Problem with nI dimension when using spancat

[mikelgda]

I have created a pipeline for span categorization using `spacy init --pipeline "spancat" --optimize "accuracy". Upon running, I get an error saying that the labels haven't been initialized. For this, I have added an initialization block at the end of the config file, which looks like this

[paths]
train = "spans/spans_train.spacy"
dev = "spans/spans_test.spacy"
vectors = null
init_tok2vec = null

[system]
gpu_allocator = "pytorch"
seed = 0

[nlp]
lang = "en"
pipeline = ["transformer","spancat"]
batch_size = 128
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
vectors = {"@vectors":"spacy.Vectors.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-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@layers":"reduce_mean.v1"}
upstream = "*"

[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]

[components.transformer]
factory = "transformer"
max_batch_items = 4096
set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}

[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "roberta-base"
mixed_precision = false

[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96

[components.transformer.model.grad_scaler_config]

[components.transformer.model.tokenizer_config]
use_fast = true

[components.transformer.model.transformer_config]

[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]
accumulate_gradient = 3
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
before_update = null

[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
get_length = null

[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

[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 0.00005

[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.components.spancat]
labels = ["Domain", "Skill", "Occupation", "Qualification", "Experience"]

[initialize.tokenizer]

However, I get the following error:

Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\__main__.py", line 4, in <module>
    setup_cli()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\cli\_util.py", line 87, in setup_cli
    command(prog_name=COMMAND)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\click\core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\typer\core.py", line 743, in main
    return _main(
           ^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\typer\core.py", line 198, in _main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\click\core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\click\core.py", line 783, in invoke
    return __callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\typer\main.py", line 698, in wrapper
    return callback(**use_params)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\cli\train.py", line 54, in train_cli
    train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\cli\train.py", line 81, in train
    nlp = init_nlp(config, use_gpu=use_gpu)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\training\initialize.py", line 95, in init_nlp
    nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\language.py", line 1352, in initialize
    proc.initialize(get_examples, nlp=self, **p_settings)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\spacy\pipeline\spancat.py", line 682, in initialize
    self.model.initialize()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 318, in initialize
    self.init(self, X=X, Y=Y)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\layers\chain.py", line 74, in init
    layer.initialize()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 318, in initialize
    self.init(self, X=X, Y=Y)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\layers\chain.py", line 74, in init
    layer.initialize()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 318, in initialize
    self.init(self, X=X, Y=Y)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\layers\chain.py", line 74, in init
    layer.initialize()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 318, in initialize
    self.init(self, X=X, Y=Y)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\layers\chain.py", line 74, in init
    layer.initialize()
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 318, in initialize
    self.init(self, X=X, Y=Y)
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\layers\maxout.py", line 80, in init
    W_shape = (model.get_dim("nO"), model.get_dim("nP"), model.get_dim("nI"))
                                                         ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\mikel\AppData\Local\mambaforge\envs\nlp\Lib\site-packages\thinc\model.py", line 194, in get_dim
    raise ValueError(err)
ValueError: Cannot get dimension 'nI' for model 'maxout': value unset

which I haven't seen referenced anywhere. Why can this be? Also, I thought you could use init labels and get the label data automatically, but it won't let me do this because it says the labels are not initialized.

Thank you.