allanj

pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

allanj

local bert_model = "bert-base-uncased";
local train_path = "./datasets/coref/train.english.v4_gold_conll";
local dev_path = "./datasets/coref/dev.english.v4_gold_conll";
local test_path = "./datasets/coref/test.english.v4_gold_conll";

{
  "dataset_reader": {
    "type": "coref",
    "token_indexers": {
      "bert": {

allanj

from typing import List, TypeVar, Callable
import numpy as np


T = TypeVar('T')

def bootstrap_paired_ttest(results_a: List[T],
                           results_b: List[T],
                           evaluate_func: Callable[[List[T]], float],
                           sample_times: int = 10000,

allanj

import streamlit as st
# To make things easier later, we're also importing numpy and pandas for working with sample data.
import numpy
import pandas

# Don't worry, we'll explain this method in the next section. We need to make at least one
# call to Streamlit in order to generate a report.
st.title("Demo Test")
# streamlit.header("I'm a large heading")
# streamlit.subheader("I'm not a large heading")

allanj

package corenlp.process;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.List;

import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.parser.nndep.DependencyParser;

allanj

def iob_iobes(tags):
    """
    IOB2 (BIO) -> IOBES
    """
    new_tags = []
    for i, tag in enumerate(tags):
        if tag == 'O':
            new_tags.append(tag)
        elif tag.split('-')[0] == 'B':
            if i + 1 != len(tags) and \

allanj

"""
IOB1:  O I I B I
IOB2:  O B I B I
"""

from typing import List

def iob2(tags: List[str]):
    """
    Check that tags have a valid IOB format.

allanj

//main.java
//First of all, after create `GlobalNetworkParam` object.
// run the following code:
GlobalNetworkParam gnp = new GlobalNetworkParam(optimizer, gnnp);
gnp.setStoreFeatureReps();


/************************
   After the model has been trained.
   model.train(...)

allanj

# 
# @author: Allan
# 



def convert(input, output):
    from gensim.models.keyedvectors import KeyedVectors
    embedding = KeyedVectors.load_word2vec_format(input, binary=True)
    f= open(output, 'w', encoding='utf-8')

allanj

require 'nn'
require 'dpnn'
require 'rnn'
require 'nngraph'

local opt = {
  n_seq = 3,
  d_hid = 4,
  d_mem = 20,
  n_batch = 2,