Textcat with tok2vec specification but no vectors?

[python3Berg]

Looking at one of the example projects, I am struggling to understand the impact of the provided config file. There is specified a

[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

but there are no vectors specified. This may be really embarrassing question, but what good is tok2vec without vec? I have trained some models using the example from projects/pipelines/textcat_demo/...but the results aren't great in real usage and the false positives suggest more a Bag of Words that any CNN or Ensemble model.

I have no intuition whats going on. Getting much better results in spacy2.3 starting with spacy.blank() and manually adding Gold annotations. It would be really helpful if someone could help me understand the moving parts of this example and how the lack of provided vectors is incorporated into training internals. Below I've included the example config from above tutorial for convenience.

[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
seed = 0
gpu_allocator = null

[nlp]
lang = "en"
pipeline = ["textcat"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 1000
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}

[components]

[components.textcat]
factory = "textcat"
threshold = 0.5

[components.textcat.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null

[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v1"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
nO = null

[components.textcat.model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

[components.textcat.model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = 64
rows = [2000,2000,1000,1000,1000,1000]
attrs = ["ORTH","LOWER","PREFIX","SUFFIX","SHAPE","ID"]
include_static_vectors = false

[components.textcat.model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 64
window_size = 1
maxout_pieces = 3
depth = 2

[corpora]

[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[training]
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1000
max_epochs = 0
max_steps = 1000
eval_frequency = 100
frozen_components = []
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
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]
cats_score_desc = null
cats_micro_p = null
cats_micro_r = null
cats_micro_f = null
cats_macro_p = null
cats_macro_r = null
cats_macro_f = null
cats_macro_auc = null
cats_f_per_type = null
cats_macro_auc_per_type = null
cats_score = 1.0

[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]

[python3Berg]

More reading before I waste teams time...

[svlandeg]

Haha, no worries. Next time, feel free to also post what was the issue and how you solved it, it might help someone else down the line when they come here through search results! ;-)