def calcule_eucl(text, question):
blob = TextBlob("".join(text))
sentences = [item.raw for item in blob.sentences]
V = 2
MODEL_PATH = 'InferSent/encoder/infersent%s.pkl' % V
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': V
}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = 'InferSent/crawl-300d-2M.vec'
infersent.set_w2v_path(W2V_PATH)
infersent.build_vocab(sentences, tokenize=True)
dict_embeddings = {}
for i in range(len(sentences)):
dict_embeddings[sentences[i]] = infersent.encode([sentences[i]],
tokenize=True)
encode_question = infersent.encode([question], tokenize=True)
eucl = eucl_sim(dict_embeddings, encode_question)
return sentences, eucl
def prepare(model_path: str,
word_vecs: str,
out_path: str,
sentences: Union[str, List[str]] = None,
max_vocab: int = 0):
"""
this method is for adapting the vocabulary,
:param model_path: unadapted model state
:param word_vecs: word vectors
:param out_path: where to store the state
:param sentences: training sentences for scanning the vocabulary
:param max_vocab: maximum vocabulary size (optional)
:return:
"""
assert bool(sentences) != bool(
max_vocab), 'Either sentences or max_vocab should be given'
model = InferSent(config=MODEL_CONF)
log.info(f"Loading state from {out_path}")
model.load_state_dict(torch.load(model_path))
log.info(f"Loading word vecs from {out_path}")
model.set_w2v_path(word_vecs)
if sentences:
if type(sentences) is not list:
sentences = list(read_lines(sentences))
log.info("Building vocabulary from sentences")
model.build_vocab(sentences, tokenize=True)
if max_vocab:
log.info(f"Pruning vocabulary to top {max_vocab} types")
model.build_vocab_k_words(K=max_vocab)
log.info(f"Saving at {out_path}")
state = SentenceEncoder._get_state(model)
torch.save(state, out_path)
def load_inferSent(sentences):
logger.info('load InferSent')
V = 2
MODEL_PATH = 'Infersent/encoder/infersent%s.pkl' % V
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': V
}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
if torch.cuda.is_available():
infersent.cuda()
# set word vector
if V == 1:
W2V_PATH = 'Infersent/Glove/glove.840B.300d.txt'
logger.warning('Use Glove Embedding')
elif V == 2:
W2V_PATH = 'Infersen/fastText/crawl-300d-2M.vec'
logger.warning('Use fastText Embedding')
else:
raise NotImplementedError
infersent.set_w2v_path(W2V_PATH)
# build voceb
infersent.build_vocab(sentences, tokenize=True)
return infersent
def create_embeddings(infer_path, data_path, em_type):
yt_titles = yt.get_yt_titles()
with open("data/whtitles", "r") as f:
wh_titles = [line.rstrip('\n') for line in f]
if em_type == "yt": # Youtube
save_f = os.path.join(data_path, "yt_embed")
titles = yt_titles
elif em_type == "wh": # Wikihow
save_f = os.path.join(data_path, "wh_embed")
titles = wh_titles
else:
raise "Unknown embedding type: {}".format(em_type)
nltk.download('punkt')
V = 1
MODEL_PATH = os.path.join(infer_path, 'encoder/infersent%s.pkl' % V)
params_model = {
'bsize': 256,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': V
}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
infersent = infersent.cuda()
W2V_PATH = os.path.join(infer_path, 'GloVe/glove.840B.300d.txt')
infersent.set_w2v_path(W2V_PATH)
infersent.build_vocab(yt_titles + wh_titles, tokenize=True)
embed = infersent.encode(titles, tokenize=True)
np.save(save_f, embed)
def load_pretrained_model(model_version):
model_path = RAW_MODEL_PATH % model_version
params = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0,
'version': model_version}
model = InferSent(params)
model.load_state_dict(torch.load(model_path))
return model
def infersent_embed_posts(posts, max_sent_cnt, embed_dim, data_fold_path):
model_path = data_fold_path + 'word_sent_embed/infersent2.pickle'
word_emb_path = data_fold_path + 'word_sent_embed/fasttext.vec'
posts_arr = np.zeros((len(posts), max_sent_cnt, embed_dim))
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 2
}
model = InferSent(params_model)
model.load_state_dict(torch.load(model_path))
model.set_w2v_path(word_emb_path)
all_sents = []
for sens in posts:
all_sents.extend(sens)
model.build_vocab(all_sents, tokenize=False)
for ind, sentences in enumerate(posts):
embeddings = model.encode(sentences, tokenize=False, verbose=False)
l = min(max_sent_cnt, len(sentences))
posts_arr[ind, :l, :] = embeddings[:l]
return posts_arr
def get_loaded_model(force_gpu=False, k_most_frequent_words=1000000):
model_path = "infersent/encoder/infersent{}.pkl".format(model_version)
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': model_version
}
model = InferSent(params_model)
model.load_state_dict(torch.load(model_path))
if (not torch.cuda.is_available()) and force_gpu:
raise GPUNotFoundException()
if torch.cuda.is_available():
model = model.cuda()
# If infersent1 -> use GloVe embeddings.
# If infersent2 -> use InferSent embeddings.
W2V_PATH = 'infersent/dataset/GloVe/glove.840B.300d.txt' if model_version == 1 else 'infersent/dataset/fastText/crawl-300d-2M.vec' ## noqa
model.set_w2v_path(W2V_PATH)
# Load embeddings of K most frequent words
model.build_vocab_k_words(K=k_most_frequent_words)
return model
def embed_sent(datafile):
sentences = []
with open(datafile, 'r') as f:
i = 0
for line in f:
line = line.replace('\n', '')
sentences.append(line)
i += 1
if i == 455820:
break
V = 1
MODEL_PATH = 'infersent%s.pkl' % V
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = 'GloVe/glove.840B.300d.txt'
infersent.set_w2v_path(W2V_PATH)
infersent.build_vocab(sentences, tokenize=True)
embeddings = infersent.encode(sentences, tokenize=True)
np.savetxt("../../wiki-split/Data/Infersent_vectors/complex_sent", embeddings)
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 __init__(self, state_path=None, state_dict=None):
assert bool(state_path) != bool(
state_dict), 'Either state_path or state_dict must be there'
self.model = InferSent(config=MODEL_CONF)
if state_path:
log.info(f"Loading state from {state_path}")
state = torch.load(state_path, map_location=device)
else:
state = state_dict
assert 'model' in state and 'word_vec' in state # created by self.prepare() method
self.model.load_state_dict(state['model'])
self.model.word_vec = state['word_vec']
self.maybe_gpu()
def __init__(self):
V = 2
MODEL_PATH = 'encoder/infersent%s.pkl' % V
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': V
}
self.infersent = InferSent(params_model)
self.infersent.load_state_dict(torch.load(MODEL_PATH))
self.infersent.set_w2v_path('fastText/crawl-300d-2M.vec')
def __init__(self, word_vectors_dir, models_dir):
word_vectors = os.path.join(word_vectors_dir, self.WORD_VECTORS_FILE)
model_file = os.path.join(models_dir, self.MODEL_FILE)
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 2
}
self.model = InferSent(params_model)
self.model.load_state_dict(torch.load(model_file))
self.model.set_w2v_path(word_vectors)
def _get_pretrained_model(self):
config = {
"checkpoint": self._get_checkpoint(),
"seed": 42,
"data_dir": "./data",
"glove_max_vectors": 10000,
"glove_dim": 300,
"batch_size": 64,
"cuda": True,
"encoder": self.encoder,
"max_epochs": None,
"lstm_hidden_dim": 2048 if self.encoder != "awe" else 300,
"classifier_hidden_dim": 512,
"debug": False
}
config = argparse.Namespace(**config)
pl.seed_everything(config.seed)
data_module = SNLIDataModule(config)
data_module.setup()
encoder = get_encoder(config)
model = InferSent.load_from_checkpoint(
config.checkpoint,
embeddings=data_module.glove_embeddings(),
encoder=encoder,
config=config)
model.to("cuda")
return model, data_module
class InferSentFeatures:
def __init__(self, lang_enc_dir, sentences):
sys.path.insert(0, os.path.join(lang_enc_dir, 'InferSent/'))
from models import InferSent
version = 1
MODEL_PATH = os.path.join(
lang_enc_dir, 'InferSent/encoder/infersent%s.pkl' % version)
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': version
}
self.model = InferSent(params_model)
self.model.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = os.path.join(lang_enc_dir, 'glove/glove.6B.300d.txt')
self.model.set_w2v_path(W2V_PATH)
self.model.build_vocab(sentences, tokenize=True)
def generate_embeddings(self, sentences):
embeddings = self.model.encode(sentences, tokenize=True)
return embeddings
class Encoder2:
''' Encoder based on InferSent '''
WORD_VECTORS_FILE = 'crawl-300d-2M.vec'
MODEL_FILE = 'infersent2.pkl'
def __init__(self, word_vectors_dir, models_dir):
word_vectors = os.path.join(word_vectors_dir, self.WORD_VECTORS_FILE)
model_file = os.path.join(models_dir, self.MODEL_FILE)
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 2
}
self.model = InferSent(params_model)
self.model.load_state_dict(torch.load(model_file))
self.model.set_w2v_path(word_vectors)
def start(self, texts):
texts_list = texts.values.tolist()
self.model.build_vocab(texts_list, tokenize=True)
def close(self):
pass
def encode(self, texts_batch):
texts_batch_list = texts_batch.values.tolist()
texts_batch_vec = self.model.encode(texts_batch_list, tokenize=True)
return texts_batch_vec
def load_infersent():
V = 2
MODEL_PATH = 'encoder/infersent%s.pkl' % V
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = 'fastText/crawl-300d-2M.vec'
infersent.set_w2v_path(W2V_PATH)
infersent.build_vocab_k_words(K=100000)
return infersent
def init_models(vocal_size: int = VOCAB_SIZE):
model = InferSent({
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': VERSION
})
model.load_state_dict(torch.load(MODEL_PATH))
model = model.cuda() if USE_CUDA else model
model.set_w2v_path(VECTOR_PATH)
model.build_vocab_k_words(K=VOCAB_SIZE)
return model
def getSentenceVector(doc, model_params: dict = {}, encoder = "distilbert", model_name = 'distilbert-base-nli-mean-tokens' ):
sp = spacy.load('en_core_web_sm')
tokenized = sp(doc)
sentences = []
for token in tokenized.sents:
sentences.append(token.text)
if encoder in ['bert', 'xlnet', 'longformer', 'reformer', 'distilbert', 'roberta', 'bart']:
# Use encoder for mapping tokens to embeddings
word_embedding_model = models.Transformer(model_name,
tokenizer_args= model_params['tokenizer_args'] if 'tokenizer_args' in model_params else {})
# Apply mean pooling to get one fixed sized sentence vector
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
pooling_mode_cls_token=False,
pooling_mode_max_tokens=False)
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
sentence_embeddings = model.encode(sentences)
elif encoder == 'use':
#!pip install embedding-as-service
from embedding_as_service.text.encode import Encoder
en = Encoder(embedding='use', model='use_dan', max_seq_length=256)
sentence_embeddings = en.encode(texts=sentences)
elif encoder == 'infersent':
import nltk
nltk.download('punkt')
from models import InferSent
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': 2}
infersent = InferSent(params_model)
W2V_PATH = 'drive/My Drive/wiki-news-300d-1M.vec'
infersent.set_w2v_path(W2V_PATH)
infersent.build_vocab(sentences, tokenize=True)
sentence_embeddings = infersent.encode(sentences, tokenize=True)
elif encoder == 'sent2vec':
import sent2vec
model = sent2vec.Sent2vecModel()
model.load_model('drive/My Drive/torontobooks_unigram.bin')
sentence_embeddings = model.embed_sentences(sentences)
elif encoder == 'laser':
from laserembeddings import Laser
laser = Laser() ## Also used for multilingual sentence embeddings
sentence_embeddings = laser.embed_sentences(sentences, lang='en')
else:
raise ValueError('Invalid encoder {} or encoder Unavailable.'.format(encoder))
return list(zip(sentences, sentence_embeddings))
def __init__(self, encoder_type, enc_hidden_dim, cls_hidden_dim, lr,
dataset_sizes):
super().__init__()
self.save_hyperparameters()
self.model = InferSent(encoder_type, enc_hidden_dim, cls_hidden_dim)
# Loss
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=1)
# Metrics
self.train_correct = 0
self.val_correct = 0
self.test_correct = 0
def __init__(self):
self.filename = "LCP/lcpr_i.sav"
self.cmudict = cmudict.dict()
self.wnlp = WonderlicNLP()
self.embeddings_index = {}
self.wiki_top10 = [
word[0].split()[0]
for word in pd.read_csv("LCP/wiki_top10.csv").values
][:10001]
self.infersent_model_path = 'LCP/infersent%s.pkl' % 1
self.infersent_model_params = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 1
}
self.infersent = InferSent(self.infersent_model_params)
self.model = RandomForestRegressor(n_estimators=100)
def __init__(self, version=1):
super().__init__()
self.version = version
if version == 1:
self.PATH_TO_W2V = os.path.join(NLP_MODELS_PATH, 'pretrained',
'word_embeddings',
'glove.840B.300d',
'glove.840B.300d.txt')
if version == 2:
self.PATH_TO_W2V = os.path.join(NLP_MODELS_PATH, 'pretrained',
'word_embeddings', 'crawl-300d-2M',
'crawl-300d-2M.vec')
self.MODEL_PATH = os.path.join(NLP_MODELS_PATH, 'pretrained',
'word_embeddings',
'infersent%s' % version,
'infersent%s.pkl' % version)
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s',
level=logging.DEBUG)
# Load InferSent model
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': version
}
self.model = InferSent(params_model)
self.model.load_state_dict(torch.load(self.MODEL_PATH))
self.model.set_w2v_path(self.PATH_TO_W2V)
self._embedding_length: int = params_model['enc_lstm_dim']
self.name = f"{self.__class__.__name__ }_v{self.version}"
self.static_embeddings = True
def load_infersent_model(model_path, w2v_path):
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': 2}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(model_path))
infersent.set_w2v_path(w2v_path)
return infersent
def infersent_flat_embed_posts(posts, embed_dim, data_fold_path):
model_path = data_fold_path + 'word_sent_embed/infersent2.pickle'
word_emb_path = data_fold_path + 'word_sent_embed/fasttext.vec'
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 2
}
model = InferSent(params_model)
model.load_state_dict(torch.load(model_path))
model.set_w2v_path(word_emb_path)
model.build_vocab(posts, tokenize=False)
return model.encode(posts, tokenize=False, verbose=False)
def apply_logician(s1, s2 , is_list=False, sick_model = False):
# is_list : If you are directly sending sentences then keep is_list = False
# If you are sending list of list of words then keep is_list = True
# sick_model: if True, will use sick model for prediction
# : if False, will use snli model for prediction
# Load InferSent model
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
model = InferSent(params_model)
model.load_state_dict(torch.load(MODEL_PATH))
model.set_w2v_path(PATH_TO_W2V)
params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}
params_senteval['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128,
'tenacity': 3, 'epoch_size': 2}
params_senteval['infersent'] = model.cuda()
if not is_list:
s1 = convert_str2lst(s1)
s2 = convert_str2lst(s2)
samples = s1+s2
params_senteval['batch_size'] = min(128,len(s1))
params_senteval = utils.dotdict(params_senteval)
params_senteval.usepytorch = True
prepare(params_senteval, samples)
emb_s1 = batcher(params_senteval, s1)
emb_s2 = batcher(params_senteval, s2)
if sick_model:
testF = np.c_[ np.abs(emb_s1 - emb_s2),emb_s1 * emb_s2]
cp = torch.load('./saved_sick.pth')
print('[Contradiction Neutral Entailment]')
else:
testF = np.c_[emb_s1, emb_s2, emb_s1 * emb_s2, np.abs(emb_s1 - emb_s2)]
cp = torch.load('./saved_snli_augment_ordered.pth')
print('[ Entailment Neutral Contradiction ]')
inputdim = testF.shape[1]
nclasses = 3
clf = nn.Sequential(nn.Linear(inputdim, nclasses),).cuda()
clf.load_state_dict(cp)
testF = torch.FloatTensor(testF).cuda()
out = clf(testF)
sf = nn.Softmax(1)
probs = sf(out)
return probs
def open_model(transformer_name):
print(f"Opening {transformer_name} sentence embeddings")
if "bert-base-nli-mean" in transformer_name:
model = SentenceTransformer('bert-base-nli-mean-tokens')
elif "s_infersent" in transformer_name:
V = 2
MODEL_PATH = 'encoder/infersent%s.pkl' % V
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
model = InferSent(params_model)
model.load_state_dict(torch.load(MODEL_PATH, encoding="utf-8"))
# setting word vector path for the model
W2V_PATH = 'data/embeddings/crawl-300d-2M.vec' #need to adapt the path
model.set_w2v_path(W2V_PATH)
return model
def init_infersent_model(self):
model_version = 1
MODEL_PATH = "encoder/infersent%s.pkl" % model_version
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': model_version
}
model = InferSent(params_model)
model.load_state_dict(torch.load(MODEL_PATH))
# Keep it on CPU or put it on GPU
use_cuda = False
model = model.cuda() if use_cuda else model
# If infersent1 -> use GloVe embeddings. If infersent2 -> use InferSent embeddings.
W2V_PATH = 'GloVe/glove.840B.300d.txt' if model_version == 1 else 'fastText/crawl-300d-2M.vec'
model.set_w2v_path(W2V_PATH)
# Load embeddings of K most frequent words
model.build_vocab_k_words(K=100000)
self.model = model
def infersent_glove():
#Set Model for InferSent+Glove
V = 1
MODEL_PATH = '/tmp/GloVe/encoder/infersent%s.pkl' % V
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': V
}
modelg = InferSent(params_model)
modelg.load_state_dict(torch.load(MODEL_PATH))
# Keep it on CPU or put it on GPU
use_cuda = True
modelg = modelg.cuda() if use_cuda else modelg
# If infersent1 -> use GloVe embeddings. If infersent2 -> use InferSent embeddings.
W2V_PATH = '/tmp/GloVe/glove.840B.300d.txt' if V == 1 else '/home/ganesh/Quora_dev/tmp/GloVe/glove.840B.300d.txt'
modelg.set_w2v_path(W2V_PATH)
# Load embeddings of K most frequent words
modelg.build_vocab_k_words(K=100000)
return modelg
def load_model():
model_version = 1
MODEL_PATH = "encoder/infersent%s.pkl" % model_version
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': model_version
}
model = InferSent(params_model)
model.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = 'GloVe/glove.840B.300d.txt'
model.set_w2v_path(W2V_PATH)
print('building vocab')
model.build_vocab_k_words(K=100000)
print('done building vocab')
return model
def resume_model(model_path, dict_path, version, use_cuda):
device = torch.device('cuda' if use_cuda else 'cpu')
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': version
}
infer_sent = InferSent(params_model)
infer_sent.load_state_dict(torch.load(model_path, map_location=device))
infer_sent.set_w2v_path(dict_path)
return infer_sent
def load_infersent_model():
file_path = dirname(os.path.realpath(__file__))
MODEL_PATH = os.path.join(file_path, 'encoder', 'infersent2.pkl')
params_model = {
'bsize': 64,
'word_emb_dim': 300,
'enc_lstm_dim': 2048,
'pool_type': 'max',
'dpout_model': 0.0,
'version': 2
}
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
W2V_PATH = os.path.join(file_path, 'fastText', 'crawl-300d-2M.vec')
infersent.set_w2v_path(W2V_PATH)
return infersent
parser.add_argument('-t', '--tokenize', action='store_true',
help='Passes tokenize=True to build_vocab()')
parser.add_argument('-o', '--out-dir', type=str, required=True,
help='Output folder to save feature files')
parser.add_argument('-c', '--cpu', action='store_true',
help='Use CPU instead of GPU.')
parser.add_argument('-b', '--batch-size', type=int, default=64,
help='Batch size (default: 64)')
parser.add_argument('files', nargs='+',
help='List of files to extract sentence embeddings')
args = parser.parse_args()
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
'pool_type': 'max', 'dpout_model': 0.0, 'version': args.version}
model = InferSent(params_model)
model.load_state_dict(torch.load(args.model_path))
if not args.cpu:
model = model.cuda()
model.set_w2v_path(args.w2v_path)
# Ensure directory
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
# Read files and extract features
for fpath in args.files:
print('Reading file {}'.format(fpath))
sents = []
