ud_parser

ud_parser

Universal Dependencies Parsing (lemmatization, features, PoS tagging and dependency parsing).

class hanlp.components.parsers.ud.ud_parser.UniversalDependenciesParser(**kwargs)[source]

Universal Dependencies Parsing (lemmatization, features, PoS tagging and dependency parsing) implementation of “75 Languages, 1 Model: Parsing Universal Dependencies Universally” (Kondratyuk & Straka 2019).

Parameters

**kwargs – Predefined config.

build_criterion(**kwargs)[source]

Implement this method to build criterion (loss function).

Parameters

**kwargs – The subclass decides the method signature.

build_dataloader(data, batch_size, shuffle=False, device=None, logger: Optional[logging.Logger] = None, sampler_builder=None, gradient_accumulation=1, transformer: Optional[hanlp.layers.embeddings.contextual_word_embedding.ContextualWordEmbedding] = None, **kwargs) → torch.utils.data.dataloader.DataLoader[source]

Build dataloader for training, dev and test sets. It’s suggested to build vocabs in this method if they are not built yet.

Parameters

  • data – Data representing samples, which can be a path or a list of samples.

  • batch_size – Number of samples per batch.

  • shuffle – Whether to shuffle this dataloader.

  • device – Device tensors should be loaded onto.

  • logger – Logger for reporting some message if dataloader takes a long time or if vocabs has to be built.

  • **kwargs – Arguments from **self.config.

build_metric(**kwargs)[source]

Implement this to build metric(s).

Parameters

**kwargs – The subclass decides the method signature.

build_model(transformer: hanlp.layers.embeddings.contextual_word_embedding.ContextualWordEmbedding, n_mlp_arc, n_mlp_rel, mlp_dropout, mix_embedding, layer_dropout, training=True, **kwargs) → torch.nn.modules.module.Module[source]

Build model.

Parameters

  • training – True if called during training.

  • **kwargs – **self.config.

build_optimizer(trn, **kwargs)[source]

Implement this method to build an optimizer.

Parameters

**kwargs – The subclass decides the method signature.

build_vocabs(trn, logger, **kwargs)[source]

Override this method to build vocabs.

Parameters

  • trn – Training set.

  • logger – Logger for reporting progress.

evaluate_dataloader(data: torch.utils.data.dataloader.DataLoader, criterion: Callable, metric: Optional[hanlp.metrics.mtl.MetricDict] = None, output=False, logger=None, ratio_width=None, **kwargs)[source]

Evaluate on a dataloader.

Parameters

  • data – Dataloader which can build from any data source.

  • criterion – Loss function.

  • metric – Metric(s).

  • output – Whether to save outputs into some file.

  • **kwargs – Not used.

execute_training_loop(trn: torch.utils.data.dataloader.DataLoader, dev: torch.utils.data.dataloader.DataLoader, epochs, criterion, optimizer, metric, save_dir, logger: logging.Logger, devices, ratio_width=None, patience=0.5, eval_trn=True, **kwargs)[source]

Implement this to run training loop.

Parameters

  • trn – Training set.

  • dev – Development set.

  • epochs – Number of epochs.

  • criterion – Loss function.

  • optimizer – Optimizer(s).

  • metric – Metric(s)

  • save_dir – The directory to save this component.

  • logger – Logger for reporting progress.

  • devices – Devices this component and dataloader will live on.

  • ratio_width – The width of dataset size measured in number of characters. Used for logger to align messages.

  • **kwargs – Other hyper-parameters passed from sub-class.

fit(trn_data, dev_data, save_dir, transformer: hanlp.layers.embeddings.contextual_word_embedding.ContextualWordEmbedding, sampler_builder=None, mix_embedding: int = 13, layer_dropout: int = 0.1, n_mlp_arc=768, n_mlp_rel=256, mlp_dropout=0.33, lr=0.001, transformer_lr=2.5e-05, patience=0.1, batch_size=32, epochs=30, gradient_accumulation=1, adam_epsilon=1e-08, weight_decay=0, warmup_steps=0.1, grad_norm=1.0, tree=False, proj=False, punct=False, logger=None, verbose=True, devices: Optional[Union[float, int, List[int]]] = None, **kwargs)[source]

Fit to data, triggers the training procedure. For training set and dev set, they shall be local or remote files.

Parameters

  • trn_data – Training set.

  • dev_data – Development set.

  • save_dir – The directory to save trained component.

  • batch_size – The number of samples in a batch.

  • epochs – Number of epochs.

  • devices – Devices this component will live on.

  • logger – Any logging.Logger instance.

  • seed – Random seed to reproduce this training.

  • finetune – True to load from save_dir instead of creating a randomly initialized component. str to specify a different save_dir to load from.

  • eval_trn – Evaluate training set after each update. This can slow down the training but provides a quick diagnostic for debugging.

  • _device_placeholder – True to create a placeholder tensor which triggers PyTorch to occupy devices so other components won’t take these devices as first choices.

  • **kwargs – Hyperparameters used by sub-classes.

Returns

Any results sub-classes would like to return. Usually the best metrics on training set.

fit_dataloader(trn: torch.utils.data.dataloader.DataLoader, criterion, optimizer, metric: hanlp.metrics.mtl.MetricDict, logger: logging.Logger, history: hanlp.common.structure.History, gradient_accumulation=1, grad_norm=None, ratio_width=None, eval_trn=True, **kwargs)[source]

Fit onto a dataloader.

Parameters

  • trn – Training set.

  • criterion – Loss function.

  • optimizer – Optimizer.

  • metric – Metric(s).

  • logger – Logger for reporting progress.

  • **kwargs – Other hyper-parameters passed from sub-class.

predict(data: Union[List[str], List[List[str]]], batch_size: Optional[int] = None, **kwargs)[source]

Predict on data fed by user. Users shall avoid directly call this method since it is not guarded with torch.no_grad and will introduces unnecessary gradient computation. Use __call__ instead.

Parameters

  • *args – Sentences or tokens.

  • **kwargs – Used in sub-classes.

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