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# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import, division, unicode_literals
import sys
import io
import numpy as np
import logging
# Set PATHs
PATH_TO_SENTEVAL = '../'
PATH_TO_DATA = '../data'
# PATH_TO_VEC = 'glove/glove.840B.300d.txt'
PATH_TO_VEC = 'fasttext/crawl-300d-2M.vec'
# import SentEval
sys.path.insert(0, PATH_TO_SENTEVAL)
import senteval
# Create dictionary
def create_dictionary(sentences, threshold=0):
words = {}
for s in sentences:
for word in s:
words[word] = words.get(word, 0) + 1
if threshold > 0:
newwords = {}
for word in words:
if words[word] >= threshold:
newwords[word] = words[word]
words = newwords
words['<s>'] = 1e9 + 4
words['</s>'] = 1e9 + 3
words['<p>'] = 1e9 + 2
sorted_words = sorted(words.items(), key=lambda x: -x[1]) # inverse sort
id2word = []
word2id = {}
for i, (w, _) in enumerate(sorted_words):
id2word.append(w)
word2id[w] = i
return id2word, word2id
# Get word vectors from vocabulary (glove, word2vec, fasttext ..)
def get_wordvec(path_to_vec, word2id):
word_vec = {}
with io.open(path_to_vec, 'r', encoding='utf-8') as f:
# if word2vec or fasttext file : skip first line "next(f)"
for line in f:
word, vec = line.split(' ', 1)
if word in word2id:
word_vec[word] = np.fromstring(vec, sep=' ')
logging.info('Found {0} words with word vectors, out of \
{1} words'.format(len(word_vec), len(word2id)))
return word_vec
# SentEval prepare and batcher
def prepare(params, samples):
_, params.word2id = create_dictionary(samples)
params.word_vec = get_wordvec(PATH_TO_VEC, params.word2id)
params.wvec_dim = 300
return
def batcher(params, batch):
batch = [sent if sent != [] else ['.'] for sent in batch]
embeddings = []
for sent in batch:
sentvec = []
for word in sent:
if word in params.word_vec:
sentvec.append(params.word_vec[word])
if not sentvec:
vec = np.zeros(params.wvec_dim)
sentvec.append(vec)
sentvec = np.mean(sentvec, 0)
embeddings.append(sentvec)
embeddings = np.vstack(embeddings)
return embeddings
# Set params for SentEval
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}
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
if __name__ == "__main__":
se = senteval.engine.SE(params_senteval, batcher, prepare)
transfer_tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16',
'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'SST5', 'TREC', 'MRPC',
'SICKEntailment', 'SICKRelatedness', 'STSBenchmark',
'Length', 'WordContent', 'Depth', 'TopConstituents',
'BigramShift', 'Tense', 'SubjNumber', 'ObjNumber',
'OddManOut', 'CoordinationInversion']
results = se.eval(transfer_tasks)
print(results)
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