chunk¶

class ChunkEvaluator(label_list, suffix=False)[源代码]

ChunkEvaluator computes the precision, recall and F1-score for chunk detection. It is often used in sequence tagging tasks, such as Named Entity Recognition(NER).

• label_list (list) -- The label list.

• suffix (bool) -- if set True, the label ends with '-B', '-I', '-E' or '-S', else the label starts with them.

compute(lengths, predictions, labels, dummy=None)[源代码]

Computes the precision, recall and F1-score for chunk detection.

• lengths (tensor) -- The valid length of every sequence, a tensor with shape [batch_size]

• predictions (tensor) -- The predictions index, a tensor with shape [batch_size, sequence_length].

• labels (tensor) -- The labels index, a tensor with shape [batch_size, sequence_length].

• dummy (tensor, optional) -- Unnecessary parameter for compatibility with older versions with parameters list inputs, lengths, predictions, labels. Defaults to None.

the number of the inference chunks. num_label_chunks (tensor): the number of the label chunks. num_correct_chunks (tensor): the number of the correct chunks.

num_infer_chunks (tensor)

update(num_infer_chunks, num_label_chunks, num_correct_chunks)[源代码]

This function takes (num_infer_chunks, num_label_chunks, num_correct_chunks) as input, to accumulate and update the corresponding status of the ChunkEvaluator object. The update method is as follows:

$\begin{split}\\ \begin{array}{l}{\text { self. num_infer_chunks }+=\text { num_infer_chunks }} \\ {\text { self. num_Label_chunks }+=\text { num_label_chunks }} \\ {\text { self. num_correct_chunks }+=\text { num_correct_chunks }}\end{array} \\\end{split}$

• num_infer_chunks (int|numpy.array) -- The number of chunks in Inference on the given minibatch.

• num_label_chunks (int|numpy.array) -- The number of chunks in Label on the given mini-batch.

• num_correct_chunks (int|float|numpy.array) -- The number of chunks both in Inference and Label on the given mini-batch.

accumulate()[源代码]

This function returns the mean precision, recall and f1 score for all accumulated minibatches.

mean precision, recall and f1 score.

float

reset()[源代码]

Reset function empties the evaluation memory for previous mini-batches.

name()[源代码]

Return name of metric instance.