ChunkEvaluator¶
该接口使用mini-batch的chunk_eval累计的counter numbers，来计算准确率、召回率和F1值。ChunkEvaluator有三个状态num_infer_chunks，num_label_chunks和num_correct_chunks，分别对应语块数目、标签中的语块数目、正确识别的语块数目。对于chunking的基础知识，请参考 https://www.aclweb.org/anthology/N01-1025 。ChunkEvalEvaluator计算块检测（chunk detection）的准确率，召回率和F1值，支持IOB, IOE, IOBES和IO标注方案。
返回¶
初始化后的 ChunkEvaluator 对象
返回类型¶
ChunkEvaluator
代码示例¶
import paddle.fluid as fluid
# init the chunk-level evaluation manager
metric = fluid.metrics.ChunkEvaluator()
# suppose the model predict 10 chucks, while 8 ones are correct and the ground truth has 9 chucks.
num_infer_chunks = 10
num_label_chunks = 9
num_correct_chunks = 8
metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks)
numpy_precision, numpy_recall, numpy_f1 = metric.eval()
print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1))
# the next batch, predicting 3 perfectly correct chucks.
num_infer_chunks = 3
num_label_chunks = 3
num_correct_chunks = 3
metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks)
numpy_precision, numpy_recall, numpy_f1 = metric.eval()
print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1))
方法¶
update(num_infer_chunks, num_label_chunks, num_correct_chunks)¶
该函数使用输入的(num_infer_chunks, num_label_chunks, num_correct_chunks)来累计更新ChunkEvaluator对象的对应状态，更新方式如下：
\[\begin{split}\\ \begin{array}{l}{\text { self. num_infer_chunks }+=\text { num_infer_chunks }} \\ {\text { self. num_Label_chunks }+=\text { num_label_chunks }} \\ {\text { self. num_correct_chunks }+=\text { num_correct_chunks }}\end{array} \\\end{split}\]
参数
num_infer_chunks (int|numpy.array) – 给定mini-batch的语块数目。
num_label_chunks (int|numpy.array) - 给定mini-batch的标签中的语块数目。
num_correct_chunks （int|numpy.array）— 给定mini-batch的正确识别的语块数目。
返回 无