I am trying to train my NER model using spaCy. I tried to generate my config file using the command python -m spacy init config spacy_split_ouput/def_base_confog_en.cfg --lang en --pipeline ner --optimize efficiency, but I keep getting the following error:
⚠ To generate a more effective transformer-based config (GPU-only),
install the spacy-transformers package and re-run this command. The config
generated now does not use transformers.
ℹ Generated config template specific for your use case
- Language: en
- Pipeline: ner
- Optimize for: efficiency
- Hardware: CPU
- Transformer: None
✘ Config validation error
training -> optimizer instance of Optimizer expected
nlp -> tokenizer Promise(registry='tokenizers', name='spacy.Tokenizer.v1', args=[], kwargs={}) is not callable
nlp -> vectors Promise(registry='vectors', name='spacy.Vectors.v1', args=[], kwargs={}) is not callable
{'components': {}, 'corpora': {'dev': {'@readers': 'spacy.Corpus.v1', 'path': None, 'max_length': 0, 'gold_preproc': False, 'limit': 0, 'augmenter': None}, 'train': {'@readers': 'spacy.Corpus.v1', 'path': None, 'max_length': 0, 'gold_preproc': False, 'limit': 0, 'augmenter': None}}, 'initialize': {'vectors': None, 'init_tok2vec': None, 'vocab_data': None, 'lookups': None, 'before_init': None, 'after_init': None, 'components': {}, 'tokenizer': {}}, 'nlp': {'lang': 'en', 'pipeline': ['tok2vec', 'ner'], 'batch_size': 1000, 'disabled': [], 'before_creation': None, 'after_creation': None, 'after_pipeline_creation': None, 'tokenizer': {'@tokenizers': 'spacy.Tokenizer.v1'}, 'vectors': {'@vectors': 'spacy.Vectors.v1'}}, 'paths': {'train': None, 'dev': None, 'vectors': None, 'init_tok2vec': None}, 'system': {'gpu_allocator': None, 'seed': 0}, 'training': {'dev_corpus': 'corpora.dev', 'train_corpus': 'corpora.train', 'seed': 0, 'gpu_allocator': None, 'dropout': 0.1, 'accumulate_gradient': 1, 'patience': 1600, 'max_epochs': 0, 'max_steps': 20000, 'eval_frequency': 200, 'frozen_components': [], 'annotating_components': [], 'before_to_disk': None, 'before_update': None, 'logger': {'@loggers': 'spacy.ConsoleLogger.v1'}, 'batcher': {'@batchers': 'spacy.batch_by_words.v1', 'discard_oversize': False, 'tolerance': 0.2, 'size': {'@schedules': 'compounding.v1', 'start': 100, 'stop': 1000, 'compound': 1.001}}, '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': 1e-08, 'learn_rate': 0.001}, 'score_weights': {}}}
I even tried using the widget but I get the same error as I try to train the model.
For instance, this is my config file after using the widgtet:
train = spacy_split_output/train.spacy
dev = spacy_split_output/dev.spacy
vectors = null
init_tok2vec = null
[system]
gpu_allocator = null
seed = 0
[nlp]
lang = "en"
pipeline = ["tok2vec","ner"]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
vectors = {"@vectors":"spacy.Vectors.v1"}
[components]
[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 100
[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.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[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 = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
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"
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
before_update = 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]
ents_f = 1.0
ents_p = 0.0
ents_r = 0.0
ents_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]
After getting that, I got the folliwng message, python -m spacy init fill-config base_config.cfg config.cfg ✔ Auto-filled config with all values ✔ Saved config config.cfg You can now add your data and train your pipeline: python -m spacy train config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy. Upon doing this, I got the same error as mentioned above.
Any help would be great! Thanks.