claf.model.token_classification package

Submodules

class claf.model.token_classification.mixin.TokenClassification

Bases: object

Token Classification Mixin Class

make_metrics(predictions)

Make metrics with prediction dictionary

• Args:

  predictions: prediction dictionary consisting of

  • key: ‘id’ (sequence id)

  • value: dictionary consisting of

    • tag_idxs

• Returns:

  metrics: metric dictionary consisting of

  • ‘accuracy’: sequence level accuracy

  • ‘tag_accuracy’: tag level accuracy

  • ‘macro_f1’: tag prediction macro(unweighted mean) f1

  • ‘macro_precision’: tag prediction macro(unweighted mean) precision

  • ‘macro_recall’: tag prediction macro(unweighted mean) recall

make_predictions(output_dict)

Make predictions with model’s output_dict

• Args:

  output_dict: model’s output dictionary consisting of

  • sequence_embed: embedding vector of the sequence

  • tag_logits: representing unnormalized log probabilities of the tag

  • tag_idxs: target tag idxs

  • data_idx: data idx

  • loss: a scalar loss to be optimized

• Returns:

  predictions: prediction dictionary consisting of

  • key: ‘id’ (sequence id)

  • value: dictionary consisting of

    • tag_idxs

predict(**kwargs)

print_examples(index, inputs, predictions)

Print evaluation examples

• Args:

  index: data index inputs: mini-batch inputs predictions: prediction dictionary consisting of

  • key: ‘id’ (sequence id)

  • value: dictionary consisting of

    • class_idx

• Returns:

  print(Sequence, Target Tags, Target Slots, Predicted Tags, Predicted Slots)

write_predictions(predictions, file_path=None, is_dict=True, pycm_obj=None)

Override write_predictions() in ModelBase to log confusion matrix

Module contents

class claf.model.token_classification.BertForTokCls(token_makers, num_tags, ignore_tag_idx, pretrained_model_name=None, dropout=0.2)

Bases: claf.model.token_classification.mixin.TokenClassification, claf.model.base.ModelWithoutTokenEmbedder

Implementation of Single Sentence Tagging model presented in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (https://arxiv.org/abs/1810.04805)

• Args:

  token_embedder: used to embed the sequence num_tags: number of classified tags ignore_tag_idx: index of the tag to ignore when calculating loss (tag pad value)

• Kwargs:

  pretrained_model_name: the name of a pre-trained model dropout: classification layer dropout

forward(features, labels=None)

• Args:

  features: feature dictionary like below. {

  “bert_input”: {

  “feature”: [

  [100, 576, 21, 45, 7, 91, 101, 0, 0, …], …,

  ]

  } “token_type”: {

  “feature”: [

  [0, 0, 0, 0, 0, 0, 0, 0, 0, …], …,

  ]

  }, “tagged_sub_token_idxs”: {

  [

  [1, 3, 4, 0, 0, 0, 0, 0, 0, …], …,

  ]

  }

  }

• Kwargs:

  label: label dictionary like below. {

  “class_idx”: [2, 1, 0, 4, 5, …] “data_idx”: [2, 4, 5, 7, 2, 1, …]

  } Do not calculate loss when there is no label. (inference/predict mode)

• Returns: output_dict (dict) consisting of

  • sequence_embed: embedding vector of the sequence

  • tag_logits: representing unnormalized log probabilities of the tags.

  • tag_idxs: target class idx

  • data_idx: data idx

  • loss: a scalar loss to be optimized

print_examples(index, inputs, predictions)

Print evaluation examples

• Args:

  index: data index inputs: mini-batch inputs predictions: prediction dictionary consisting of

  • key: ‘id’ (sequence id)

  • value: dictionary consisting of

    • class_idx

• Returns:

  print(Sequence, Sequence Tokens, Target Tags, Target Slots, Predicted Tags, Predicted Slots)