Python库 | OpenNMT-tf-2.8.1.tar.gz

[](https://travis-ci.org/OpenNMT/OpenNMT-tf) [](https://badge.fury.io/py/OpenNMT-tf) [](https://opennmt.net/OpenNMT-tf/) [](https://gitter.im/OpenNMT/OpenNMT-tf?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) # OpenNMT-tf OpenNMT-tf is a general purpose sequence learning toolkit using TensorFlow 2.0. While neural machine translation is the main target task, it has been designed to more generally support: * sequence to sequence mapping * sequence tagging * sequence classification * language modeling The project is production-oriented and comes with [backward compatibility guarantees](https://github.com/OpenNMT/OpenNMT-tf/blob/master/CHANGELOG.md). ## Key features ### Modular model architecture Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence to sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings: ```python opennmt.models.SequenceToSequence( source_inputter=opennmt.inputters.ParallelInputter( [opennmt.inputters.WordEmbedder(embedding_size=256), opennmt.inputters.WordEmbedder(embedding_size=256)], reducer=opennmt.layers.ConcatReducer(axis=-1)), target_inputter=opennmt.inputters.WordEmbedder(embedding_size=512), encoder=opennmt.encoders.SelfAttentionEncoder(num_layers=6), decoder=opennmt.decoders.AttentionalRNNDecoder( num_layers=4, num_units=512, attention_mechanism_class=tfa.seq2seq.LuongAttention), share_embeddings=opennmt.models.EmbeddingsSharingLevel.TARGET) ``` The [`opennmt`](https://opennmt.net/OpenNMT-tf/package/opennmt.html) package exposes other building blocks that can be used to design: * [multiple input features](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html) * [mixed embedding representation](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.MixedInputter.html) * [multi-source context](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html) * [cascaded](https://opennmt.net/OpenNMT-tf/package/opennmt.encoders.SequentialEncoder.html) or [multi-column](https://opennmt.net/OpenNMT-tf/package/opennmt.encoders.ParallelEncoder.html) encoder * [hybrid sequence to sequence models](https://opennmt.net/OpenNMT-tf/package/opennmt.models.SequenceToSequence.html) Standard models such as the Transformer are defined in a [model catalog](https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/models/catalog.py) and can be used without additional configuration. *Find more information about model configuration in the [documentation](https://opennmt.net/OpenNMT-tf/model.html).* ### Full TensorFlow 2.0 integration OpenNMT-tf is fully integrated in the TensorFlow 2.0 ecosystem: * Reusable layers extending [`tf.keras.layers.Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) * Multi-GPU training with [`tf.distribute`](https://www.tensorflow.org/api_docs/python/tf/distribute) * Mixed precision support via a [graph optimization pass](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/train/experimental/enable_mixed_precision_graph_rewrite) * Visualization with [TensorBoard](https://www.tensorflow.org/tensorboard) * `tf.function` graph tracing that can be [exported to a SavedModel](https://opennmt.net/OpenNMT-tf/serving.html) and served with [TensorFlow Serving](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/serving/tensorflow_serving) or [Python](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/serving/python) ### Dynamic data pipeline OpenNMT-tf does not require to compile the data before the training. Instead, it can directly read text files and preprocess the data when needed by the training. This allows [on-the-fly tokenization](https://opennmt.net/OpenNMT-tf/tokenization.html) and data augmentation by injecting random noise. ### Model fine-tuning OpenNMT-tf supports model fine-tuning workflows: * Model weights can be transferred to new word vocabularies, e.g. to inject domain terminology before fine-tuning on in-domain data * [Contrastive learning](https://ai.google/research/pubs/pub48253/) to reduce word omission errors ### Source-target alignment Sequence to sequence models can be trained with [guided alignment](https://arxiv.org/abs/1607.01628) and alignment information are returned as part of the translation API. --- OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as: * automatic evaluation during the training * multiple decoding strategy: greedy search, beam search, random sampling * N-best rescoring * gradient accumulation * scheduled sampling * checkpoint averaging * ... and more! *See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.* ## Usage OpenNMT-tf requires: * Python >= 3.5 We recommend installing it with `pip`: ```bash pip install --upgrade pip pip install OpenNMT-tf ``` *See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.* ### Command line OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models. For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements: * the **model** type * the **parameters** described in a YAML file * the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab` that are passed to the main script: ``` onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options> ``` *For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).* ### Library OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/opennmt.html), from high-level training utilities to low-level model layers and dataset transformations. For example, the `Runner` class can be used to train and evaluate models with few lines of code: ```python import opennmt config = { "model_dir": "/data/wmt-ende/checkpoints/, "data": { "source_vocabulary": "/data/wmt-ende/joint-vocab.txt", "target_vocabulary": "/data/wmt-ende/joint-vocab.txt", "train_features_file": "/data/wmt-ende/train.en", "train_labels_file": "/data/wmt-ende/train.de", "eval_features_file": "/data/wmt-ende/valid.en", "eval_labels_file": "/data/wmt-ende/valid.de", } } model = opennmt.models.TransformerBase() runner = opennmt.Runner(model, config, auto_config=True) runner.train(num_devices=2, with_eval=True) ``` Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder: ```python decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6) decoder.initialize(vocab_size=32000) initial_state = decoder.initial_state( memory=memory, memory_sequence_length=memory_sequence_length) batch_size = tf.shape(memory)[0] start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID) decoding_result = decoder.dynamic_decode( target_embedding, start_ids=start_ids, initial_state=initial_state, decoding_strategy=opennmt.utils.BeamSearch(4)) ``` More examples using OpenNMT-tf as a library can be found online: * The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library * [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-le