sshleifer

GROUP_MEMBERS = {
    'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
    'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo',
                'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 
                'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
    'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
    'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
    'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
    'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
    'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']

lantiga

from transformers import BertForQuestionAnswering
import torch

bert_name = "bert-large-uncased-whole-word-masking-finetuned-squad"

model = BertForQuestionAnswering.from_pretrained(bert_name, torchscript=True)
model.eval()

inputs = [torch.ones(1, 2, dtype=torch.int64),
          torch.ones(1, 2, dtype=torch.int64),

aditya-malte

VictorSanh

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Optimizer

KD_loss = nn.KLDivLoss(reduction='batchmean')

def kd_step(teacher: nn.Module,
            student: nn.Module,
            temperature: float,

hitvoice

loretoparisi

#!/usr/local/bin/python3
# @author cpuhrsch https://github.com/cpuhrsch
# @author Loreto Parisi loreto@musixmatch.com

import argparse
import numpy as np
from sklearn.metrics import confusion_matrix

def parse_labels(path):
    with open(path, 'r') as f:

embanner

ColeMurray

import keras
from keras.preprocessing import image
from keras.applications.inception_v3 import preprocess_input, decode_predictions
import numpy as np
import tensorflow as tf

model = keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
graph = tf.get_default_graph()

shagunsodhani

Bag of Tricks for Efficient Text Classification

Introduction

• Introduces fastText, a simple and highly efficient approach for text classification.
• At par with deep learning models in terms of accuracy though an order of magnitude faster in performance.
• Link to the paper
• Link to code

Architecture

cburgdorf

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense

training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
target_data = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(32, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))