def main(unused_argv):
if not FLAGS.data_dir:
raise ValueError("--data_dir is required.")
if not FLAGS.model_config:
raise ValueError("--model_config is required.")
encoder = encoder_manager.EncoderManager()
with open(FLAGS.model_config) as json_config_file:
model_config = json.load(json_config_file)
if type(model_config) is dict:
model_config = [model_config]
for mdl_cfg in model_config:
model_config = configuration.model_config(mdl_cfg, mode="encode")
encoder.load_model(model_config)
if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
results = eval_classification.eval_nested_kfold(
encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
scores = results[0]
print('Mean score', np.mean(scores))
elif FLAGS.eval_task == "SICK":
results = eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
elif FLAGS.eval_task == "MSRP":
results = eval_msrp.evaluate(
encoder, evalcv=True, evaltest=True, use_feats=False, loc=FLAGS.data_dir)
elif FLAGS.eval_task == "TREC":
eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
else:
raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)
encoder.close()
def load_model(FLAGS):
if FLAGS.sr_model == 'IS':
#Load InferSent
MODEL_PATH = os.path.join(FLAGS.is_dir, 'encoder/infersent1.pkl')
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 1
}
model = InferSent(params_model)
model.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = os.path.join(FLAGS.is_dir,
'dataset/GloVe/glove.840B.300d.txt')
model.set_w2v_path(W2V_PATH)
elif FLAGS.sr_model == 'QT':
# Load Quick-Thought
model = encoder_manager.EncoderManager()
with open(FLAGS.model_config) as json_config_file:
model_config = json.load(json_config_file)
if type(model_config) is dict:
model_config = [model_config]
for mdl_cfg in model_config:
model_config = configuration.model_config(mdl_cfg, mode='encode')
model.load_model(model_config)
elif FLAGS.sr_model == 'USE':
model = hub.Module(
'https://tfhub.dev/google/universal-sentence-encoder-large/2')
return model
def load_model(self, FLAGS):
tf.logging.set_verbosity(tf.logging.INFO)
model = encoder_manager.EncoderManager()
with open(FLAGS.model_config) as json_config_file:
model_config = json.load(json_config_file)
if type(model_config) is dict:
model_config = [model_config]
for mdl_cfg in model_config:
model_config = configuration.model_config(mdl_cfg, mode="encode")
model.load_model(model_config)
return model
def main(unused_argv):
if not FLAGS.data_dir:
raise ValueError("--data_dir is required.")
encoder = encoder_manager.EncoderManager()
# Maybe load unidirectional encoder.
if FLAGS.uni_checkpoint_path:
print("Loading unidirectional model...")
uni_config = configuration.model_config()
encoder.load_model(uni_config, FLAGS.uni_vocab_file,
FLAGS.uni_embeddings_file,
FLAGS.uni_checkpoint_path)
# Maybe load bidirectional encoder.
if FLAGS.bi_checkpoint_path:
print("Loading bidirectional model...")
bi_config = configuration.model_config(bidirectional_encoder=True)
encoder.load_model(bi_config, FLAGS.bi_vocab_file,
FLAGS.bi_embeddings_file, FLAGS.bi_checkpoint_path)
if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
# we changed from skip-thoughts to ec.
ec.eval_nested_kfold(encoder,
FLAGS.eval_task,
FLAGS.data_dir,
use_nb=False)
elif FLAGS.eval_task == "SICK":
eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
elif FLAGS.eval_task == "MSRP":
eval_msrp.evaluate(encoder,
evalcv=True,
evaltest=True,
use_feats=True,
loc=FLAGS.data_dir)
elif FLAGS.eval_task == "TREC":
eval_trec.evaluate(encoder,
evalcv=True,
evaltest=True,
loc=FLAGS.data_dir)
else:
raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)
encoder.close()
from TfidfVocabCreator import Tfidf_Creator as TFIDF
import configuration
import encoder_manager
import numpy as np
import document as doc
# Set paths to the model.
VOCAB_FILE = "./data/vocab.txt"
EMBEDDING_MATRIX_FILE = 'use_trained_model'
CHECKPOINT_PATH = "./model/train/model.ckpt-22638"
TEXT_FILE = "/home/shmuelfeld/Desktop/inputFiles_Heb/*.txt"
data = []
tfidf = TFIDF()
tfidf_dict = tfidf.get_tfidf_dic(TEXT_FILE)
encoder = encoder_manager.EncoderManager()
encoder.load_model(configuration.model_config(),
vocabulary_file=VOCAB_FILE,
embedding_matrix_file=EMBEDDING_MATRIX_FILE,
checkpoint_path=CHECKPOINT_PATH)
def sentence_to_vec(sentence):
sen = list()
sen.append(sentence)
return encoder.encode(sen)
def sens2vec(list_of_sentences, total_tfidf):
multed = []
for sent in list_of_sentences:
def main(unused_argv):
if not FLAGS.data_dir:
raise ValueError("--data_dir is required.")
if not FLAGS.model_config:
raise ValueError("--model_config is required.")
encoder = encoder_manager.EncoderManager()
with open(FLAGS.model_config) as json_config_file:
model_config = json.load(json_config_file)
if type(model_config) is dict:
model_config = [model_config]
sp = None
if FLAGS.sentencepiece_model_path:
print('Loading sentencepiece model', FLAGS.sentencepiece_model_path)
sp = spm.SentencePieceProcessor()
sp.Load(FLAGS.sentencepiece_model_path)
for mdl_cfg in model_config:
model_config = configuration.model_config(mdl_cfg, mode="encode")
encoder.load_model(model_config)
if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
results = eval_classification.eval_nested_kfold(encoder,
FLAGS.eval_task,
FLAGS.data_dir,
use_nb=False)
scores = results[0]
print('Mean score', np.mean(scores))
elif FLAGS.eval_task == "SICK":
results = eval_sick.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
sp=sp)
elif FLAGS.eval_task == "MSRP":
results = eval_msrp.evaluate(encoder,
evalcv=True,
evaltest=True,
use_feats=False,
loc=FLAGS.data_dir)
elif FLAGS.eval_task == "TREC":
eval_trec.evaluate(encoder,
evalcv=True,
evaltest=True,
loc=FLAGS.data_dir)
elif FLAGS.eval_task == 'SNLI-MT-TR':
file_meta_data = {
'file_names': {
'train': 'snli_train_translation.jsonl',
'dev': 'snli_dev_translation.jsonl',
'test': 'snli_test_translation.jsonl'
},
'sentence_keys': {
'sentence1': 'translate-sentence1',
'sentence2': 'translate-sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_nli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'SNLI':
file_meta_data = {
'file_names': {
'train': 'snli_1.0_train.jsonl',
'dev': 'snli_1.0_dev.jsonl',
'test': 'snli_1.0_test.jsonl'
},
'sentence_keys': {
'sentence1': 'sentence1',
'sentence2': 'sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_nli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'MULTINLI-MT-TR-MATCHED':
file_meta_data = {
'file_names': {
'train':
'multinli_train_translation.jsonl',
'dev':
'multinli_dev_matched_translation.jsonl',
'test':
'multinli_0.9_test_matched_translation_unlabeled.jsonl',
'test_output':
'multinli_0.9_test_matched_translation_unlabeled_output.csv'
},
'sentence_keys': {
'sentence1': 'translate-sentence1',
'sentence2': 'translate-sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_multinli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'MULTINLI-MATCHED':
file_meta_data = {
'file_names': {
'train': 'multinli_1.0_train.jsonl',
'dev': 'multinli_1.0_dev_matched.jsonl',
'test': 'multinli_0.9_test_matched_unlabeled.jsonl',
'test_output': 'multinli_0.9_test_matched_unlabeled_output.csv'
},
'sentence_keys': {
'sentence1': 'sentence1',
'sentence2': 'sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_multinli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'MULTINLI-MT-TR-MISMATCHED':
file_meta_data = {
'file_names': {
'train':
'multinli_train_translation.jsonl',
'dev':
'multinli_dev_mismatched_translation.jsonl',
'test':
'multinli_0.9_test_mismatched_translation_unlabeled.jsonl',
'test_output':
'multinli_0.9_test_mismatched_translation_unlabeled_output.csv',
},
'sentence_keys': {
'sentence1': 'translate-sentence1',
'sentence2': 'translate-sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_multinli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'MULTINLI-MISMATCHED':
file_meta_data = {
'file_names': {
'train': 'multinli_1.0_train.jsonl',
'dev': 'multinli_1.0_dev_mismatched.jsonl',
'test': 'multinli_0.9_test_mismatched_unlabeled.jsonl',
'test_output':
'multinli_0.9_test_mismatched_unlabeled_output.csv'
},
'sentence_keys': {
'sentence1': 'sentence1',
'sentence2': 'sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral']
}
eval_multinli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'XNLI-MT-TR':
file_meta_data = {
'file_names': {
'train': 'multinli_train_translation.jsonl',
'dev': 'xnli_dev_translation.jsonl',
'test': 'xnli_test_translation.jsonl'
},
'sentence_keys': {
'sentence1': 'translate-sentence1',
'sentence2': 'translate-sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral'],
'language': 'any'
}
eval_xnli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
elif FLAGS.eval_task == 'XNLI':
file_meta_data = {
'file_names': {
'train': 'multinli_1.0_train.jsonl',
'dev': 'xnli.dev.jsonl',
'test': 'xnli.test.jsonl'
},
'sentence_keys': {
'sentence1': 'sentence1',
'sentence2': 'sentence2'
},
'label_classes': ['contradiction', 'entailment', 'neutral'],
'language': 'en'
}
eval_xnli.evaluate(encoder,
evaltest=True,
loc=FLAGS.data_dir,
file_meta_data=file_meta_data,
sp=sp)
else:
raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)
encoder.close()