Can't run the spacy spancat (spancategorizer) model? - python-3.x

Question

I am trying to train the spancat model without luck.
I am getting:
ValueError: [E143] Labels for component 'spancat' not initialized. This can be fixed by calling add_label, or by providing a representative batch of examples to the component's 'initialize' method.
I did convert my NER ents to spans:
def main(loc: Path, lang: str, span_key: str):
"""
Set the NER data into the doc.spans, under a given key.
The SpanCategorizer component uses the doc.spans, so that it can work with
overlapping or nested annotations, which can't be represented on the
per-token level.
"""
nlp = spacy.blank(lang)
docbin = DocBin().from_disk(loc)
docs = list(docbin.get_docs(nlp.vocab))
for doc in docs:
doc.spans[span_key] = list(doc.ents)
DocBin(docs=docs).to_disk(loc)
Here is my config file:
[paths]
train = null
dev = null
vectors = null
init_tok2vec = null
[system]
gpu_allocator = null
seed = 444
[nlp]
lang = "en"
pipeline = ["tok2vec","spancat"]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"#tokenizers":"spacy.Tokenizer.v1"}
[components]
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"#scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.spancat.model]
#architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
#layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
#layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
#architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[components.spancat.suggester]
#misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
#architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
#architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
rows = [5000,1000,2500,2500]
include_static_vectors = true
[components.tok2vec.model.encode]
#architectures = "spacy.MaxoutWindowEncoder.v2"
width = 256
depth = 8
window_size = 1
maxout_pieces = 3
[corpora]
[corpora.dev]
#readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[corpora.train]
#readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
max_epochs = 70
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
[training.batcher]
#batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
get_length = null
[training.batcher.size]
#schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0
[training.logger]
#loggers = "spacy.ConsoleLogger.v1"
progress_bar = false
[training.optimizer]
#optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
learn_rate = 0.001
[training.score_weights]
spans_sc_f = 1.0
spans_sc_p = 0.0
spans_sc_r = 0.0
[pretraining]
[initialize]
vectors = ${paths.vectors}
init_tok2vec = ${paths.init_tok2vec}
vocab_data = null
lookups = null
before_init = null
after_init = null
[initialize.components]
[initialize.tokenizer]
I am using the "sc" key. Please advise how to solve it.

Answer 1

I have solved it using the following function, but one should address the spans Span(doc, start, end, label) according to the project/text for their task. It worked for me because all the text (a few words in my case) are labeled with a label and this is my need.
def convert_to_docbin(input, output_path="./train.spacy", lang='en'):
""" Convert a pair of text annotations into DocBin then save """
# Load a new spacy model:
nlp = spacy.blank(lang)
# Create a DocBin object:
db = DocBin()
for text, annotations in input: # Data in previous format
doc = nlp(text)
ents = []
spans = []
for start, end, label in annotations: # Add character indexes
spans.append(Span(doc, 0, len(doc), label=label))
span = doc.char_span(start, end, label=label)
ents.append(span)
doc.ents = ents # Label the text with the ents
group = SpanGroup(doc, name="sc", spans=spans)
doc.spans["sc"] = group
db.add(doc)
db.to_disk(output_path)