thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

apaszke

import sys
from collections import OrderedDict

PY2 = sys.version_info[0] == 2
_internal_attrs = {'_backend', '_parameters', '_buffers', '_backward_hooks', '_forward_hooks', '_forward_pre_hooks', '_modules'}


class Scope(object):
    def __init__(self):
        self._modules = OrderedDict()

jfsantos

def train_fn(model, optimizer, criterion, batch):
    x, y, lengths = batch

    x = Variable(x.cuda())
    y = Variable(y.cuda(), requires_grad=False)

    mask = Variable(torch.ByteTensor(x.size()).fill_(1).cuda(),
            requires_grad=False)
    for k, l in enumerate(lengths):
        mask[:l, k, :] = 0

jacobsvante

agnoster.zsh-theme

A ZSH theme optimized for people who use:

• Solarized
• Git
• Unicode-compatible fonts and terminals (I use iTerm2 + Menlo)

For Mac users, I highly recommend iTerm 2 + Solarized Dark