sobamchan

import numpy as np
from lineflow import datasets
from sklearn.svm import SVC

import sister


def main():
    train = datasets.Imdb("train")
    test = datasets.Imdb("test")

sobamchan

from pathlib import Path
import tarfile
import wget
import pandas as pd


URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz"
SAVETO = Path("./livedoor-news-data.tar.gz")
DATASET_PATH = Path("dataset")

sobamchan

import sister
embedder = sister.MeanEmbedding(lang="en")

sentence = "I am a dog."
vector = embedder(sentence)  # 300-dim vector

sobamchan

from typing import Dict
from functools import partial

import lineflow as lf
import lineflow.datasets as lfds
import lineflow.cross_validation as lfcv

from transformers import BertTokenizer

MAX_LEN = 256

sobamchan

    def training_step(self, batch, batch_idx):
        labels = batch["label"]
        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        token_type_ids = batch["token_type_ids"]

        loss, _ = self.model(
                input_ids,
                token_type_ids=token_type_ids,
                attention_mask=attention_mask,

sobamchan

    @pl.data_loader
    def train_dataloader(self):
        return self._train_dataloader

sobamchan

    def configure_optimizers(self):
        param_optimizer = list(self.model.named_parameters())
        no_decay = ["bias", "gamma", "beta"]
        optimizer_grouped_parameters = [
                {
                    "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
                    "weight_decay_rate": 0.01
                    },
                {
                    "params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],

sobamchan

import lineflow.datasets as lfds

train = lfds.MsrParaphrase('train')
test = lfds.MsrParaphrase('test')

The item in this dataset as follows:

sobamchan

from typing import List, Dict, Callable
from collections import OrderedDict
from functools import partial

import lineflow as lf
import lineflow.datasets as lfds
import lineflow.cross_validation as lfcv

import torch
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler