biaffine_ner

biaffine_ner

Biaffine Named Entity Recognition.

class hanlp.components.mtl.tasks.ner.biaffine_ner.BiaffineNamedEntityRecognition(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=None, separate_optimizer=False, doc_level_offset=True, is_flat_ner=True, tagset=None, ret_tokens=' ', ffnn_size=150, loss_reduction='mean', **kwargs)[source]

An implementation of Named Entity Recognition as Dependency Parsing (Yu et al. 2020). It treats every possible span as a candidate of entity and predicts its entity label. Non-entity spans are assigned NULL label to be excluded. The label prediction is done with a biaffine layer (Dozat & Manning 2017). As it makes no assumption about the spans, it naturally supports flat NER and nested NER.

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.

  • doc_level_offset – True to indicate the offsets in jsonlines are of document level.

  • is_flat_ner – True for flat NER, otherwise nested NER.

  • tagset – Optional tagset to prune entities outside of this tagset from datasets.

  • ret_tokens – A delimiter between tokens in entities so that the surface form of an entity can be rebuilt.

  • ffnn_size – Feedforward size for MLPs extracting the head/tail representations.

  • loss_reduction – The loss reduction used in aggregating losses.

  • **kwargs – Not used.

build_dataloader(data, transform: Optional[hanlp.common.transform.TransformList] = None, training=False, device=None, logger: Optional[logging.Logger] = None, 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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