from functools import partial
import jsonlines
from gensim.models import KeyedVectors
import os
dirname = os.path.dirname(__file__)
join_path = partial(os.path.join, dirname)
with jsonlines.open(join_path('data/cambridge.sense.000.jsonl')) as f:
    senses = list(f)
def_embeds = KeyedVectors.load_word2vec_format(join_path(
    'data/cambridge.sense.000.sg.def_embeds.txt'), binary=False)

605d3d176e4732255d3aa7ebf9999ef73ddf90dc
<mask token>

<mask token>
with jsonlines.open(join_path('data/cambridge.sense.000.jsonl')) as f:
    senses = list(f)
<mask token>

<mask token>
dirname = os.path.dirname(__file__)
join_path = partial(os.path.join, dirname)
with jsonlines.open(join_path('data/cambridge.sense.000.jsonl')) as f:
    senses = list(f)
def_embeds = KeyedVectors.load_word2vec_format(join_path(
    'data/cambridge.sense.000.sg.def_embeds.txt'), binary=False)

from functools import partial
import jsonlines
from gensim.models import KeyedVectors
import os
dirname = os.path.dirname(__file__)
join_path = partial(os.path.join, dirname)
with jsonlines.open(join_path('data/cambridge.sense.000.jsonl')) as f:
    senses = list(f)
def_embeds = KeyedVectors.load_word2vec_format(join_path(
    'data/cambridge.sense.000.sg.def_embeds.txt'), binary=False)
