无法运行 spacy spancat (spancategorizer) 模型？

我正在尝试训练 spancat 模型，但运气不佳。 我正进入（状态：

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.

我确实将 NER ent 转换为跨度：

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)

这是我的配置文件：

[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]

我正在使用“sc”键。请指教如何解决。

我已经使用以下函数解决了这个问题，但是应该根据其任务的项目/文本来解决跨度 Span(doc, start, end, label) 的问题。它对我有用，因为所有文本（在我的例子中是几个单词）都标有标签，这是我的需要。

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)