Unpacking Error Training Custom NER

[vrunm]

from spacy.util import minibatch, compounding
from spacy.training import Example
import random

n_iter = 10
for i in range(n_iter):
    random.shuffle(TRAIN_DATA)
    losses = {}
    batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
    for batch in batches:
        texts = []
        annotations = []
        for text, annotation in batch:
            texts.append(text)
            annotations.append(annotation)
        examples = [Example.from_dict(nlp.make_doc(text), annotation) for text, annotation in zip(texts, annotations)]
        nlp.update(examples, losses=losses)
    print("Losses:", losses)

While training the custom NER model I am getting this error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_27/4118121174.py in <module>
     11         texts = []
     12         annotations = []
---> 13         for text, annotation in batch:
     14             texts.append(text)
     15             annotations.append(annotation)

ValueError: too many values to unpack (expected 2)

Is there any way around it?

[kadarakos]

Hey there,

It seems like that as you iterate through the elements of your TRAIN_DATA each element contains more elements than two:

In [1]: a, b = [1, 2]

In [2]: a, b = [1, 2, 3]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 a, b = [1, 2, 3]

ValueError: too many values to unpack (expected 2)

In [3]: a, b = [1]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[3], line 1
----> 1 a, b = [1]

ValueError: not enough values to unpack (expected 2, got 1)