pytext package¶
Subpackages¶
- pytext.common package
- pytext.config package
- Submodules
- pytext.config.component module
- pytext.config.config_adapter module
- pytext.config.contextual_intent_slot module
- pytext.config.doc_classification module
- pytext.config.field_config module
- pytext.config.module_config module
- pytext.config.pair_classification module
- pytext.config.pytext_config module
- pytext.config.query_document_pairwise_ranking module
- pytext.config.serialize module
- pytext.config.utils module
- Module contents
- pytext.data package
- Subpackages
- pytext.data.data_structures package
- pytext.data.featurizer package
- pytext.data.sources package
- pytext.data.test package
- Submodules
- pytext.data.test.batch_sampler_test module
- pytext.data.test.data_test module
- pytext.data.test.dynamic_pooling_batcher_test module
- pytext.data.test.pandas_data_source_test module
- pytext.data.test.round_robin_batchiterator_test module
- pytext.data.test.simple_featurizer_test module
- pytext.data.test.tensorizers_test module
- pytext.data.test.tokenizers_test module
- pytext.data.test.tsv_data_source_test module
- pytext.data.test.utils_test module
- Module contents
- pytext.data.tokenizers package
- Submodules
- pytext.data.batch_sampler module
- pytext.data.bert_tensorizer module
- pytext.data.data module
- pytext.data.data_handler module
- pytext.data.disjoint_multitask_data module
- pytext.data.disjoint_multitask_data_handler module
- pytext.data.dynamic_pooling_batcher module
- pytext.data.packed_lm_data module
- pytext.data.roberta_tensorizer module
- pytext.data.squad_for_bert_tensorizer module
- pytext.data.squad_tensorizer module
- pytext.data.tensorizers module
- pytext.data.utils module
- pytext.data.xlm_constants module
- pytext.data.xlm_dictionary module
- pytext.data.xlm_tensorizer module
- Module contents
- Subpackages
- pytext.exporters package
- pytext.fields package
- pytext.loss package
- pytext.metric_reporters package
- Submodules
- pytext.metric_reporters.channel module
- pytext.metric_reporters.classification_metric_reporter module
- pytext.metric_reporters.compositional_metric_reporter module
- pytext.metric_reporters.disjoint_multitask_metric_reporter module
- pytext.metric_reporters.intent_slot_detection_metric_reporter module
- pytext.metric_reporters.language_model_metric_reporter module
- pytext.metric_reporters.metric_reporter module
- pytext.metric_reporters.pairwise_ranking_metric_reporter module
- pytext.metric_reporters.regression_metric_reporter module
- pytext.metric_reporters.squad_metric_reporter module
- pytext.metric_reporters.word_tagging_metric_reporter module
- Module contents
- pytext.metrics package
- pytext.models package
- Subpackages
- pytext.models.decoders package
- pytext.models.embeddings package
- Submodules
- pytext.models.embeddings.char_embedding module
- pytext.models.embeddings.contextual_token_embedding module
- pytext.models.embeddings.dict_embedding module
- pytext.models.embeddings.embedding_base module
- pytext.models.embeddings.embedding_list module
- pytext.models.embeddings.word_embedding module
- Module contents
- pytext.models.ensembles package
- pytext.models.language_models package
- pytext.models.output_layers package
- Submodules
- pytext.models.output_layers.distance_output_layer module
- pytext.models.output_layers.doc_classification_output_layer module
- pytext.models.output_layers.doc_regression_output_layer module
- pytext.models.output_layers.intent_slot_output_layer module
- pytext.models.output_layers.lm_output_layer module
- pytext.models.output_layers.output_layer_base module
- pytext.models.output_layers.pairwise_ranking_output_layer module
- pytext.models.output_layers.squad_output_layer module
- pytext.models.output_layers.utils module
- pytext.models.output_layers.word_tagging_output_layer module
- Module contents
- pytext.models.qna package
- pytext.models.representations package
- Subpackages
- pytext.models.representations.transformer package
- Submodules
- pytext.models.representations.transformer.multihead_attention module
- pytext.models.representations.transformer.positional_embedding module
- pytext.models.representations.transformer.residual_mlp module
- pytext.models.representations.transformer.sentence_encoder module
- pytext.models.representations.transformer.transformer module
- Module contents
- pytext.models.representations.transformer package
- Submodules
- pytext.models.representations.attention module
- pytext.models.representations.augmented_lstm module
- pytext.models.representations.bilstm module
- pytext.models.representations.bilstm_doc_attention module
- pytext.models.representations.bilstm_doc_slot_attention module
- pytext.models.representations.bilstm_slot_attn module
- pytext.models.representations.biseqcnn module
- pytext.models.representations.contextual_intent_slot_rep module
- pytext.models.representations.deepcnn module
- pytext.models.representations.docnn module
- pytext.models.representations.huggingface_bert_sentence_encoder module
- pytext.models.representations.jointcnn_rep module
- pytext.models.representations.ordered_neuron_lstm module
- pytext.models.representations.pair_rep module
- pytext.models.representations.pass_through module
- pytext.models.representations.pooling module
- pytext.models.representations.pure_doc_attention module
- pytext.models.representations.representation_base module
- pytext.models.representations.seq_rep module
- pytext.models.representations.slot_attention module
- pytext.models.representations.sparse_transformer_sentence_encoder module
- pytext.models.representations.stacked_bidirectional_rnn module
- pytext.models.representations.traced_transformer_encoder module
- pytext.models.representations.transformer_sentence_encoder module
- pytext.models.representations.transformer_sentence_encoder_base module
- Module contents
- Subpackages
- pytext.models.semantic_parsers package
- pytext.models.seq_models package
- Submodules
- pytext.models.bert_classification_models module
- pytext.models.bert_regression_model module
- pytext.models.crf module
- pytext.models.disjoint_multitask_model module
- pytext.models.distributed_model module
- pytext.models.doc_model module
- pytext.models.joint_model module
- pytext.models.masked_lm module
- pytext.models.masking_utils module
- pytext.models.model module
- pytext.models.module module
- pytext.models.pair_classification_model module
- pytext.models.query_document_pairwise_ranking_model module
- pytext.models.roberta module
- pytext.models.word_model module
- Module contents
- Subpackages
- pytext.optimizer package
- Subpackages
- Submodules
- pytext.optimizer.activations module
- pytext.optimizer.fairseq_fp16_utils module
- pytext.optimizer.fp16_optimizer module
- pytext.optimizer.lamb module
- pytext.optimizer.optimizers module
- pytext.optimizer.radam module
- pytext.optimizer.scheduler module
- pytext.optimizer.swa module
- Module contents
- pytext.task package
- pytext.torchscript package
- pytext.trainers package
- pytext.utils package
- Submodules
- pytext.utils.ascii_table module
- pytext.utils.cuda module
- pytext.utils.data module
- pytext.utils.distributed module
- pytext.utils.documentation module
- pytext.utils.embeddings module
- pytext.utils.file_io module
- pytext.utils.label module
- pytext.utils.lazy module
- pytext.utils.loss module
- pytext.utils.meter module
- pytext.utils.mobile_onnx module
- pytext.utils.model module
- pytext.utils.onnx module
- pytext.utils.path module
- pytext.utils.precision module
- pytext.utils.tensor module
- pytext.utils.test module
- pytext.utils.timing module
- pytext.utils.torch module
- Module contents
Submodules¶
pytext.builtin_task module¶
pytext.main module¶
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pytext.main.
run_single
(rank: int, config_json: str, world_size: int, dist_init_method: Optional[str], metadata: Union[Dict[str, pytext.data.data_handler.CommonMetadata], pytext.data.data_handler.CommonMetadata, None], metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]])[source]¶
pytext.workflow module¶
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pytext.workflow.
export_saved_model_to_caffe2
(saved_model_path: str, export_caffe2_path: str, output_onnx_path: str = None) → None[source]¶
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pytext.workflow.
export_saved_model_to_torchscript
(saved_model_path: str, path: str, quantize: bool = False) → None[source]¶
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pytext.workflow.
get_logits
(snapshot_path: str, use_cuda_if_available: bool, output_path: Optional[str] = None, test_path: Optional[str] = None, field_names: Optional[List[str]] = None, dump_raw_input: bool = False)[source]¶
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pytext.workflow.
prepare_task
(config: pytext.config.pytext_config.PyTextConfig, dist_init_url: str = None, device_id: int = 0, rank: int = 0, world_size: int = 1, metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]] = None, metadata: pytext.data.data_handler.CommonMetadata = None) → Tuple[pytext.task.task.Task_Deprecated, pytext.trainers.training_state.TrainingState][source]¶
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pytext.workflow.
prepare_task_metadata
(config: pytext.config.pytext_config.PyTextConfig) → pytext.data.data_handler.CommonMetadata[source]¶ Loading the whole dataset into cpu memory on every single processes could cause OOMs for data parallel distributed training. To avoid such practice, we move the operations that required loading the whole dataset out of spawn, and pass the context to every single process.
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pytext.workflow.
save_and_export
(config: pytext.config.pytext_config.PyTextConfig, task: pytext.task.task.Task_Deprecated, metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]] = None) → None[source]¶
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pytext.workflow.
test_model
(test_config: pytext.config.pytext_config.TestConfig, metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]], test_out_path: str) → Any[source]¶
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pytext.workflow.
test_model_from_snapshot_path
(snapshot_path: str, use_cuda_if_available: bool, test_path: Optional[str] = None, metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]] = None, test_out_path: str = '', field_names: Optional[List[str]] = None)[source]¶
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pytext.workflow.
train_model
(config: pytext.config.pytext_config.PyTextConfig, dist_init_url: str = None, device_id: int = 0, rank: int = 0, world_size: int = 1, metric_channels: Optional[List[pytext.metric_reporters.channel.Channel]] = None, metadata: pytext.data.data_handler.CommonMetadata = None) → Tuple[source]¶
Module contents¶
-
pytext.
batch_predict_caffe2_model
(pytext_model_file: str, caffe2_model_file: str, db_type: str = 'minidb', data_source: Optional[pytext.data.sources.data_source.DataSource] = None, use_cuda=False, task: Optional[pytext.task.new_task.NewTask] = None, train_config: Optional[pytext.config.pytext_config.PyTextConfig] = None)[source]¶
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pytext.
create_predictor
(config: pytext.config.pytext_config.PyTextConfig, model_file: Optional[str] = None, db_type: str = 'minidb', task: Optional[pytext.task.new_task.NewTask] = None) → Callable[[Mapping[str, str]], Mapping[str, numpy.array]][source]¶ Create a simple prediction API from a training config and an exported caffe2 model file. This model file should be created by calling export on a trained model snapshot.