So Im using Spacy 3.0 to train basically all pipelines with custom data. Weirdly enough Spacy seems to source the entities of the NER pipeline from another model, possibly en_core_web_md. So if I type the sentence "Who is Barrack Obama" into spacy it detects the name as a PERSON entity, which is not a behavior I want since I only want to work with my own entitites.
I cant seem to find why it would source the entities though, here is my config:
[paths]
train = "trainer/data/sent_train.spacy"
dev = "trainer/data/sent_train.spacy"
vectors = "en_core_web_md"
init_tok2vec = "en_core_web_md"
[system]
gpu_allocator = null
seed = 0
[nlp]
lang = "en"
pipeline = ["tok2vec","tagger","parser","ner","textcat_multilabel"]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
[components]
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.parser]
source = "en_core_web_md"
[components.tagger]
source = "en_core_web_md"
[components.textcat_multilabel]
factory = "textcat_multilabel"
scorer = {"@scorers":"spacy.textcat_multilabel_scorer.v1"}
threshold = 0.5
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
nO = null
[components.tok2vec]
source = "en_core_web_md"
[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"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_epochs = 0
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]
tag_acc = 0.25
dep_uas = 0.12
dep_las = 0.12
dep_las_per_type = null
sents_p = null
sents_r = null
sents_f = 0.0
ents_f = 0.25
ents_p = 0.0
ents_r = 0.0
ents_per_type = null
cats_score = 0.25
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
[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]
Since the [components.ner] section says factory = "ner". It should create a blank new one right? So why are there already entity types in my model? I do have to mention here that I want other components like the parser or tagger to be sourced from the already trained model, so there is no mistake here