pos

pos

Part-of-speech tagging.

class hanlp.components.mtl.tasks.pos.TransformerTagging(trn: Optional[str] = None, dev: Optional[str] = None, tst: Optional[str] = None, sampler_builder: Optional[hanlp.common.dataset.SamplerBuilder] = None, dependencies: Optional[str] = None, scalar_mix: Optional[hanlp.layers.scalar_mix.ScalarMixWithDropoutBuilder] = None, use_raw_hidden_states=False, lr=0.001, separate_optimizer=False, cls_is_bos=False, sep_is_eos=False, max_seq_len=None, sent_delimiter=None, char_level=False, hard_constraint=False, crf=False, token_key='token', dict_tags: Optional[Union[hanlp_trie.dictionary.DictInterface, Dict[Union[str, Sequence[str]], Union[str, Sequence[str]]]]] = None, **kwargs)[source]

A simple tagger using a linear layer with an optional CRF (Lafferty et al. 2001) layer for any tagging tasks including PoS tagging and many others. It also features with a custom dictionary dict_tags to perform longest-prefix-matching which replaces matched tokens with given tags.

Note

For algorithm beginners, longest-prefix-matching is the prerequisite to understand what dictionary can do and what it can’t do. The tutorial in this book can be very helpful.

Parameters

  • trn – Path to training set.

  • dev – Path to dev set.

  • tst – Path to test set.

  • sampler_builder – A builder which builds a sampler.

  • dependencies – Its dependencies on other tasks.

  • scalar_mix – A builder which builds a ScalarMixWithDropout object.

  • use_raw_hidden_states – Whether to use raw hidden states from transformer without any pooling.

  • lr – Learning rate for this task.

  • separate_optimizer – Use customized separate optimizer for this task.

  • cls_is_bos – True to treat the first token as BOS.

  • sep_is_eos – True to treat the last token as EOS.

  • max_seq_len – Sentences longer than max_seq_len will be split into shorter ones if possible.

  • sent_delimiter – Delimiter between sentences, like period or comma, which indicates a long sentence can be split here.

  • char_level – Whether the sequence length is measured at char level, which is never the case for lemmatization.

  • hard_constraint – Whether to enforce hard length constraint on sentences. If there is no sent_delimiter in a sentence, it will be split at a token anyway.

  • crf – True to enable CRF (Lafferty et al. 2001).

  • token_key – The key to tokens in dataset. This should always be set to token in MTL.

  • dict_tags – A custom dictionary to override predicted tags by performing longest-prefix-matching.

  • **kwargs – Not used.

build_dataloader(data, transform: Optional[Callable] = None, training=False, device=None, logger: Optional[logging.Logger] = None, cache=False, gradient_accumulation=1, **kwargs) → torch.utils.data.dataloader.DataLoader[source]

Build a dataloader for training or evaluation.

Parameters

  • data – Either a path or a list of samples.

  • transform – The transform from MTL, which is usually [TransformerSequenceTokenizer, FieldLength(‘token’)]

  • training – Whether this method is called on training set.

  • device – The device dataloader is intended to work with.

  • logger – Logger for printing message indicating progress.

  • cache – Whether the dataloader should be cached.

  • gradient_accumulation – Gradient accumulation to be passed to sampler builder.

  • **kwargs – Additional experimental arguments.

build_metric(**kwargs)[source]

Implement this to build metric(s).

Parameters

**kwargs – The subclass decides the method signature.

build_model(encoder_size, training=True, **kwargs) → torch.nn.modules.module.Module[source]

Build model.

Parameters

  • training – True if called during training.

  • **kwargs – **self.config.

input_is_flat(data) → bool[source]

Check whether the data is flat (meaning that it’s only a single sample, not even batched).

Returns

True to indicate the input data is flat.

Return type

bool

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