizedEmbedding layer.
This layer consists of:
- two character-level embedding layers (one to be used as input to the forward LSTM, one to be used as an input to the backward LSTM)
- two character-level LSTM layers (one going forward, one going backward along the sentence)
- two indexing layers for selecting character-level LSTM outputs at the locations where tokens end/begin (resulting in two output vectors per token).
- two masking layers to make sure the outputs at padded locations are set to zeros. This is necessary because sentences will have different numbers of tokens and the outputs will be padded to max_token_sequence_length.
The following inputs to the ContextualizedEmbedding layer are required:
forward_input: padded array of character codes corresponding to each sentence with special begin/end characters
backward_input: padded array of character codes in reverse order with special begin/end characters
forward_index_input: padded array of locations of token outputs in forward_input
backward_index_input: padded array of locations of token outputs in backward_input
forward_mask_input: mask of same shape as forward_index_input, with 0's where padded and 1's where real tokens
backward_mask_input:mask of same shape as back_index_input, with 0's where padded and 1's where real tokens
This class provides two methods for preparing inputs to the ContextualizedEmbedding layer:
input_batches_from_raw_text()will accept a raw text string, split it into sentences and tokens, enforce character and token limits by breaking longer sentences into parts. It will then translate characters into numeric codes from the dictionary in
char_to_int.py, pad sentences to the same length, and compute indices of token-level outputs from the character-level LSTMs.
prepare_inputs_from_pretokenized()will accept a list of lists of tokens and output model inputs . Use at your own risk: this function will not enforce character or token limits and will assume that all sentences fit into one batch. Make sure you split all your sentences into batches before calling this function. Otherwise the indices in
backward_index_inputwill be incorrect.
The model weights
The weights of the ContextualizedEmbedding layer were copied from the corresponding weights inside flair's
news-backward-fast embeddings (see
scripts/extracting_model_weights.py for a code snippet that was used to extract the weights).
The name Bubs
Bubs is named after the author's cat, Bubs (short for Bubbles).
A minimal example model
Below we define a very simple example that outputs contextualized embeddings for the following text: "Bubs is a cat. Bubs is cute.".
from keras.layers import Input from keras.models import Model from keras.optimizers import Adam from bubs import ContextualizedEmbedding from bubs.embedding_layer import load_weights_from_npz from bubs.helpers import InputEncoder MAX_TOKEN_SEQUENCE_LEN = 125 MAX_CHAR_SEQUENCE_LEN = 2500 """Load the default weights (provided with this package). If you would like to provide your own weights, you may pass a path to the weights npz file to the load_weights_from_npz() function. """ weights = load_weights_from_npz() context_embedding_layer = ContextualizedEmbedding(MAX_TOKEN_SEQUENCE_LEN, weights) """Required: define inputs to the ContextualizedEmbedding layer""" forward_input = Input(shape=(None,), name="forward_input", dtype="int16") backward_input = Input(shape=(None,), name="backward_input", dtype="int16") forward_index_input = Input( batch_shape=(None, MAX_TOKEN_SEQUENCE_LEN, 2), name="forward_index_input", dtype="int32" ) forward_mask_input = Input( batch_shape=(None, MAX_TOKEN_SEQUENCE_LEN), name="forward_mask_input", dtype="float32" ) backward_index_input = Input( batch_shape=(None, MAX_TOKEN_SEQUENCE_LEN, 2), name="backward_index_input", dtype="int32" ) backward_mask_input = Input( batch_shape=(None, MAX_TOKEN_SEQUENCE_LEN), name="backward_mask_input", dtype="float32" ) all_inputs = [ forward_input, backward_input, forward_index_input, backward_index_input, forward_mask_input, backward_mask_input, ] forward_embeddings, backward_embeddings = context_embedding_layer(all_inputs) model = Model(inputs=all_inputs, outputs=[forward_embeddings, backward_embeddings]) model.compile(optimizer=Adam(), loss="categorical_crossentropy")
Now, let's get contextualized embeddings for each token in a couple of sentences.
# Initialize an InputEncoder for creating model inputs from raw text sentences input_encoder = InputEncoder(MAX_TOKEN_SEQUENCE_LEN, MAX_CHAR_SEQUENCE_LEN) # Embed a couple of test sentences raw_text = "Bubs is a cat. Bubs is cute." ( generator, num_batches, document_index_batches ) = input_encoder.input_batches_from_raw_text(raw_text, batch_size=128) # Only one batch, so we use the generator once forward_embedding, backward_embedding = model.predict_on_batch(next(generator))
The shape of each output will be (2, 125, 1024) for:
- 2 sentences
- 125 words in a padded sentence =
- 1024: dimension of the embedding for each word
Note that the outputs are padded with zeros from the left. For example, to get the forward and backward embedding of the word 'Bubs' in the first sentence, you would need to index the following locations in the model outputs:
The embeddings for the word 'cat' are:
forward_embedding[0, -2] and
First, install tensorflow or tensorflow-gpu, depending on your system:
pip install tensorflow-gpu==1.7.1
pip install tensorflow==1.7.1
1.13.1 pass the tests.
Then, install Bubs:
pip install bubs
Licensed under the Apache 2.0 License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Copyright 2019 Kensho Technologies, Inc.
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