cosmozhang

import torch
from transformers import BertTokenizer, BertModel, BertForMaskedLM
import logging
logging.basicConfig(level=logging.INFO)# OPTIONAL



tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model.eval()

cosmozhang

# -*- coding: utf-8 -*-

u"""
Beta regression for modeling rates and proportions.

References
----------
Grün, Bettina, Ioannis Kosmidis, and Achim Zeileis. Extended beta regression
in R: Shaken, stirred, mixed, and partitioned. No. 2011-22. Working Papers in
Economics and Statistics, 2011.

cosmozhang

'''
Original implementation
https://github.com/clab/dynet_tutorial_examples/blob/master/tutorial_parser.ipynb

The code structure and variable names are similar for better reference.

Not for serious business, just for some comparison between PyTorch and DyNet
(and I still prefer PyTorch)
'''
import torch as T

cosmozhang

import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.utils
from torch.autograd import Variable
from torch.nn import Parameter, init
from torch.nn._functions.rnn import variable_recurrent_factory, StackedRNN
from torch.nn.modules.rnn import RNNCellBase
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence
import numpy as np

cosmozhang

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import numpy as np
import itertools

def flatten(l):
	return list(itertools.chain.from_iterable(l))

cosmozhang

sudo ldconfig /usr/local/cuda-7.0/lib64 

cosmozhang

import edu.stanford.nlp.sentiment.BuildBinarizedDataset;

public class BinarizeTrees {

	public static void main(String[] args) {
			  
	String textFile = (args.length > 1) ? args[1] : args[0];
	System.out.println("Generating binarized trees from file "+textFile);
	String[] input = {"-input", textFile};

cosmozhang

import os
from nltk.parse import stanford
os.environ['STANFORD_PARSER'] = '/path/to/standford/jars'
os.environ['STANFORD_MODELS'] = '/path/to/standford/jars'

parser = stanford.StanfordParser(model_path="/location/of/the/englishPCFG.ser.gz")
sentences = parser.raw_parse_sents(("Hello, My name is Melroy.", "What is your name?"))
print sentences

# GUI