def train_ner(output_dir: str,
train_data_path: str,
dev_data_path: str,
test_data_path: str,
run_test: bool = None,
model: str = None,
n_iter: int = 10,
meta_overrides: str = None):
util.fix_random_seed(util.env_opt("seed", 0))
train_data = read_ner_from_tsv(train_data_path)
dev_data = read_ner_from_tsv(dev_data_path)
test_data = read_ner_from_tsv(test_data_path)
os.makedirs(output_dir, exist_ok=True)
if run_test:
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
evaluate_ner(nlp,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
else:
train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides)
def train_ner(output_dir: str,
train_data_path: str,
dev_data_path: str,
test_data_path: str,
run_test: bool = None,
model: str = None,
n_iter: int = 10,
meta_overrides: str = None):
model = "en_core_sci_sm"
util.fix_random_seed(util.env_opt("seed", 0))
with Path(train_data_path).open('rb') as file:
train_data = pickle.load(file)
with Path(dev_data_path).open('rb') as file:
dev_data = pickle.load(file)
with Path(test_data_path).open('rb') as file:
test_data = pickle.load(file)
# train_data = read_ner_from_tsv(train_data_path)
# dev_data = read_ner_from_tsv(dev_data_path)
# test_data = read_ner_from_tsv(test_data_path)
os.makedirs(output_dir, exist_ok=True)
if run_test:
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
evaluate_ner(nlp,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
else:
train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides)
def retrain_model(project, model=None, n_iter=30):
"""Load the model, set up the pipeline and train the entity recognizer."""
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
util.env_opt('dropout_to', 0.2),
util.env_opt('dropout_decay', 0.0))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 16),
util.env_opt('batch_compound', 1.001))
if model == 'model_1':
output_model = 'model_2'
else:
output_model = 'model_1'
al_project = ContentType.objects.get(app_label="spacyal",
model="al_project").model_class()
project = al_project.objects.get(pk=project)
base_d = '/'.join(project.texts.path.split('/')[:-1])
output_dir = os.path.join(base_d, output_model)
if project.len_training_data() < project.num_retrain:
message = {'folder': os.path.join(base_d, model),
'retrained': False, 'project': project.pk}
return message
TRAIN_DATA, eval_data, hist_object = project.get_training_data(
include_all=True, include_negative=True)
nlp = spacy.load(os.path.join(base_d, model))# load existing spaCy model
if project.project_history_set.all().count() == 1:
project_history = ContentType.objects.get(
app_label="spacyal", model="project_history").model_class()
ev = test_model(eval_data, nlp)
f1 = ev.compute_f1()
hist2 = project_history.objects.create(
project=project, eval_f1=f1['fbeta'],
eval_precission=f1['precission'], eval_recall=f1['recall'])
hist2.cases_training.add(*list(hist_object.cases_training.all()))
hist2.cases_evaluation.add(*list(hist_object.cases_evaluation.all()))
TRAIN_DATA = mix_train_data(nlp, TRAIN_DATA)
with open(os.path.join(base_d, 'training_data.json'), 'w') as outp:
json.dump(TRAIN_DATA, outp)
r = nlp.get_pipe('ner')
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
count_prog_list = list(range(0, n_iter, int(n_iter/10)))
with nlp.disable_pipes(*other_pipes):
optimizer = nlp.begin_training()
for itn in range(n_iter):
if itn in count_prog_list:
current_task.update_state(
state='PROGRESS',
meta={'progress': count_prog_list.index(itn)*10,
'model': output_model, 'project': project.pk})
random.shuffle(TRAIN_DATA)
losses = {}
for batch in util.minibatch(TRAIN_DATA, size=batch_sizes):
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
drop=next(dropout_rates), # dropout - make it harder to memorise data
sgd=optimizer, # callable to update weights
losses=losses)
if not Path(output_dir).exists():
Path(output_dir).mkdir()
nlp.to_disk(output_dir)
print(eval_data)
ev = test_model(eval_data, nlp)
f1 = ev.compute_f1()
hist_object.eval_f1 = f1['fbeta']
hist_object.eval_precission = f1['precission']
hist_object.eval_recall = f1['recall']
hist_object.model_path = output_dir
hist_object.save()
message = {'folder': output_dir, 'retrained': True, 'project': project.pk, 'f1': f1}
return message
def Model(cls, nr_class, **cfg):
depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
subword_features = util.env_opt('subword_features',
cfg.get('subword_features', True))
conv_depth = util.env_opt('conv_depth', cfg.get('conv_depth', 4))
conv_window = util.env_opt('conv_window', cfg.get('conv_depth', 1))
t2v_pieces = util.env_opt('cnn_maxout_pieces',
cfg.get('cnn_maxout_pieces', 3))
bilstm_depth = util.env_opt('bilstm_depth', cfg.get('bilstm_depth', 0))
self_attn_depth = util.env_opt('self_attn_depth',
cfg.get('self_attn_depth', 0))
assert depth == 1
parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
cfg.get('maxout_pieces', 2))
token_vector_width = util.env_opt('token_vector_width',
cfg.get('token_vector_width', 96))
hidden_width = util.env_opt('hidden_width',
cfg.get('hidden_width', 64))
embed_size = util.env_opt('embed_size', cfg.get('embed_size', 2000))
tok2vec = get_t2v(token_vector_width,
embed_size,
conv_depth=conv_depth,
conv_window=conv_window,
cnn_maxout_pieces=t2v_pieces,
subword_features=subword_features,
bilstm_depth=bilstm_depth)
tok2vec = chain(tok2vec, flatten)
tok2vec.nO = token_vector_width
lower = PrecomputableAffine(hidden_width,
nF=cls.nr_feature,
nI=token_vector_width,
nP=parser_maxout_pieces)
lower.nP = parser_maxout_pieces
with Model.use_device('cpu'):
upper = Affine(nr_class, hidden_width, drop_factor=0.0)
upper.W *= 0
cfg = {
'nr_class': nr_class,
'hidden_depth': depth,
'token_vector_width': token_vector_width,
'hidden_width': hidden_width,
'maxout_pieces': parser_maxout_pieces,
'pretrained_vectors': None,
'bilstm_depth': bilstm_depth,
'self_attn_depth': self_attn_depth,
'conv_depth': conv_depth,
'conv_window': conv_window,
'embed_size': embed_size,
'cnn_maxout_pieces': t2v_pieces
}
return ParserModel(tok2vec, lower, upper), cfg
def train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank('en') # create blank Language class
print("Created blank 'en' model")
if meta_overrides is not None:
metadata = json.load(open(meta_overrides))
nlp.meta.update(metadata)
original_tokenizer = nlp.tokenizer
nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names and "parser" in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, after="parser")
elif 'ner' not in nlp.pipe_names and "tagger" in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, after="tagger")
elif 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
# otherwise, get it so we can add labels
else:
ner = nlp.get_pipe('ner')
# add labels
for _, annotations in train_data:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
util.env_opt('dropout_to', 0.2),
util.env_opt('dropout_decay', 0.005))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 32),
util.env_opt('batch_compound', 1.001))
with nlp.disable_pipes(*other_pipes):
optimizer = nlp.begin_training()
best_epoch = 0
best_f1 = 0
for i in range(n_iter):
random.shuffle(train_data)
count = 0
losses = {}
total = len(train_data)
with nlp.disable_pipes(*other_pipes): # only train NER
with tqdm.tqdm(total=total, leave=True) as pbar:
for batch in minibatch(train_data, size=batch_sizes):
docs, golds = zip(*batch)
nlp.update(docs,
golds,
sgd=optimizer,
losses=losses,
drop=next(dropout_rates))
pbar.update(len(batch))
if count % 100 == 0 and count > 0:
print('sum loss: %s' % losses['ner'])
count += 1
# save model to output directory
output_dir_path = Path(output_dir + "/" + str(i))
if not output_dir_path.exists():
output_dir_path.mkdir()
with nlp.use_params(optimizer.averages):
nlp.tokenizer = original_tokenizer
nlp.to_disk(output_dir_path)
print("Saved model to", output_dir_path)
# test the saved model
print("Loading from", output_dir_path)
nlp2 = util.load_model_from_path(output_dir_path)
nlp2.tokenizer = WhitespaceTokenizer(nlp.vocab)
metrics = evaluate_ner(nlp2, dev_data)
if metrics["f1-measure-overall"] > best_f1:
best_f1 = metrics["f1-measure-overall"]
best_epoch = i
# save model to output directory
best_model_path = Path(output_dir + "/" + "best")
print(f"Best Epoch: {best_epoch} of {n_iter}")
if os.path.exists(best_model_path):
shutil.rmtree(best_model_path)
shutil.copytree(os.path.join(output_dir, str(best_epoch)), best_model_path)
# test the saved model
print("Loading from", best_model_path)
nlp2 = util.load_model_from_path(best_model_path)
nlp2.tokenizer = WhitespaceTokenizer(nlp.vocab)
evaluate_ner(nlp2,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp2,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
def main(model='en',
new_model_name='en-animals',
output_dir=animal_model,
use_gpu=-1,
n_iter=20):
if model_exists(output_dir):
print('model exists.')
test_model(output_dir, use_gpu)
return
"""Set up the pipeline and entity recognizer, and train the new entity."""
if model is not None:
print("Loading model '%s' ... " % model)
if (use_gpu >= 0):
spacy.util.use_gpu(0)
nlp = spacy.load(model) # load existing spaCy model
else:
print("Creating blank 'en' model ... ")
nlp = spacy.blank('en') # create blank Language class
# Add entity recognizer to model if it's not in the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner)
else:
# otherwise, get it, so we can add labels to it
ner = nlp.get_pipe('ner')
ner.add_label(LABEL) # add new entity label to entity recognizer
print('begin training... ')
if model is None:
optimizer = nlp.begin_training(device=use_gpu)
else:
# Note that 'begin_training' initializes the models, so it'll zero out
# existing entity types.
optimizer = nlp.entity.create_optimizer()
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = decaying(env_opt('dropout_from', 0.6),
env_opt('dropout_to', 0.2),
env_opt('dropout_decay', 1e-4))
batch_sizes = compounding(env_opt('batch_from', 15),
env_opt('batch_to', 30),
env_opt('batch_compound', 1.005))
# disable other pipes during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes): # only train NER
n_train_words = count_train()
for i in range(n_iter):
losses = {}
random.shuffle(TRAIN_DATA)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
for batch in minibatch(TRAIN_DATA, size=batch_sizes):
texts, annotations = zip(*batch)
nlp.update(texts,
annotations,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses)
pbar.update(count_tokens(texts))
print('{}/{} loss: {}'.format(i + 1, n_iter, losses))
for text in test_texts:
doc = nlp(text)
print("Entities in '%s'" % text)
for ent in doc.ents:
print(ent.label_, ent.text)
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.meta['name'] = new_model_name # rename model
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
# test the saved model
test_model(output_dir, use_gpu)
def custom_train(
lang,
output_path,
train_path,
dev_path,
raw_text=None,
base_model=None,
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
orth_variant_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
textcat_multilabel=False,
textcat_arch="bow",
textcat_positive_label=None,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
import tqdm
msg = Printer()
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
output_path = util.ensure_path(output_path)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path) if meta_path else {}
if output_path.exists() and [
p for p in output_path.iterdir() if p.is_dir()
]:
msg.warn(
"Output directory is not empty",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if not output_path.exists():
output_path.mkdir()
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 100.0),
util.env_opt("batch_to", 1000.0),
util.env_opt("batch_compound", 1.001),
)
if not eval_beam_widths:
eval_beam_widths = [1]
else:
eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
if 1 not in eval_beam_widths:
eval_beam_widths.append(1)
eval_beam_widths.sort()
has_beam_widths = eval_beam_widths != [1]
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text("Training pipeline: {}".format(pipeline))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(
"Model language ('{}') doesn't match language specified as "
"`lang` argument ('{}') ".format(nlp.lang, lang),
exits=1,
)
nlp.disable_pipes([p for p in nlp.pipe_names if p not in pipeline])
for pipe in pipeline:
if pipe not in nlp.pipe_names:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
else:
if pipe == "textcat":
textcat_cfg = nlp.get_pipe("textcat").cfg
base_cfg = {
"exclusive_classes": textcat_cfg["exclusive_classes"],
"architecture": textcat_cfg["architecture"],
"positive_label": textcat_cfg["positive_label"],
}
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if base_cfg != pipe_cfg:
msg.fail(
"The base textcat model configuration does"
"not match the provided training options. "
"Existing cfg: {}, provided cfg: {}".format(
base_cfg, pipe_cfg),
exits=1,
)
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
### Here are our modifications:
lang_cls.Defaults.tag_map = custom_tag_map
nlp = lang_cls()
assert nlp.vocab.morphology.n_tags == 36
###
for pipe in pipeline:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
if vectors:
msg.text("Loading vector from model '{}'".format(vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks),
("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail("Can't use multitask objective without '{}' in the "
"pipeline".format(pipe_name))
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text("Counting training words (limit={})".format(n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples,
device=use_gpu)
nlp._optimizer = None
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Verify textcat config
if "textcat" in pipeline:
textcat_labels = nlp.get_pipe("textcat").cfg["labels"]
if textcat_positive_label and textcat_positive_label not in textcat_labels:
msg.fail(
"The textcat_positive_label (tpl) '{}' does not match any "
"label in the training data.".format(textcat_positive_label),
exits=1,
)
if textcat_positive_label and len(textcat_labels) != 2:
msg.fail(
"A textcat_positive_label (tpl) '{}' was provided for training "
"data that does not appear to be a binary classification "
"problem with two labels.".format(textcat_positive_label),
exits=1,
)
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
gold_preproc=gold_preproc,
max_length=0,
ignore_misaligned=True,
)
train_labels = set()
if textcat_multilabel:
multilabel_found = False
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1:
multilabel_found = True
if not multilabel_found and not base_model:
msg.warn("The textcat training instances look like they have "
"mutually-exclusive classes. Remove the flag "
"'--textcat-multilabel' to train a classifier with "
"mutually-exclusive classes.")
if not textcat_multilabel:
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1 and not base_model:
msg.warn(
"Some textcat training instances do not have exactly "
"one positive label. Modifying training options to "
"include the flag '--textcat-multilabel' for classes "
"that are not mutually exclusive.")
nlp.get_pipe("textcat").cfg["exclusive_classes"] = False
textcat_multilabel = True
break
if base_model and set(textcat_labels) != train_labels:
msg.fail(
"Cannot extend textcat model using data with different "
"labels. Base model labels: {}, training data labels: "
"{}.".format(textcat_labels, list(train_labels)),
exits=1,
)
if textcat_multilabel:
msg.text(
"Textcat evaluation score: ROC AUC score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels)))
elif textcat_positive_label and len(textcat_labels) == 2:
msg.text("Textcat evaluation score: F1-score for the "
"label '{}'".format(textcat_positive_label))
elif len(textcat_labels) > 1:
if len(textcat_labels) == 2:
msg.warn(
"If the textcat component is a binary classifier with "
"exclusive classes, provide '--textcat_positive_label' for "
"an evaluation on the positive class.")
msg.text(
"Textcat evaluation score: F1-score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels)))
else:
msg.fail(
"Unsupported textcat configuration. Use `spacy debug-data` "
"for more information.")
# fmt: off
row_head, output_stats = _configure_training_output(
pipeline, use_gpu, has_beam_widths)
row_widths = [len(w) for w in row_head]
row_settings = {
"widths": row_widths,
"aligns": tuple(["r" for i in row_head]),
"spacing": 2
}
# fmt: on
print("")
msg.row(row_head, **row_settings)
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
gold_preproc=gold_preproc,
max_length=0,
ignore_misaligned=True,
)
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs,
size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
if not int(os.environ.get("LOG_FRIENDLY", 0)):
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
for beam_width in eval_beam_widths:
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(
nlp_loaded,
gold_preproc=gold_preproc,
ignore_misaligned=True,
))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(
epoch_model_path)
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(
nlp_loaded,
gold_preproc=gold_preproc,
ignore_misaligned=True,
))
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs,
verbose=verbose)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
srsly.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
if beam_width == 1:
meta["speed"] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["accuracy"] = scorer.scores
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
meta["beam_accuracy"][beam_width] = scorer.scores
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta["labels"] = nlp.meta["labels"]
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
progress = _get_progress(
i,
losses,
scorer.scores,
output_stats,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
if i == 0 and "textcat" in pipeline:
textcats_per_cat = scorer.scores.get(
"textcats_per_cat", {})
for cat, cat_score in textcats_per_cat.items():
if cat_score.get("roc_auc_score", 0) < 0:
msg.warn(
"Textcat ROC AUC score is undefined due to "
"only one value in label '{}'.".format(
cat))
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= n_early_stopping:
msg.text("Early stopping, best iteration "
"is: {}".format(i - iter_since_best))
msg.text("Best score = {}; Final iteration "
"score = {}".format(best_score,
current_score))
break
finally:
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(meta, output_path,
nlp.pipe_names)
msg.good("Created best model", best_model_path)
def train(pretrained,
output_dir,
train_data,
dev_data,
n_iter=30,
n_sents=0,
parser_multitasks='',
entity_multitasks='',
use_gpu=-1,
no_tagger=False,
no_parser=False,
no_entities=False,
gold_preproc=False,
version="0.0.0",
meta_path=None,
verbose=False):
"""
Re-train a pre-trained model. Expects data in spaCy's JSON
format. This code is based on
https://github.com/explosion/spaCy/blob/master/spacy/cli/train.py.
"""
# There is a bug that prevents me from using the GPU when resuming
# training from a saved model. See
# https://github.com/explosion/spaCy/issues/1806.
if use_gpu >= 0:
msg = "\nWARNING: using GPU may require re-installing thinc. "
msg += "See https://github.com/explosion/spaCy/issues/1806.\n"
print(msg)
util.fix_random_seed()
util.set_env_log(True)
n_sents = n_sents or None
output_path = util.ensure_path(output_dir)
train_path = util.ensure_path(train_data)
dev_path = util.ensure_path(dev_data)
meta_path = util.ensure_path(meta_path)
if not output_path.exists():
output_path.mkdir()
if not train_path.exists():
prints(train_path, title=Messages.M050, exits=1)
if dev_path and not dev_path.exists():
prints(dev_path, title=Messages.M051, exits=1)
if meta_path is not None and not meta_path.exists():
prints(meta_path, title=Messages.M020, exits=1)
meta = util.read_json(meta_path) if meta_path else {}
if not isinstance(meta, dict):
prints(Messages.M053.format(meta_type=type(meta)),
title=Messages.M052,
exits=1)
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
util.env_opt('dropout_to', 0.2),
util.env_opt('dropout_decay', 0.0))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 16),
util.env_opt('batch_compound', 1.001))
max_doc_len = util.env_opt('max_doc_len', 5000)
corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
n_train_words = corpus.count_train()
# Load pre-trained model. Remove components that we are not
# re-training.
nlp = load(pretrained)
if no_tagger and 'tagger' in nlp.pipe_names:
nlp.remove_pipe('tagger')
if no_parser and 'parser' in nlp.pipe_names:
nlp.remove_pipe('parser')
if no_entities and 'ner' in nlp.pipe_names:
nlp.remove_pipe('ner')
meta.setdefault('name', 'unnamed')
meta['pipeline'] = nlp.pipe_names
meta.setdefault('lang', nlp.lang)
nlp.meta.update(meta)
# Add multi-task objectives
if parser_multitasks:
for objective in parser_multitasks.split(','):
nlp.parser.add_multitask_objective(objective)
if entity_multitasks:
for objective in entity_multitasks.split(','):
nlp.entity.add_multitask_objective(objective)
# Get optimizer
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None
print(nlp.pipe_names)
print(nlp.pipeline)
print(
"Itn. Dep Loss NER Loss UAS NER P. NER R. NER F. Tag % Token % CPU WPS GPU WPS"
)
try:
train_docs = corpus.train_docs(nlp,
projectivize=True,
noise_level=0.0,
gold_preproc=gold_preproc,
max_length=0)
train_docs = list(train_docs)
for i in range(n_iter):
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in minibatch(train_docs, size=batch_sizes):
batch = [(d, g) for (d, g) in batch
if len(d) < max_doc_len]
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses)
pbar.update(sum(len(doc) for doc in docs))
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ('model%d' % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device('cpu'):
nlp_loaded = util.load_model_from_path(
epoch_model_path)
dev_docs = list(
corpus.dev_docs(nlp_loaded,
gold_preproc=gold_preproc))
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = (output_path / ('model%d' % i) / 'accuracy.json')
with acc_loc.open('w') as file_:
file_.write(json_dumps(scorer.scores))
meta_loc = output_path / ('model%d' % i) / 'meta.json'
meta['accuracy'] = scorer.scores
meta['speed'] = {
'nwords': nwords,
'cpu': cpu_wps,
'gpu': gpu_wps
}
meta['vectors'] = {
'width': nlp.vocab.vectors_length,
'vectors': len(nlp.vocab.vectors),
'keys': nlp.vocab.vectors.n_keys
}
meta['lang'] = nlp.lang
meta['pipeline'] = nlp.pipe_names
meta['spacy_version'] = '>=%s' % about.__version__
meta.setdefault('name', 'model%d' % i)
meta.setdefault('version', version)
with meta_loc.open('w') as file_:
file_.write(json_dumps(meta))
util.set_env_log(True)
print_progress(i,
losses,
scorer.scores,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps)
finally:
print("Saving model...")
with nlp.use_params(optimizer.averages):
final_model_path = output_path / 'model-final'
nlp.to_disk(final_model_path)
