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🏖 Easy training and deployment of seq2seq models.

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Headliner is a sequence modeling library that eases the training and in particular, the deployment of custom sequence models for both researchers and developers. You can very easily deploy your models in a few lines of code. It was originally built for our own research to generate headlines from Welt news articles (see figure 1). That's why we chose the name, Headliner.

Figure 1: One example from our headline generator.

Update 21.01.2020

The library now supports fine-tuning pre-trained BERT models with custom preprocessing as in Text Summarization with Pretrained Encoders!

check out this tutorial on colab!

🧠 Internals

We use sequence-to-sequence (seq2seq) under the hood, an encoder-decoder framework (see figure 2). We provide a very simple interface to train and deploy seq2seq models. Although this library was created internally to generate headlines, you can also use it for other tasks like machine translations, text summarization and many more.

Figure 2: Encoder-decoder sequence-to-sequence model.

Why Headliner?

You may ask why another seq2seq library? There are a couple of them out there already. For example, Facebook has fairseq, Google has seq2seq and there is also OpenNMT. Although those libraries are great, they have a few drawbacks for our use case e.g. the former doesn't focus much on production whereas the Google one is not actively maintained. OpenNMT was the closest one to match our requirements i.e. it has a strong focus on production. However, we didn't like that their workflow (preparing data, training and evaluation) is mainly done via the command line. They also expose a well-defined API though but the complexity there is still too high with too much custom code (see their minimal transformer training example).

Therefore, we built this library for us with the following goals in mind:

  • Easy-to-use API for training and deployment (only a few lines of code)
  • Uses TensorFlow 2.0 with all its new features (
  • Modular classes: text preprocessing, modeling, evaluation
  • Extensible for different encoder-decoder models
  • Works on large text data

For more details on the library, read the documentation at:

Headliner is compatible with Python 3.6 and is distributed under the MIT license.

⚙️ Installation

⚠️ Before installing Headliner, you need to install TensorFlow as we use this as our deep learning framework. For more details on how to install it, have a look at the TensorFlow installation instructions.

Then you can install Headliner itself. There are two ways to install Headliner:

  • Install Headliner from PyPI (recommended):
pip install headliner
  • Install Headliner from the GitHub source:
git clone
cd headliner
python install

📖 Usage


For the training, you need to import one of our provided models or create your own custom one. Then you need to create the dataset, a

of input-output sequences, and then train it:
from headliner.trainer import Trainer
from headliner.model.transformer_summarizer import TransformerSummarizer

data = [('You are the stars, earth and sky for me!', 'I love you.'), ('You are great, but I have other plans.', 'I like you.')]

summarizer = TransformerSummarizer(embedding_size=64, max_prediction_len=20) trainer = Trainer(batch_size=2, steps_per_epoch=100) trainer.train(summarizer, data, num_epochs=2)'/tmp/summarizer')


The prediction can be done in a few lines of code:

from headliner.model.transformer_summarizer import TransformerSummarizer

summarizer = TransformerSummarizer.load('/tmp/summarizer') summarizer.predict('You are the stars, earth and sky for me!')


Currently available models include a basic encoder-decoder, an encoder-decoder with Luong attention, the transformer and a transformer on top of a pre-trained BERT-model:

from headliner.model.basic_summarizer import BasicSummarizer
from headliner.model.attention_summarizer import AttentionSummarizer
from headliner.model.transformer_summarizer import TransformerSummarizer
from headliner.model.bert_summarizer import BertSummarizer

basic_summarizer = BasicSummarizer() attention_summarizer = AttentionSummarizer() transformer_summarizer = TransformerSummarizer() bert_summarizer = BertSummarizer()

Advanced training

Training using a validation split and model checkpointing:

from headliner.model.transformer_summarizer import TransformerSummarizer
from headliner.trainer import Trainer

train_data = [('You are the stars, earth and sky for me!', 'I love you.'), ('You are great, but I have other plans.', 'I like you.')] val_data = [('You are great, but I have other plans.', 'I like you.')]

summarizer = TransformerSummarizer(num_heads=1, feed_forward_dim=512, num_layers=1, embedding_size=64, max_prediction_len=50) trainer = Trainer(batch_size=8, steps_per_epoch=50, max_vocab_size_encoder=10000, max_vocab_size_decoder=10000, tensorboard_dir='/tmp/tensorboard', model_save_path='/tmp/summarizer')

trainer.train(summarizer, train_data, val_data=val_data, num_epochs=3)

Advanced prediction

Prediction information such as attention weights and logits can be accessed via predict_vectors returning a dictionary:

from headliner.model.transformer_summarizer import TransformerSummarizer

summarizer = TransformerSummarizer.load('/tmp/summarizer') summarizer.predict_vectors('You are the stars, earth and sky for me!')

Resume training

A previously trained summarizer can be loaded and then retrained. In this case the data preprocessing and vectorization is loaded from the model.

train_data = [('Some new training data.', 'New data.')] * 10

summarizer_loaded = TransformerSummarizer.load('/tmp/summarizer') trainer = Trainer(batch_size=2) trainer.train(summarizer_loaded, train_data)'/tmp/summarizer_retrained')

Use pretrained GloVe embeddings

Embeddings in GloVe format can be injected in to the trainer as follows. Optionally, set the embedding to non-trainable.

trainer = Trainer(embedding_path_encoder='/tmp/embedding_encoder.txt',

make sure the embedding size matches to the embedding size of the files

summarizer = TransformerSummarizer(embedding_size=64, embedding_encoder_trainable=False, embedding_decoder_trainable=False)

Custom preprocessing

A model can be initialized with custom preprocessing and tokenization:

from headliner.preprocessing.preprocessor import Preprocessor

train_data = [('Some inputs.', 'Some outputs.')] * 10

preprocessor = Preprocessor(filter_pattern='', lower_case=True, hash_numbers=False) train_prep = [preprocessor(t) for t in train_data] inputs_prep = [t[0] for t in train_prep] targets_prep = [t[1] for t in train_prep]

Build tf subword tokenizers. Other custom tokenizers can be implemented

by subclassing headliner.preprocessing.Tokenizer

from tensorflow_datasets.core.features.text import SubwordTextEncoder tokenizer_input = SubwordTextEncoder.build_from_corpus( inputs_prep, target_vocab_size=213, reserved_tokens=[preprocessor.start_token, preprocessor.end_token]) tokenizer_target = SubwordTextEncoder.build_from_corpus( targets_prep, target_vocab_size=213, reserved_tokens=[preprocessor.start_token, preprocessor.end_token])

vectorizer = Vectorizer(tokenizer_input, tokenizer_target) summarizer = TransformerSummarizer(embedding_size=64, max_prediction_len=50) summarizer.init_model(preprocessor, vectorizer)

trainer = Trainer(batch_size=2) trainer.train(summarizer, train_data, num_epochs=3)

Use pre-trained BERT embeddings

Pre-trained BERT models can be included as follows. Be aware that pre-trained BERT models are expensive to train and require custom preprocessing!

from headliner.preprocessing.bert_preprocessor import BertPreprocessor
from spacy.lang.en import English

train_data = [('Some inputs.', 'Some outputs.')] * 10

use BERT-specific start and end token

preprocessor = BertPreprocessor(nlp=English() train_prep = [preprocessor(t) for t in train_data] targets_prep = [t[1] for t in train_prep]

from tensorflow_datasets.core.features.text import SubwordTextEncoder from transformers import BertTokenizer from headliner.model.bert_summarizer import BertSummarizer

Use a pre-trained BERT embedding and BERT tokenizer for the encoder

tokenizer_input = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer_target = SubwordTextEncoder.build_from_corpus( targets_prep, target_vocab_size=2**13, reserved_tokens=[preprocessor.start_token, preprocessor.end_token])

vectorizer = BertVectorizer(tokenizer_input, tokenizer_target) summarizer = BertSummarizer(num_heads=2, feed_forward_dim=512, num_layers_encoder=0, num_layers_decoder=4, bert_embedding_encoder='bert-base-uncased', embedding_size_encoder=768, embedding_size_decoder=768, dropout_rate=0.1, max_prediction_len=50)) summarizer.init_model(preprocessor, vectorizer)

trainer = Trainer(batch_size=2) trainer.train(summarizer, train_data, num_epochs=3)

Training on large datasets

Large datasets can be handled by using an iterator:

def read_data_iteratively():
    return (('Some inputs.', 'Some outputs.') for _ in range(1000))

class DataIterator: def iter(self): return read_data_iteratively()

data_iter = DataIterator()

summarizer = TransformerSummarizer(embedding_size=10, max_prediction_len=20) trainer = Trainer(batch_size=16, steps_per_epoch=1000) trainer.train(summarizer, data_iter, num_epochs=3)

🤝 Contribute

We welcome all kinds of contributions such as new models, new examples and many more. See the Contribution guide for more details.

📝 Cite this work

Please cite Headliner in your publications if this is useful for your research. Here is an example BibTeX entry:

  author={Christian Schäfer & Dat Tran},

🏗 Maintainers

© Copyright

See LICENSE for details.


Text Summarization with Pretrained Encoders

Effective Approaches to Attention-based Neural Machine Translation


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