Образец обучающих данных (ввод. json), полный json имеет только 100 резюме.
{"content": "Resume 1 text in french","annotation":[{"label":["diplomes"],"points":[{"start":1233,"end":1423,"text":"1995-1996 : Lycée Dar Essalam Rabat \n Baccalauréat scientifique option sciences Expérimentales "}]},{"label":["diplomes"],"points":[{"start":1012,"end":1226,"text":"1996-1998 : Faculté des Sciences Rabat \n C.E.U.S (Certificat des Etudes universitaires Supérieurs) option physique et chimie "}]},{"label":["diplomes"],"points":[{"start":812,"end":1004,"text":"1999-2000 : Faculté des Sciences Rabat \n Licence es sciences physique option électronique "}]},{"label":["diplomes"],"points":[{"start":589,"end":805,"text":"2002-2004 : Faculté des Sciences Rabat \nDESA ((Diplôme des Etudes Supérieures Approfondies) en informatique \n\ntélécommunication multimédia "}]},{"label":["diplomes"],"points":[{"start":365,"end":582,"text":"2014-2017 : Institut National des Postes et Télécommunications INPT Rabat \n Thèse de doctorat en informatique et télécommunication "}]},{"label":["adresse"],"points":[{"start":122,"end":157,"text":"Rue 34 n 17 Hay Errachad Rabat Maroc"}]}],"extras":null,"metadata":{"first_done_at":1586140561000,"last_updated_at":1586140561000,"sec_taken":0,"last_updated_by":"wP21IMXff9TFSNLNp5v0fxbycFX2","status":"done","evaluation":"NONE"}}
{"content": "Resume 2 text in french","annotation":[{"label":["diplomes"],"points":[{"start":1251,"end":1345,"text":"Lycée Oued El Makhazine - Meknès \n\n- Bachelier mention très bien \n- Option : Sciences physiques"}]},{"label":["diplomes"],"points":[{"start":1122,"end":1231,"text":"Classes préparatoires Moulay Youssef - Rabat \n\n- Admis au Concours National Commun CNC \n- Option : PCSI - PSI "}]},{"label":["diplomes"],"points":[{"start":907,"end":1101,"text":"Institut National des Postes et Télécommunications INPT - Rabat \n\n- Ingénieur d’État en Télécommunications et technologies de l’information \n- Option : MTE Management des Télécoms de l’entreprise"}]},{"label":["adresse"],"points":[{"start":79,"end":133,"text":"94, Hay El Izdihar, Avenue El Massira, Ouislane, MEKNES"}]}],"extras":null,"metadata":{"first_done_at":1586126476000,"last_updated_at":1586325851000,"sec_taken":0,"last_updated_by":"wP21IMXff9TFSNLNp5v0fxbycFX2","status":"done","evaluation":"NONE"}}
{"content": "Resume 3 text in french","annotation":[{"label":["adresse"],"points":[{"start":2757,"end":2804,"text":"N141 Av. El Hansali Agharass \nBouargane \nAgadir "}]},{"label":["diplomes"],"points":[{"start":262,"end":369,"text":"2009-2010 : Baccalauréat Scientifique, option : Sciences Physiques au Lycée Qualifiant \nIBN MAJJA à Agadir."}]},{"label":["diplomes"],"points":[{"start":125,"end":259,"text":"2010-2016 : Diplôme d’Ingénieur d’Etat, option : Génie Informatique, à l’Ecole \nNationale des Sciences Appliquées d’Agadir (ENSAA). "}]}],"extras":null,"metadata":{"first_done_at":1586141779000,"last_updated_at":1586141779000,"sec_taken":0,"last_updated_by":"wP21IMXff9TFSNLNp5v0fxbycFX2","status":"done","evaluation":"NONE"}}
{"content": "Resume 4 text in french","annotation":[{"label":["diplomes"],"points":[{"start":505,"end":611,"text":"2012 Baccalauréat Sciences Expérimentales option Sciences Physiques, Lycée Hassan Bno \nTabit, Ouled Abbou. "}]},{"label":["diplomes"],"points":[{"start":375,"end":499,"text":"2012–2015 Diplôme de licence en Informatique et Gestion Industrielle, IGI, Faculté des sciences \net Techniques, Settat, LST. "}]},{"label":["diplomes"],"points":[{"start":272,"end":367,"text":"2015–2017 Master Spécialité BioInformatique et Systèmes Complexes, BISC, ENSA , Tanger, \n\nBac+5."}]},{"label":["adresse"],"points":[{"start":15,"end":71,"text":"246 Hay Pam Eljadid OULED ABBOU \n26450 BERRECHID, Maroc "}]}],"extras":null,"metadata":{"first_done_at":1586127374000,"last_updated_at":1586327010000,"sec_taken":0,"last_updated_by":"wP21IMXff9TFSNLNp5v0fxbycFX2","status":"done","evaluation":"NONE"}}
{"content": "Resume 5 text in french","annotation":null,"extras":null,"metadata":{"first_done_at":1586139511000,"last_updated_at":1586139511000,"sec_taken":0,"last_updated_by":"wP21IMXff9TFSNLNp5v0fxbycFX2","status":"done","evaluation":"NONE"}}
Код, который преобразует эти json данные в пространственный формат
input_file="input.json"
output_file="output.json"
training_data = []
lines=[]
with open(input_file, 'r', encoding="utf8") as f:
lines = f.readlines()
for line in lines:
data = json.loads(line)
print(data)
text = data['content']
entities = []
for annotation in data['annotation']:
point = annotation['points'][0]
labels = annotation['label']
if not isinstance(labels, list):
labels = [labels]
for label in labels:
entities.append((point['start'], point['end'] + 1 ,label))
training_data.append((text, {"entities" : entities}))
with open(output_file, 'wb') as fp:
pickle.dump(training_data, fp)
Код для обучения пространственной модели
def train_spacy():
TRAIN_DATA = training_data
nlp = spacy.load('fr_core_news_md') # create blank Language class
# 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:
# ner = nlp.create_pipe('ner')
# nlp.add_pipe(ner, last=True)
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']
with nlp.disable_pipes(*other_pipes): # only train NER
optimizer = nlp.begin_training()
for itn in range(20):
print("Statring iteration " + str(itn))
random.shuffle(TRAIN_DATA)
losses = {}
for text, annotations in TRAIN_DATA:
nlp.update(
[text], # batch of texts
[annotations], # batch of annotations
# drop=0.2, # dropout - make it harder to memorise data
sgd=optimizer, # callable to update weights
losses=losses)
print(itn, dt.datetime.now(), losses)
output_dir = "new-model"
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.meta['name'] = "addr_edu" # rename model
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
train_spacy()
Когда я тестирую модель, это именно то, что происходит
import spacy
nlp = spacy.load("new-model")
doc = nlp("Text of a Resume already trained on")
print(doc.ents)
# It prints out this ()
doc = nlp("Text of a Resume not trained on")
print(doc.ents)
# It prints out this ()
Я ожидаю, что это даст мне адрес субъекта (адрес ) и дипломы (Academi c градусов), присутствующие в тексте
Редактировать 1
Пример данных (ввод. json) в самом верху является частью данных, которые я получаю после того, как я аннотирую резюме на платформе текстовых аннотаций.
Но я должен преобразовать его в просторный формат, чтобы я мог дать модели для обучения.
Вот как выглядит резюме с аннотациями, когда Я даю это модели
training_data = [(
'Dr.XXXXXX XXXXXXX \n\n \nEmail : XXXXXXXXXXXXXXXXXXXXXXXX \n\nGSM : XXXXXXXXXX \n\nAdresse : XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX \n \n\n \n\nETAT CIVIL \n \n\nSituation de famille : célibataire \n\nNationalité : Marocaine \n\nNé le : 10 février 1983 \n\nLieu de naissance : XXXXXXXXXXXXXXXX \n\n \n FORMATION \n\n• 2014-2017 : Institut National des Postes et Télécommunications INPT Rabat \n Thèse de doctorat en informatique et télécommunication \n \n\n• 2002-2004 : Faculté des Sciences Rabat \nDESA ((Diplôme des Etudes Supérieures Approfondies) en informatique \n\ntélécommunication multimédia \n \n\n• 1999-2000 : Faculté des Sciences Rabat \n Licence es sciences physique option électronique \n \n\n• 1996-1998 : Faculté des Sciences Rabat \n C.E.U.S (Certificat des Etudes universitaires Supérieurs) option physique et chimie \n \n\n• 1995-1996 : Lycée Dar Essalam Rabat \n Baccalauréat scientifique option sciences Expérimentales \n\nSTAGE DE FORMATION \n\n• Du 03/03/2004 au 17/09/2004 : Stage de Projet de Fin d’Etudes à l’ INPT pour \nl’obtention du DESA (Diplôme des Etudes Supérieures Approfondies). \n\n Sujet : AGENT RMON DANS LA GESTION DE RESEAUX. \n\n• Du 03/06/2002 au 17/01/2003: Stage de Projet de Fin d’année à INPT \n Sujet : Mécanisme d’Authentification Kerbéros Dans un Réseau Sans fils sous Redhat. \n\nPUBLICATION \n\n✓ Ababou, Mohamed, Rachid Elkouch, and Mostafa Bellafkih and Nabil Ababou. "New \n\nstrategy to optimize the performance of epidemic routing protocol." International Journal \n\nof Computer Applications, vol. 92, N.7, 2014. \n\n✓ Ababou, Mohamed, Rachid Elkouch, and Mostafa Bellafkih and Nabil Ababou. "New \n\nStrategy to optimize the Performance of Spray and wait Routing Protocol." International \n\nJournal of Wireless and Mobile Networks v.6, N.2, 2014. \n\n✓ Ababou, Mohamed, Rachid Elkouch, and Mostafa Bellafkih and Nabil Ababou. "Impact of \n\nmobility models on Supp-Tran optimized DTN Spray and Wait routing." International \n\njournal of Mobile Network Communications & Telematics ( IJMNCT), Vol.4, N.2, April \n\n2014. \n\n✓ M. Ababou, R. Elkouch, M. Bellafkih and N. Ababou, "AntProPHET: A new routing \n\nprotocol for delay tolerant networks," Proceedings of 2014 Mediterranean Microwave \n\nSymposium (MMS2014), Marrakech, 2014, IEEE. \n\nmailto:XXXXXXXXXXXXXXXXXXXXXXXX\n\n\n✓ Ababou, Mohamed, et al. "BeeAntDTN: A nature inspired routing protocol for delay \n\ntolerant networks." Proceedings of 2014 Mediterranean Microwave Symposium \n\n(MMS2014). IEEE, 2014. \n\n✓ Ababou, Mohamed, et al. "ACDTN: A new routing protocol for delay tolerant networks \n\nbased on ant colony." Information Technology: Towards New Smart World (NSITNSW), \n\n2015 5th National Symposium on. IEEE, 2015. \n\n✓ Ababou, Mohamed, et al. "Energy-efficient routing in Delay-Tolerant Networks." RFID \n\nAnd Adaptive Wireless Sensor Networks (RAWSN), 2015 Third International Workshop \n\non. IEEE, 2015. \n\n✓ Ababou, Mohamed, et al. "Energy efficient and effect of mobility on ACDTN routing \n\nprotocol based on ant colony." Electrical and Information Technologies (ICEIT), 2015 \n\nInternational Conference on. IEEE, 2015. \n\n✓ Mohamed, Ababou et al. "Fuzzy ant colony based routing protocol for delay tolerant \n\nnetwork." 10th International Conference on Intelligent Systems: Theories and Applications \n\n(SITA). IEEE, 2015. \n\nARTICLES EN COURS DE PUBLICATION \n\n✓ Ababou, Mohamed, Rachid Elkouch, and Mostafa Bellafkih and Nabil Ababou.”Dynamic \n\nUtility-Based Buffer Management Strategy for Delay-tolerant Networks. “International \n\nJournal of Ad Hoc and Ubiquitous Computing, 2017. ‘accepté par la revue’ \n\n✓ Ababou, Mohamed, Rachid Elkouch, and Mostafa Bellafkih and Nabil Ababou. "Energy \n\nefficient routing protocol for delay tolerant network based on fuzzy logic and ant colony." \n\nInternational Journal of Intelligent Systems and Applications (IJISA), 2017. ‘accepté par la \n\nrevue’ \n\nCONNAISSANCES EN INFORMATIQUE \n\n \n\nLANGUES \n\nArabe, Français, anglais. \n\nLOISIRS ET INTERETS PERSONNELS \n\n \n\nVoyages, Photographie, Sport (tennis de table, footing), bénévolat. \n\nSystèmes : UNIX, DOS, Windows \n\nLangages : Séquentiels ( C, Assembleur), Requêtes (SQL), WEB (HTML, PHP, MySQL, \n\nJavaScript), Objets (C++, DOTNET,JAVA) , I.A. (Lisp, Prolog) \n\nLogiciels : Open ERP (Enterprise Resource Planning), AutoCAD, MATLAB, Visual \n\nBasic, Dreamweaver MX. \n\nDivers : Bases de données, ONE (Opportunistic Network Environment), NS3, \n\nArchitecture réseaux,Merise,... \n\n',
{'entities': [(1233, 1424, 'diplomes'), (1012, 1227, 'diplomes'), (812, 1005, 'diplomes'), (589, 806, 'diplomes'), (365, 583, 'diplomes'), (122, 158, 'adresse')]}
)]
Я согласен, лучше, если мы попытаемся обучить модель только на одном резюме, и проверим с ним, чтобы узнать, учится ли он.
Я имею изменил код, разница сейчас пытаюсь натренировать пустую модель.
def train_spacy():
TRAIN_DATA = training_data
nlp = spacy.blank('fr')
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner, last=True)
ner = nlp.get_pipe("ner")
# add labels
for _, annotations in TRAIN_DATA:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
optimizer = nlp.begin_training()
for itn in range(20):
random.shuffle(TRAIN_DATA)
losses = {}
for text, annotations in TRAIN_DATA:
nlp.update(
[text], # batch of texts
[annotations], # batch of annotations
drop=0.1, # dropout - make it harder to memorise data
sgd=optimizer, # callable to update weights
losses=losses
)
print(itn, dt.datetime.now(), losses)
return nlp
Вот вот sses, которые я получаю на тренировке 
Вот тест, здесь я тестирую то же резюме, которое использовалось для обучения. 
Хорошо, что теперь у меня нет пустого кортежа, модель на самом деле что-то правильно распознала, в данном случае объект "адрес".
Но Я не узнаю сущность «дипломы», в которой у меня есть 5 из них в этом резюме, хотя она обучена этому.