def _test_biluov_task():
import es_core_news_md
from scripts.utils import Sentence
def forward(tokensxsentence, entitiesxsentence):
labelsxsentence, _ = to_biluov(tokensxsentence, entitiesxsentence)
return [
from_biluov(biluov, sentence, spans=True)
for biluov, sentence in zip(labelsxsentence, tokensxsentence)
]
training = Collection().load(Path("data/training/scenario.txt"))
nlp = es_core_news_md.load()
def per_label(label):
tokensxsentence = [nlp(s.text) for s in training.sentences]
entitiesxsentence = [[
k.spans for k in s.keyphrases if k.label == label
] for s in training.sentences]
decoded = forward(tokensxsentence, entitiesxsentence)
return decoded
collection = Collection([Sentence(s.text) for s in training.sentences])
for label in ENTITIES:
decoded = per_label(label)
for entities, sentence in zip(decoded, collection.sentences):
for spans in entities:
keyphrase = Keyphrase(sentence, label, -1, spans)
sentence.keyphrases.append(keyphrase)
collection.fix_ids()
output = Path(
"data/submissions/forward-biluov/train/run1/scenario2-taskA/")
output.mkdir(parents=True, exist_ok=True)
collection.dump(output / "scenario.txt", skip_empty_sentences=False)
def main(gold_input, submit_input, skip_A, skip_B, verbose, skip_C=True):
gold = Collection()
gold.load(gold_input)
submit = Collection()
submit.load(submit_input)
data = OrderedDict()
dataA = subtaskA(gold, submit, verbose)
data.update(dataA)
if not skip_A:
report(dataA, verbose)
if not skip_B:
dataB = subtaskB(gold, submit, dataA, verbose)
data.update(dataB)
report(dataB, verbose)
if not skip_C:
dataC = subtaskC(gold, submit, data, verbose)
data.update(dataC)
report(dataC, verbose)
print("-" * 20)
metrics = compute_metrics(data, skip_A, skip_B, skip_C)
for key, value in metrics.items():
print("{0}: {1:0.4}".format(key, value))
return data
def _load_collection(self, scenario):
gold = self.gold.format(scenario)
gold = Path(gold)
loader = Collection().load_dir if gold.is_dir() else Collection().load
return loader(
gold,
legacy=False,
keyphrases=scenario.endswith("-taskB"),
relations=False,
attributes=False,
)
def get_clean_collection(anns_path: Path, select: str) -> Collection:
collection = Collection()
for file in sorted((anns_path / select).iterdir()):
if file.suffix == ".txt":
collection.load(file, attributes=False)
for s in collection.sentences:
overlaps = s.overlapping_keyphrases()
if overlaps:
print("Found overlapping:", overlaps)
s.merge_overlapping_keyphrases()
overlaps = s.overlapping_keyphrases()
dups = s.dup_relations()
if dups:
print(
"Found duplicated relations %r in sentence '%s'"
% ([v[0] for v in dups.values()], s.text)
)
s.remove_dup_relations()
dups = s.dup_relations()
assert not overlaps
assert not dups
return collection
def load_corpus(anns_path: Path, clean=True) -> Collection:
collection = Collection()
for file in sorted(anns_path.iterdir()):
if file.name.endswith(".txt"):
collection.load(file)
if clean:
for s in collection.sentences:
overlaps = s.overlapping_keyphrases()
if overlaps:
print("Found overlapping:", overlaps)
s.merge_overlapping_keyphrases()
overlaps = s.overlapping_keyphrases()
dups = s.dup_relations()
if dups:
print("Found duplicated relations %r in sentence '%s'" %
([v[0] for v in dups.values()], s.text))
s.remove_dup_relations()
dups = s.dup_relations()
assert not overlaps
assert not dups
return collection
def count_labels_based_on(path: Path, reference: Path):
collection = load_corpus(path)
reference = CollectionV1Handler.load_dir(Collection(), reference)
sentences = []
for ref_sent in reference.sentences:
for sent in collection.sentences:
if sent.text == ref_sent.text:
sentences.append(sent)
break
print(len(collection))
print(len(reference))
print(len(sentences))
return count_labels_on(Collection(sentences))
def main():
baseline = Baseline()
baseline.train(Path("data/training/"))
collection = CollectionV1Handler.load_dir(
Collection(), Path("data/testing/scenario2-taskA/"))
output = baseline.run(collection, taskA=True, taskB=False)
CollectionV1Handler.dump(output, Path("pepe/input_scenario2.txt"), False)
def load_and_dump_from_corpus(path2sentences, path2corpus, path2output):
sentences = path2sentences.read_text().splitlines()
print(len(sentences))
collection = Collection().load_dir(path2corpus, legacy=True, attributes=False)
print(len(collection))
collection = filter(collection, sentences)
print(len(collection))
collection.dump(path2output)
def test(self, finput: Path, skip_A=False, skip_B=False):
collection = Collection()
collection.load(finput)
self.predict_entities(collection)
return collection
def load_training_data(corpus) -> Collection:
packs = Path("/data") / corpus / "packs/submitted/"
collection = Collection()
for filename in packs.glob("*.txt"):
collection.load(filename)
return collection
def task_annotate_relations(corpus: str, pack: str):
model = get_model(corpus)
text_path = Path("/data") / corpus / "packs" / "open" / pack / "pack.txt"
collection = Collection().load(text_path)
collection = model.predict_relations(collection)
collection.dump(text_path, skip_empty_sentences=False)
return {"reload": True}
def get_train_valid_set(self,
finput_train: Path,
finput_valid: Path = None):
#TRAIN SET
finput_train = Path(finput_train)
collection_train = (Collection().load_dir(finput_train)
if finput_train.is_dir() else
Collection().load(finput_train))
#VALIDATION SET
if finput_valid:
finput_valid = Path(finput_valid)
collection_valid = (Collection().load_dir(finput_valid)
if finput_valid.is_dir() else
Collection().load(finput_valid))
else:
collection_valid = None
return collection_train, collection_valid
def filter(collection: Collection, sentences):
# return Collection([s for s in collection.sentences if s.text in sentences])
def find(text):
for s in collection.sentences:
if s.text == text:
return s
raise Exception("Not found! " + text)
return Collection([find(text) for text in sentences])
def _load_collection(self, scenario):
gold = self.gold.format(scenario)
return Collection().load(
Path(gold),
legacy=False,
keyphrases=scenario.endswith("-taskB"),
relations=False,
attributes=False,
)
def train(self, finput: Path):
collection = (Collection().load_dir(finput)
if finput.is_dir() else Collection().load(finput))
self.model = keyphrases, relations = {}, {}
for sentence in collection.sentences:
for keyphrase in sentence.keyphrases:
text = keyphrase.text.lower()
keyphrases[text] = keyphrase.label
for sentence in collection.sentences:
for relation in sentence.relations:
origin = relation.from_phrase
origin_text = origin.text.lower()
destination = relation.to_phrase
destination_text = destination.text.lower()
relations[origin_text, origin.label, destination_text,
destination.label] = relation.label
def task_clear_all(corpus: str, pack: str):
path = Path("/data") / corpus / "packs" / "open" / pack / "pack.txt"
collection = Collection()
collection.load(path)
for sentence in collection.sentences:
sentence.relations = []
collection.dump(path)
return {"reload": True}
def test(self, finput: Path, skip_A, skip_B):
collection = Collection()
if skip_A:
collection.load_keyphrases(finput)
else:
collection.load_input(finput)
self.predict_entities(collection)
if not skip_B:
for sentence in collection.sentences:
self.predict_relations(sentence)
sentence.remove_dup_relations()
return collection
def main(anns_path: Path, training_path, develop_path, test_path, public):
random.seed(42) # default seed, but each generator should use his own
# dump training and development collections ----------------------------------
train_develop_sentences = get_training_and_development(anns_path)
#### training
training = Collection(train_develop_sentences[:800])
training.dump(training_path / "scenario.txt")
#### development/main
develop = Collection(train_develop_sentences[800:])
develop.dump(develop_path / "main" / "scenario.txt")
# dump test collection (per scenario) ----------------------------------------
test_sentences = get_test(anns_path)
extra_sentences_main = get_extra(anns_path, "main",
train_develop_sentences, test_sentences)
extra_sentences_transfer = get_extra(anns_path, "transfer",
train_develop_sentences,
test_sentences)
#### test/scenario3
scn3 = Collection(test_sentences[200:])
clean(scn3, public, remove_keyphrases=False)
scn3.dump(test_path / "scenario3-taskB" / "scenario.txt", False)
#### test/scenario2
scn2 = Collection(test_sentences[100:200])
clean(scn2, public)
scn2.dump(test_path / "scenario2-taskA" / "scenario.txt", False)
#### test/scenario1
scn1 = Collection(
shuffle(extra_sentences_main[:4900], test_sentences[:100]))
clean(scn1, public)
scn1.dump(test_path / "scenario1-main" / "scenario.txt", False)
# dump transfer learning collections ----------------------------------------
transfer_sentences = get_transfer(anns_path)
#### development/transfer
develop_transfer = Collection(transfer_sentences[:100])
develop_transfer.dump(develop_path / "transfer" / "scenario.txt")
#### test/scenario4
scn4 = Collection(
shuffle(extra_sentences_transfer[:1400], transfer_sentences[100:]))
clean(scn4, public)
scn4.dump(test_path / "scenario4-transfer" / "scenario.txt", False)
def predict_entities(self, sentences):
if isinstance(sentences[0], Sentence):
sentences = [s.text for s in sentences]
result = []
nlp = spacy_model("es")
for i, sentence in enumerate(sentences):
if self.callback:
self.callback(msg="Processing sentence",
current=i,
total=len(sentences))
doc, xs = self.feature_sentence(sentence)
sentence = self.predict_single(doc, xs)
result.append(sentence)
return Collection(sentences=result)
def evaluate_scenario(submit_path: Path, gold: Collection, scenario: int):
submit_file = submit_path / ("scenario.txt")
if not submit_file.exists():
warnings.warn("Input file not found in '%s'" % submit_path)
return {}
submit = Collection().load(submit_file)
resultA = subtaskA(gold, submit)
resultB = subtaskB(gold, submit, resultA)
results = {}
for k, v in list(resultA.items()) + list(resultB.items()):
results[k] = len(v)
metrics = compute_metrics(dict(resultA, **resultB),
skipA=scenario == 3,
skipB=scenario == 2)
results.update(metrics)
return results
def train(self, finput):
collection = Collection()
collection.load(finput)
self.keyphrases.clear()
for sentence in collection.sentences:
for keyphrase in sentence.keyphrases:
text = keyphrase.text.lower()
self.keyphrases[text] = keyphrase.label
self.relations.clear()
for sentence in collection.sentences:
for relation in sentence.relations:
origin = relation.from_phrase
origin_text = origin.text.lower()
destination = relation.to_phrase
destination_text = destination.text.lower()
self.relations[origin_text, origin.label, destination_text, destination.label] = relation.label
def main(anns_path: Path, training_path, develop_path, test_path):
random.seed(42) # default seed, but each generator should use his own
# dump training and development collections ----------------------------------
train_develop_sentences = get_training_and_development(anns_path)
#### training
training = Collection(train_develop_sentences[:800])
training.dump(training_path / "scenario.txt")
#### development/main
develop = Collection(train_develop_sentences[800:])
develop.dump(develop_path / "main" / "scenario.txt")
# dump test collection (per scenario) ----------------------------------------
test_sentences = get_test(anns_path)
extra_sentences = get_extra(anns_path, train_develop_sentences, test_sentences)
#### test/scenario3
scn3 = Collection(test_sentences[200:])
scn3.dump(test_path / "scenario3-taskB" / "scenario.txt")
#### test/scenario2
scn2 = Collection(test_sentences[100:200])
scn2.dump(test_path / "scenario2-taskA" / "scenario.txt")
#### test/scenario1
scn1 = Collection(
extra_sentences[:4567] + test_sentences[:100] + extra_sentences[4567:]
)
scn1.dump(test_path / "scenario1-main" / "scenario.txt", False)
# dump transfer learning collections ----------------------------------------
transfer_sentences = get_transfer(anns_path)
#### development/transfer
develop_transfer = Collection(transfer_sentences[:100])
develop_transfer.dump(develop_path / "transfer" / "scenario.txt")
#### test/scenario4
scn4 = Collection(transfer_sentences[100:])
scn4.dump(test_path / "scenario4-transfer" / "scenario.txt")
def train(self, finput: Path):
collection = Collection()
collection = CollectionV1Handler.load(collection,
finput / "input_training.txt")
# collection = CollectionV2Handler.load(collection, finput / 'ensemble.txt')
self.model = keyphrases, relations = {}, {}
for sentence in collection.sentences:
for keyphrase in sentence.keyphrases:
text = keyphrase.text.lower()
keyphrases[text] = keyphrase.label
for sentence in collection.sentences:
for relation in sentence.relations:
origin = relation.from_phrase
origin_text = origin.text.lower()
destination = relation.to_phrase
destination_text = destination.text.lower()
relations[origin_text, origin.label, destination_text,
destination.label] = relation.label
def train(self, finput):
collection = Collection()
collection.load(finput)
"""
full_text = ""
for sentence in collection.sentences:
full_text += sentence.text
full_text += "\n"
doc = es_pipeline.nlp(full_text)
"""
x_train, y_train = self.preprocess(collection, True)
# print('Counter train: %s' % Counter(y_train))
fit_result = self.clf.fit(x_train, y_train)
print("Success at training!\n")
self.label_set = list(set(y_train))
return fit_result
def count_labels(path: Path, handler=None):
corpus = handler.load_dir(Collection(),
path) if handler else load_corpus(path)
return count_labels_on(corpus)
def main(
mode="test",
best=False,
single=False,
csv=False,
pretty=False,
final=False,
plain=False,
compact=False,
gold="data",
submit="data/submissions",
ignore=False,
):
users = collections.defaultdict(list)
if csv and not best:
raise ValueError("Error: --csv implies --best")
if final and (not csv or not best):
raise ValueError("Error: --final implies --csv and --best")
if mode == "test":
test_gold = Path(gold)
gold_scenarios = [
Collection().load(test_gold /
"testing/scenario1-main/scenario.txt"),
Collection().load(test_gold /
"testing/scenario2-taskA/scenario.txt"),
Collection().load(test_gold /
"testing/scenario3-taskB/scenario.txt"),
Collection().load(test_gold /
"testing/scenario4-transfer/scenario.txt"),
]
elif mode == "dev":
dev_gold = Path(gold)
gold_scenarios = [
Collection().load(dev_gold / "development/main/scenario.txt"),
Collection().load(dev_gold / "development/main/scenario.txt"),
Collection().load(dev_gold / "development/main/scenario.txt"),
Collection().load(dev_gold / "development/transfer/scenario.txt"),
]
elif mode == "train":
dev_gold = Path(gold)
gold_scenarios = [
Collection().load(dev_gold / "training/scenario.txt"),
Collection().load(dev_gold / "training/scenario.txt"),
Collection().load(dev_gold / "training/scenario.txt"),
]
else:
raise ValueError("Unexpected mode: {0}".format(mode))
submits = Path(submit)
if single:
submits = submits / single
runs = submits / mode
if not runs.exists():
msg = "Directory {0} not found. Check --mode and --single options.".format(
runs)
raise ValueError(msg)
ensure_number_of_runs(runs)
for subfolder in runs.iterdir():
users[submits.name].append(evaluate_one(subfolder,
*gold_scenarios))
else:
for userfolder in submits.iterdir():
if not userfolder.is_dir():
continue
runs = userfolder / mode
if not runs.exists():
msg = "Directory {0} not found. Did you mean to use --single? Check --mode option.".format(
runs)
if ignore:
warnings.warn(msg)
continue
else:
raise ValueError(msg)
ensure_number_of_runs(runs)
for subfolder in runs.iterdir():
users[userfolder.name].append(
evaluate_one(subfolder, *gold_scenarios))
results = dict(users)
if best:
results = filter_best(results)
if csv:
import pandas as pd
items = []
for user, data in results.items():
userdata = dict(name=user)
for k, metrics in data.items():
userdata.update(
{"%s-%s" % (k, m): v
for m, v in metrics.items()})
items.append(userdata)
df = pd.DataFrame(items)
df = df.set_index("name").sort_index().transpose()
if final:
df1 = df.transpose()[[
"scenario1-f1", "scenario1-precision", "scenario1-recall"
]]
df1 = df1.sort_values("scenario1-f1", ascending=False)
df2 = df.transpose()[[
"scenario2-f1", "scenario2-precision", "scenario2-recall"
]]
df2 = df2.sort_values("scenario2-f1", ascending=False)
df3 = df.transpose()[[
"scenario3-f1", "scenario3-precision", "scenario3-recall"
]]
df3 = df3.sort_values("scenario3-f1", ascending=False)
df4 = df.transpose()[[
"scenario4-f1", "scenario4-precision", "scenario4-recall"
]]
df4 = df4.sort_values("scenario4-f1", ascending=False)
if pretty:
print(df1.round(3).to_markdown() + "\n")
print(df2.round(3).to_markdown() + "\n")
print(df3.round(3).to_markdown() + "\n")
print(df4.round(3).to_markdown() + "\n")
else:
print(df1.to_csv())
print(df2.to_csv())
print(df3.to_csv())
print(df4.to_csv())
else:
print(df.to_csv())
elif plain:
for user, info in results.items():
print(50 * "=")
print(" {0} ".format(user).center(50, ":").upper())
print(50 * "=")
for run in info:
print("[ {0} ]".format(run["submit"]).center(50, "-"))
for scenario, data in run.items():
if scenario == "submit":
continue
print("> {0} ".format(scenario))
for metric, value in data.items():
if metric == "submit":
continue
metric = "{0}".format(metric).ljust(15)
if isinstance(value, float):
print(" {0} ~ {1:0.4}".format(metric, value))
else:
print(" {0} = {1}".format(metric, value))
elif compact:
if not single:
raise ValueError("--compact requires --single")
if not best:
raise ValueError("--compact requires --best")
results = results[single]
for scn, metrics in results.items():
for m in ["f1", "precision", "recall"]:
print(f"{scn}-{m}: {metrics[m]:0.5}")
else:
print(json.dumps(results, sort_keys=True,
indent=2 if pretty else None))
def main(
mode="test",
best=False,
single=False,
csv=False,
pretty=False,
final=False,
plain=False,
):
users = collections.defaultdict(list)
if csv and not best:
raise ValueError("Error: --csv implies --best")
if final and (not csv or not best):
raise ValueError("Error: --final implies --csv and --best")
if mode == "test":
scn1_gold = Collection().load(
Path("data/testing/scenario1-main/scenario.txt"))
scn2_gold = Collection().load(
Path("data/testing/scenario2-taskA/scenario.txt"))
scn3_gold = Collection().load(
Path("data/testing/scenario3-taskB/scenario.txt"))
scn4_gold = Collection().load(
Path("data/testing/scenario4-transfer/scenario.txt"))
elif mode == "dev":
scn1_gold = Collection().load(
Path("data/development/main/scenario.txt"))
scn2_gold = Collection().load(
Path("data/development/main/scenario.txt"))
scn3_gold = Collection().load(
Path("data/development/main/scenario.txt"))
scn4_gold = Collection().load(
Path("data/development/transfer/scenario.txt"))
else:
raise ValueError("Unexpected mode: {0}".format(mode))
submits = Path("data/submissions/")
if single:
submits = submits / single
runs = submits / mode
if not runs.exists():
raise ValueError(
"Directory {0} not found. Check --mode and --single options.")
ensure_number_of_runs(runs)
for subfolder in runs.iterdir():
users[submits.name].append(
evaluate_one(
subfolder,
scn1_gold,
scn2_gold,
scn3_gold,
scn4_gold,
))
else:
for userfolder in submits.iterdir():
if not userfolder.is_dir():
continue
runs = userfolder / mode
if not runs.exists():
raise ValueError(
"Directory {0} not found. Did you mean to use --single? Check --mode option."
)
ensure_number_of_runs(runs)
for subfolder in runs.iterdir():
users[userfolder.name].append(
evaluate_one(
subfolder,
scn1_gold,
scn2_gold,
scn3_gold,
scn4_gold,
))
results = dict(users)
if best:
results = filter_best(results)
if csv:
import pandas as pd
items = []
for user, data in results.items():
userdata = dict(name=user)
for k, metrics in data.items():
userdata.update(
{"%s-%s" % (k, m): v
for m, v in metrics.items()})
items.append(userdata)
df = pd.DataFrame(items)
df = df.set_index("name").sort_index().transpose()
if final:
df1 = df.transpose()[[
"scenario1-f1", "scenario1-precision", "scenario1-recall"
]]
df1 = df1.sort_values("scenario1-f1", ascending=False).to_csv()
df2 = df.transpose()[[
"scenario2-f1", "scenario2-precision", "scenario2-recall"
]]
df2 = df2.sort_values("scenario2-f1", ascending=False).to_csv()
df3 = df.transpose()[[
"scenario3-f1", "scenario3-precision", "scenario3-recall"
]]
df3 = df3.sort_values("scenario3-f1", ascending=False).to_csv()
df4 = df.transpose()[[
"scenario4-f1", "scenario4-precision", "scenario4-recall"
]]
df4 = df4.sort_values("scenario4-f1", ascending=False).to_csv()
print(df1)
print(df2)
print(df3)
print(df4)
elif pretty:
print(df.to_html())
else:
print(df.to_csv())
elif plain:
for user, info in results.items():
print(50 * "=")
print(" {0} ".format(user).center(50, ":").upper())
print(50 * "=")
for run in info:
print("[ {0} ]".format(run["submit"]).center(50, "-"))
for scenario, data in run.items():
if scenario == "submit":
continue
print("> {0} ".format(scenario))
for metric, value in data.items():
if metric == "submit":
continue
metric = "{0}".format(metric).ljust(15)
if isinstance(value, float):
print(" {0} ~ {1:0.4}".format(metric, value))
else:
print(" {0} = {1}".format(metric, value))
else:
print(json.dumps(results, sort_keys=True,
indent=2 if pretty else None))
def _training_task(
n_epochs,
*,
bert_mode,
cnet_mode,
ignore_path,
inclusion=1.1,
task=None,
jointly=True,
early_stopping=None,
use_crf=True,
weight=True,
only_bert=False,
reduce=False,
split_relations="both",
straight_forward_encoding=False,
dropout=False,
stacked_layers=1,
):
if split_relations not in ("both", "pair", "seq"):
raise ValueError()
training = Collection().load(Path("data/training/scenario.txt"))
validation = Collection().load(
Path("data/development/main/scenario.txt"))
early_stopping = early_stopping or dict(wait=5, delta=0.0)
train_pairs = (TAXONOMIC_RELS if split_relations == "both" else
RELATIONS if split_relations == "pair" else None)
train_seqs = (CONTEXT_RELS if split_relations == "both" else
RELATIONS if split_relations == "seq" else None)
algorithm = eHealth20Model(
bert_mode=bert_mode,
only_bert=only_bert,
cnet_mode=cnet_mode,
ignore_path=ignore_path,
)
if task is None:
algorithm.train(
training,
validation,
jointly=jointly,
inclusion=inclusion,
n_epochs=n_epochs,
save_to=name_to_path,
early_stopping=early_stopping,
use_crf=use_crf,
weight=weight,
train_pairs=train_pairs,
train_seqs=train_seqs,
straight_forward_encoding=straight_forward_encoding,
reduce=reduce,
dropout=dropout,
stacked_layers=stacked_layers,
)
elif task == "A":
algorithm.train_taskA(
training,
validation,
jointly=jointly,
n_epochs=n_epochs,
save_to=name_to_path,
early_stopping=early_stopping,
use_crf=use_crf,
weight=weight,
dropout=dropout,
stacked_layers=stacked_layers,
)
elif task == "B":
# load A
if jointly:
taskA_models = {}
for label in ENTITIES:
checkpoint = torch.load(f"trained/taskA-{label}.pt")
_ensure_bert(bert_mode, checkpoint)
model = checkpoint["model"]
taskA_models[label] = model
model.eval()
algorithm.taskA_models = taskA_models
algorithm.train_taskB(
training,
validation,
jointly=jointly,
inclusion=inclusion,
n_epochs=n_epochs,
save_to=name_to_path,
early_stopping=early_stopping,
weight=weight,
use_crf=use_crf,
train_pairs=train_pairs,
train_seqs=train_seqs,
straight_forward_encoding=straight_forward_encoding,
reduce=reduce,
dropout=dropout,
)
from pathlib import Path
from scripts.utils import Collection, CollectionV1Handler, CollectionV2Handler
talp = CollectionV1Handler.load(
Collection(),
Path("data/training/talp-576640/scenario1-main/input_scenario1.txt"))
print(f"Talp: {len(talp)}")
ensemble = CollectionV2Handler.load(Collection(),
Path("data/training/ensemble.txt"))
print(f"Ensemble: {len(ensemble)}")
sentences = set([s.text for s in ensemble.sentences])
selection = Collection([s for s in talp.sentences if s.text in sentences])
print(f"Selection: {len(selection)}")
output = Path("data/training/talp.txt")
output.parent.mkdir(exist_ok=True)
CollectionV2Handler.dump(selection, output, skip_empty_sentences=False)
from streamlit.ScriptRunner import StopException
from autobrat.classifier import Model
from scripts.score import compute_metrics, subtaskA, subtaskB
from scripts.utils import (
ENTITIES,
RELATIONS,
Collection,
CollectionV1Handler,
CollectionV2Handler,
Keyphrase,
Relation,
Sentence,
)
c = Collection()
if st.sidebar.checkbox("Original Data", value=False):
c = CollectionV1Handler.load(c, Path("data/training/input_training.txt"))
if st.sidebar.checkbox("Ensemble Data", value=False):
old_size = len(c)
c = CollectionV2Handler.load(c, Path("data/training/ensemble.txt"))
ensemble_size = len(c) - old_size
top_agreement = st.sidebar.number_input("Number of sentences (Ensemble)",
0, ensemble_size, ensemble_size)
c.sentences = c.sentences[:old_size + top_agreement]
if st.sidebar.checkbox("Talp Data", value=False):
old_size = len(c)
c = CollectionV2Handler.load(c, Path("data/training/talp.txt"))