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        "description": "[![CI](https://github.com/OpenNMT/OpenNMT-tf/workflows/CI/badge.svg)](https://github.com/OpenNMT/OpenNMT-tf/actions?query=workflow%3ACI) [![codecov](https://codecov.io/gh/OpenNMT/OpenNMT-tf/branch/master/graph/badge.svg)](https://codecov.io/gh/OpenNMT/OpenNMT-tf) [![PyPI version](https://badge.fury.io/py/OpenNMT-tf.svg)](https://badge.fury.io/py/OpenNMT-tf) [![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://opennmt.net/OpenNMT-tf/) [![Gitter](https://badges.gitter.im/OpenNMT/OpenNMT-tf.svg)](https://gitter.im/OpenNMT/OpenNMT-tf?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![Forum](https://img.shields.io/discourse/status?server=https%3A%2F%2Fforum.opennmt.net%2F)](https://forum.opennmt.net/)\n\n# OpenNMT-tf\n\nOpenNMT-tf is a general purpose sequence learning toolkit using TensorFlow 2. While neural machine translation is the main target task, it has been designed to more generally support:\n\n* sequence to sequence mapping\n* sequence tagging\n* sequence classification\n* language modeling\n\nThe project is production-oriented and comes with [backward compatibility guarantees](https://github.com/OpenNMT/OpenNMT-tf/blob/master/CHANGELOG.md).\n\n## Key features\n\n### Modular model architecture\n\nModels 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:\n\n```python\nopennmt.models.SequenceToSequence(\n    source_inputter=opennmt.inputters.ParallelInputter(\n        [\n            opennmt.inputters.WordEmbedder(embedding_size=256),\n            opennmt.inputters.WordEmbedder(embedding_size=256),\n        ],\n        reducer=opennmt.layers.ConcatReducer(axis=-1),\n    ),\n    target_inputter=opennmt.inputters.WordEmbedder(embedding_size=512),\n    encoder=opennmt.encoders.SelfAttentionEncoder(num_layers=6),\n    decoder=opennmt.decoders.AttentionalRNNDecoder(\n        num_layers=4,\n        num_units=512,\n        attention_mechanism_class=tfa.seq2seq.LuongAttention,\n    ),\n    share_embeddings=opennmt.models.EmbeddingsSharingLevel.TARGET,\n)\n```\n\nThe [`opennmt`](https://opennmt.net/OpenNMT-tf/package/opennmt.html) package exposes other building blocks that can be used to design:\n\n* [multiple input features](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html)\n* [mixed embedding representation](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.MixedInputter.html)\n* [multi-source context](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html)\n* [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\n* [hybrid sequence to sequence models](https://opennmt.net/OpenNMT-tf/package/opennmt.models.SequenceToSequence.html)\n\nStandard 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.\n\n*Find more information about model configuration in the [documentation](https://opennmt.net/OpenNMT-tf/model.html).*\n\n### Full TensorFlow 2 integration\n\nOpenNMT-tf is fully integrated in the TensorFlow 2 ecosystem:\n\n* Reusable layers extending [`tf.keras.layers.Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer)\n* Multi-GPU training with [`tf.distribute`](https://www.tensorflow.org/api_docs/python/tf/distribute) and distributed training with [Horovod](https://github.com/horovod/horovod)\n* Mixed precision training with [`tf.keras.mixed_precision`](https://www.tensorflow.org/guide/mixed_precision)\n* Visualization with [TensorBoard](https://www.tensorflow.org/tensorboard)\n* `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)\n\n### Compatibility with CTranslate2\n\n[CTranslate2](https://github.com/OpenNMT/CTranslate2) is an optimized inference engine for OpenNMT models featuring fast CPU and GPU execution, model quantization, parallel translations, dynamic memory usage, interactive decoding, and more! OpenNMT-tf can [automatically export](https://opennmt.net/OpenNMT-tf/serving.html#ctranslate2) models to be used in CTranslate2.\n\n### Dynamic data pipeline\n\nOpenNMT-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.\n\n### Model fine-tuning\n\nOpenNMT-tf supports model fine-tuning workflows:\n\n* Model weights can be transferred to new word vocabularies, e.g. to inject domain terminology before fine-tuning on in-domain data\n* [Contrastive learning](https://ai.google/research/pubs/pub48253/) to reduce word omission errors\n\n### Source-target alignment\n\nSequence 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.\n\n---\n\nOpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:\n\n* automatic evaluation during the training\n* multiple decoding strategy: greedy search, beam search, random sampling\n* N-best rescoring\n* gradient accumulation\n* scheduled sampling\n* checkpoint averaging\n* ... and more!\n\n*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*\n\n## Usage\n\nOpenNMT-tf requires:\n\n* Python 3.6 or above\n* TensorFlow 2.4, 2.5, 2.6, 2.7, or 2.8\n\nWe recommend installing it with `pip`:\n\n```bash\npip install --upgrade pip\npip install OpenNMT-tf\n```\n\n*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*\n\n### Command line\n\nOpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.\n\nFor all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:\n\n* the **model** type\n* the **parameters** described in a YAML file\n* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`\n\nthat are passed to the main script:\n\n```\nonmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>\n```\n\n*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*\n\n### Library\n\nOpenNMT-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.\n\nFor example, the `Runner` class can be used to train and evaluate models with few lines of code:\n\n```python\nimport opennmt\n\nconfig = {\n    \"model_dir\": \"/data/wmt-ende/checkpoints/\",\n    \"data\": {\n        \"source_vocabulary\": \"/data/wmt-ende/joint-vocab.txt\",\n        \"target_vocabulary\": \"/data/wmt-ende/joint-vocab.txt\",\n        \"train_features_file\": \"/data/wmt-ende/train.en\",\n        \"train_labels_file\": \"/data/wmt-ende/train.de\",\n        \"eval_features_file\": \"/data/wmt-ende/valid.en\",\n        \"eval_labels_file\": \"/data/wmt-ende/valid.de\",\n    }\n}\n\nmodel = opennmt.models.TransformerBase()\nrunner = opennmt.Runner(model, config, auto_config=True)\nrunner.train(num_devices=2, with_eval=True)\n```\n\nHere is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:\n\n```python\ndecoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)\n\ninitial_state = decoder.initial_state(\n    memory=memory, memory_sequence_length=memory_sequence_length\n)\n\nbatch_size = tf.shape(memory)[0]\nstart_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)\n\ndecoding_result = decoder.dynamic_decode(\n    target_embedding,\n    start_ids=start_ids,\n    initial_state=initial_state,\n    decoding_strategy=opennmt.utils.BeamSearch(4),\n)\n```\n\nMore examples using OpenNMT-tf as a library can be found online:\n\n* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library\n* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving\n\n*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/opennmt.html).*\n\n## Additional resources\n\n* [Documentation](https://opennmt.net/OpenNMT-tf)\n* [Forum](https://forum.opennmt.net)\n* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)\n\n\n",
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