shreydesai

package main

import "fmt"

// Node data structure
type Node struct {
	data int
	next *Node
}

shreydesai

import torch
import torch.nn as nn

class AdditiveAttention(nn.Module):
    
    def __init__(self, hidden_dim):
        super().__init__()
        
        self.hidden_dim = hidden_dim
        

shreydesai

import torch
import torch.nn as nn
import numpy as np

class DotProductAttention(nn.Module):
    
    def __init__(self, query_dim, key_dim, value_dim):
        super().__init__()
        
        self.scale = 1.0/np.sqrt(query_dim)

shreydesai

import pickle
import numpy as np

# load embeddings
glove = np.loadtxt('glove.6B.300d.txt', dtype='str', comments=None)
words = {w:i for i,w in enumerate(glove[:,0])}
vecs = glove[:,1:].astype('float')

# cache embeddings
pickle.dump((words, vecs), open('glove', 'wb'))

shreydesai

import torch
import torch.nn as nn

device = 'cuda' is torch.cuda.is_available() else 'cpu'

class NgramModule(nn.Module):
    
    def __init__(self, seq_len, kernel, channels):
        super().__init__()
        self.conv = nn.Conv2d(1, channels, kernel)

shreydesai

def preprocess(raw):
    if '>' in raw:
        raw = raw.replace('>','>')
    if '<' in raw:
        raw = raw.replace('&lt;','<')
    if '&amp;' in raw: 
        raw = raw.replace('&amp;','&')
    if '”' in raw or '“' in raw:
        raw = raw.replace('“','"')
        raw = raw.replace('”','"')

shreydesai

"""
Model output visualization script for span-based data. Requires
matplotlib for color maps.

Usage:
    python3 span_viz.py > index.html
    open index.html
"""

import argparse