Tafkas

import numpy as np

def rmsle(h, y): 
    """
    Compute the Root Mean Squared Log Error for hypthesis h and targets y
    
    Args:
        h - numpy array containing predictions with shape (n_samples, n_targets)
        y - numpy array containing targets with shape (n_samples, n_targets)
    """

roeeaharoni

import torch
from pytorch_pretrained_bert import BertForMaskedLM, BertTokenizer
import random

# Requires pytorch_pretrained_bert: https://github.com/huggingface/pytorch-pretrained-BERT

# returns the probabilities over the vocabulary for the masked words in sent
def get_preds(sent):
    tokenized = bert_tokenizer.tokenize(sent)
    tokenized = ['[CLS]'] + ['[MASK]' if x == 'mask' else x for x in tokenized] + ['[SEP]']