himkt

var sheetId = "Your_sheet_id";
var sheetName = "Forrm_tab_name";

var schema = {
  timeStamp: 0,
  title: 3,
  authors: 2,
  isRead: 1,
  sourceShort: 4,
  year: 5,

himkt

from optuna.integration import AllenNLPExecutor
import optuna


def objective(trial: optuna.Trial) -> float:
    trial.suggest_float("embedding_dropout", 0.0, 0.5)
    executor = AllenNLPExecutor(trial, "./config.jsonnet", "result", include_package="allennlp_models")
    return executor.run()

himkt

{
  "dataset_reader":{
    "type": "sst_tokens",
    "use_subtrees": true,
    "granularity": "5-class"
  },
  "validation_dataset_reader":{
    "type": "sst_tokens",
    "use_subtrees": false,
    "granularity": "5-class"

himkt

{
    "dataset_reader": {
        "lazy": false,
        "token_indexers": {
            "tokens": {
                "lowercase_tokens": true,
                "type": "single_id"
            }
        },
        "tokenizer": {

himkt

Sending build context to Docker daemon  81.92kB

Step 1/7 : FROM ubuntu:20.04
 ---> adafef2e596e
Step 2/7 : RUN apt update -y &&   apt install -y python3 python3-dev python3-venv python3-pip
 ---> Using cache
 ---> 5ed03d347c2e
Step 3/7 : RUN pip3 install poetry
 ---> Using cache
 ---> ea3716a0aa47

himkt

# -*- coding: utf-8 -*-
from setuptools import setup

packages = \
['konoha',
 'konoha.api',
 'konoha.data',
 'konoha.integrations',
 'konoha.word_tokenizers']

himkt

[tool.poetry]
name = "konoha"
version = "4.4.0"
description = "A tiny sentence/word tokenizer for Japanese text written in Python"
authors = ["himkt <himkt@klis.tsukuba.ac.jp>"]

[tool.poetry.dependencies]
python = "^3.6.1"
janome = {version = "^0.3.10", optional = true}
natto-py = {version = "^0.9.0", optional = true}

himkt

> python allennlp_simple.py                                                                                                                                 (feature/allennlp-pruner| ● 3)
3000it [00:11, 255.15it/s]                                                                                                                                                                
3000it [00:01, 1724.34it/s]                                                                                                                                                               
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [00:00<00:00, 5099.38it/s]
400000it [00:02, 156772.51it/s]                                                                                                                                                           
/home/ubuntu/work/github.com/himkt/optuna/optuna/_experimental.py:84: ExperimentalWarning

himkt

// Use dev.jsonl for training to reduce computation time.
// local TRAIN_PATH = 'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/dev.jsonl';
local VALIDATION_PATH = 'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/test.jsonl';
local DROPOUT = std.extVar('DROPOUT');
local EMBEDDING_DIM = std.extVar('EMBEDDING_DIM');
local CNN_FIELDS(max_filter_size, embedding_dim, hidden_size, num_filters) = {
  type: 'cnn',
  ngram_filter_sizes: std.range(1, max_filter_size),
  num_filters: num_filters,
  embedding_dim: embedding_dim,

himkt

============================= test session starts ==============================
platform linux -- Python 3.7.7, pytest-5.4.3, py-1.8.2, pluggy-0.13.1
rootdir: /workspaces/optuna
plugins: nbval-0.9.5
collected 10 items

tests/integration_tests/test_fastai.py .                                 [ 10%]
tests/integration_tests/allennlp_tests/test_allennlp.py ....FFFF.        [100%]

=================================== FAILURES ===================================