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feat: add proper datagen

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uetchy 2021-09-09 01:14:21 +09:00
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6
CONTRIBUTING.md
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@ -1,5 +1,11 @@
# Contribution Guide
## Setup
```bash
poetry install
```
## Generate dataset
```bash

4
Makefile
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@ -1,7 +1,7 @@
all: build upload
build:
python3 -m sensai_gen.cli
python3 -m sensai_dataset.gen
upload:
kaggle datasets version -d -m "New version" --path $$DATASET_DIR
kaggle datasets version -m "New version" --path $$DATASET_DIR

62
README.md
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@ -1,8 +1,10 @@
# ❤️‍🩹 Sensai: Toxic Chat Dataset
Sensai is a dataset consists of live chats from all across Virtual YouTubers' live streams, ready for training toxic chat classification models.
Sensai is a toxic chat dataset consists of live chats from Virtual YouTubers' live streams.
Download the dataset from [Kaggle Datasets](https://www.kaggle.com/uetchy/sensai) and join `#livechat-dataset` channel on [holodata Discord](https://holodata.org/discord) for discussions.
Download the dataset from [Huggingface Hub](https://huggingface.co/datasets/holodata/sensai) or alternatively from [Kaggle Datasets](https://www.kaggle.com/uetchy/sensai).
Join `#livechat-dataset` channel on [holodata Discord](https://holodata.org/discord) for discussions.
## Provenance
@ -23,7 +25,7 @@ See [public notebooks](https://www.kaggle.com/uetchy/sensai/code) for ideas.
| filename | summary | size |
| ------------------------- | -------------------------------------------------------------- | -------- |
| `chats_flagged_%Y-%m.csv` | Chats flagged as either deleted or banned by mods (3,100,000+) | ~ 400 MB |
| `chats_nonflag_%Y-%m.csv` | Non-flagged chats (3,000,000+) | ~ 300 MB |
| `chats_nonflag_%Y-%m.csv` | Non-flagged chats (3,100,000+) | ~ 300 MB |
To make it a balanced dataset, the number of `chats_nonflags` is adjusted (randomly sampled) to be the same as `chats_flagged`.
Ban and deletion are equivalent to `markChatItemsByAuthorAsDeletedAction` and `markChatItemAsDeletedAction` respectively.
@ -35,38 +37,42 @@ Ban and deletion are equivalent to `markChatItemsByAuthorAsDeletedAction` and `m
| column | type | description |
| --------------- | ------ | ---------------------------- |
| body | string | chat message |
| membership | string | membership status |
| authorChannelId | string | anonymized author channel id |
| channelId | string | source channel id |
| label | enum | toxic,spam,safe |
#### Membership status
## Usage
| value | duration |
| ----------------- | ------------------------- |
| unknown | Indistinguishable |
| non-member | 0 |
| less than 1 month | < 1 month |
| 1 month | >= 1 month, < 2 months |
| 2 months | >= 2 months, < 6 months |
| 6 months | >= 6 months, < 12 months |
| 1 year | >= 12 months, < 24 months |
| 2 years | >= 24 months |
#### Pandas usage
Set `keep_default_na` to `False` and `na_values` to `''` in `read_csv`. Otherwise, chat message like `NA` would incorrectly be treated as NaN value.
https://huggingface.co/docs/datasets/loading_datasets.html
```python
import pandas as pd
from glob import iglob
# $ pip3 install datasets
from datasets import load_dataset, Features, ClassLabel, Value
flagged = pd.concat([
pd.read_csv(f,
na_values='',
keep_default_na=False)
for f in iglob('../input/sensai/chats_flagged_*.csv')
],
ignore_index=True)
dataset = load_dataset("holodata/sensai",
features=Features(
{
"body": Value("string"),
"toxic": ClassLabel(num_classes=2, names=['0', '1'])
}
))
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["body"], padding="max_length", truncation=True)
tokenized_datasets = dataset['train'].shuffle().select(range(50000)).map(tokenize_function, batched=True)
tokenized_datasets.rename_column_("toxic", "label")
splitset = tokenized_datasets.train_test_split(0.2)
training_args = TrainingArguments("test_trainer")
trainer = Trainer(
model=model, args=training_args, train_dataset=splitset['train'], eval_dataset=splitset['test']
)
trainer.train()
```
## Consideration

354
notebooks/playground.ipynb vendored Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"source": [
"from datasets import load_dataset, Features\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
"import os\n",
"from os.path import join\n",
"import pandas as pd\n",
"from datasets import ClassLabel, Value\n",
"\n",
"# https://huggingface.co/docs/datasets/loading_datasets.html\n",
"\n",
"DATASET_DIR = os.environ['DATASET_DIR']"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"dataset = load_dataset(\"holodata/sensai\", features=Features(\n",
" {\n",
" \"body\": Value(\"string\"),\n",
" \"toxic\": ClassLabel(num_classes=2, names=['0', '1'])\n",
" }\n",
" ))\n",
"dataset = dataset['train']"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "704789c10f1e44ddbb83262b8a826eec"
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Using custom data configuration sensai-4d9ed81389161083\n",
"Reusing dataset parquet (/home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a)\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8acdde0c4caa4d4698f97ef29993195b"
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"dataset.features"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'body': Value(dtype='string', id=None),\n",
" 'toxic': ClassLabel(num_classes=2, names=['0', '1'], names_file=None, id=None)}"
]
},
"metadata": {},
"execution_count": 3
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"source": [
"samples = dataset.shuffle().select(range(50000))\n",
"\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"body\"], padding=\"max_length\", truncation=True)\n",
"\n",
"tokenized_datasets = samples.map(tokenize_function, batched=True)\n",
"tokenized_datasets.rename_column_(\"toxic\", \"label\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading cached shuffled indices for dataset at /home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a/cache-24e3dd769ef2f1b7.arrow\n",
"Loading cached processed dataset at /home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a/cache-8395f066c72e57d7.arrow\n",
"/tmp/ipykernel_4082765/2982913603.py:7: FutureWarning: rename_column_ is deprecated and will be removed in the next major version of datasets. Use Dataset.rename_column instead.\n",
" tokenized_datasets.rename_column_(\"toxic\", \"label\")\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"splitset = tokenized_datasets.train_test_split(0.2)\n",
"splitset"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['attention_mask', 'body', 'input_ids', 'token_type_ids', 'label'],\n",
" num_rows: 40000\n",
" })\n",
" test: Dataset({\n",
" features: ['attention_mask', 'body', 'input_ids', 'token_type_ids', 'label'],\n",
" num_rows: 10000\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 6
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"source": [
"training_args = TrainingArguments(\"test_trainer\")\n",
"trainer = Trainer(\n",
" model=model, args=training_args, train_dataset=splitset['train'], eval_dataset=splitset['test']\n",
")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"source": [
"trainer.train(resume_from_checkpoint=True)"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading model from test_trainer/checkpoint-12500).\n",
"The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: body.\n",
"***** Running training *****\n",
" Num examples = 40000\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 15000\n",
" Continuing training from checkpoint, will skip to saved global_step\n",
" Continuing training from epoch 2\n",
" Continuing training from global step 12500\n",
" Will skip the first 2 epochs then the first 2500 batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` flag to your launch command, but you will resume the training on data already seen by your model.\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d749dea90f914485916ce82c65e73fe6"
},
"text/plain": [
" 0%| | 0/2500 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Didn't find an RNG file, if you are resuming a training that was launched in a distributed fashion, reproducibility is not guaranteed.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='15000' max='15000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [15000/15000 31:45, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>13000</td>\n",
" <td>0.687500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13500</td>\n",
" <td>0.686300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14000</td>\n",
" <td>0.637900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14500</td>\n",
" <td>0.643200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15000</td>\n",
" <td>0.627700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to test_trainer/checkpoint-13000\n",
"Configuration saved in test_trainer/checkpoint-13000/config.json\n",
"Model weights saved in test_trainer/checkpoint-13000/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-13500\n",
"Configuration saved in test_trainer/checkpoint-13500/config.json\n",
"Model weights saved in test_trainer/checkpoint-13500/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-14000\n",
"Configuration saved in test_trainer/checkpoint-14000/config.json\n",
"Model weights saved in test_trainer/checkpoint-14000/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-14500\n",
"Configuration saved in test_trainer/checkpoint-14500/config.json\n",
"Model weights saved in test_trainer/checkpoint-14500/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-15000\n",
"Configuration saved in test_trainer/checkpoint-15000/config.json\n",
"Model weights saved in test_trainer/checkpoint-15000/pytorch_model.bin\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=15000, training_loss=0.10941998901367188, metrics={'train_runtime': 1918.0916, 'train_samples_per_second': 62.562, 'train_steps_per_second': 7.82, 'total_flos': 3.24994775580672e+16, 'train_loss': 0.10941998901367188, 'epoch': 3.0})"
]
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}
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}

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poetry.lock generated
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23
pyproject.toml
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@ -1,22 +1,23 @@
[tool.poetry]
name = "sensai"
name = "sensai-dataset"
version = "1.0.0"
description = "Toxic Live Chat Dataset"
authors = ["Yasuaki Uechi <y@uechi.io>"]
[tool.poetry.dependencies]
python = "^3.9"
kaggle = "^1.5.12"
numpy = "^1.21.0"
pandas = "^1.2.3"
pymongo = "^3.11.3"
python-dateutil = "^2.8.1"
altair = "^4.1.0"
matplotlib = "^3.4.2"
streamlit = "^0.87.0"
plotly = "^5.0.0"
python = "^3.8"
datasets = {git = "https://github.com/huggingface/datasets.git", rev = "master"}
#datasets = "^1.11.0"
pyarrow = "^5.0.0"
[tool.poetry.dev-dependencies]
pymongo = "^3.11.3"
transformers = "^4.10.0"
torch = "^1.9.0"
tokenizers = "^0.10.3"
kaggle = "^1.5.12"
pandas = "^1.2.3"
python-dateutil = "^2.8.1"
ipykernel = "^6.2.0"
yapf = "^0.31.0"

0
sensai/__init__.py → sensai_dataset/__init__.py
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13
sensai_dataset/dataset.py Normal file
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@ -0,0 +1,13 @@
from datasets.features import ClassLabel, Features, Value
from datasets.load import load_dataset
def load_sensai_dataset():
dataset = load_dataset(
"holodata/sensai",
features=Features({
"body": Value("string"),
"toxic": ClassLabel(num_classes=2, names=['0', '1'])
}))
dataset = dataset['train']
return dataset

16
sensai_dataset/generator/__init__.py Normal file
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@ -0,0 +1,16 @@
import argparse
from sensai_dataset.generator.commands import generate_dataset
from sensai_dataset.generator.constants import DATASET_DIR, DATASET_SOURCE_DIR
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='dataset generator')
parser.add_argument('-m', '--matcher', type=str, default='chats_*.csv')
args = parser.parse_args()
print('target: ' + DATASET_DIR)
print('source: ' + DATASET_SOURCE_DIR)
generate_dataset(source_dir=DATASET_SOURCE_DIR,
target_dir=DATASET_DIR,
matcher=args.matcher)

91
sensai_dataset/generator/commands.py Normal file
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@ -0,0 +1,91 @@
import gc
from glob import iglob
from os.path import basename, join, splitext
import pandas as pd
def generate_dataset(source_dir, target_dir, matcher):
print('[generate_sensai_dataset]')
delet_path = join(source_dir, 'deletion_events.csv')
del_events = pd.read_csv(delet_path, usecols=['id', 'retracted'])
del_events = del_events.query('retracted == 0').copy()
del_events.drop(columns=['retracted'], inplace=True)
del_events['label'] = 'toxic'
ban_path = join(source_dir, 'ban_events.csv')
ban_events = pd.read_csv(ban_path, usecols=['authorChannelId', 'videoId'])
ban_events['label'] = 'spam'
for f in sorted(iglob(join(source_dir, matcher))):
period_string = splitext(basename(f))[0].split('_')[1]
print('>>> Period:', period_string)
# load chat
print('>>> Loading chats')
chat_path = join(source_dir, 'chats_' + period_string + '.csv')
chats = pd.read_csv(chat_path,
na_values='',
keep_default_na=False,
usecols=[
'authorChannelId',
'videoId',
'id',
'body',
])
# remove NA
chats = chats[chats['body'].notna()]
# apply mods
print('>>> Merging bans')
chats = pd.merge(chats,
ban_events,
on=['authorChannelId', 'videoId'],
how='left')
# apply mods
print('>>> Merging deletion')
chats = pd.merge(chats, del_events, on='id', how='left')
# fill NA label
chats['label'].fillna('safe', inplace=True)
isFlagged = chats['label'] != 'safe'
flagged = chats[isFlagged].copy()
# to make balanced dataset
nbFlagged = flagged.shape[0]
if nbFlagged == 0:
continue
print('>>> Sampling nonflagged chats')
print('nbFlagged', nbFlagged)
nonflag = chats[~isFlagged].sample(nbFlagged)
print('>>> Writing dataset')
# NOTE: do not use categorical type with to_parquest. otherwise, it will be failed to load them with huggingface's Dataset
columns_to_delete = [
'authorChannelId',
'videoId',
'id',
]
flagged.drop(columns=columns_to_delete, inplace=True)
flagged.to_parquet(join(target_dir,
f'chats_flagged_{period_string}.parquet'),
index=False)
nonflag.drop(columns=columns_to_delete, inplace=True)
nonflag.to_parquet(join(target_dir,
f'chats_nonflag_{period_string}.parquet'),
index=False)
# free up memory
del nonflag
del flagged
del chats
gc.collect()

2
sensai_gen/constants.py → sensai_dataset/generator/constants.py
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@ -4,4 +4,4 @@ DATASET_DIR = os.environ['DATASET_DIR']
DATASET_SOURCE_DIR = os.environ['DATASET_SOURCE_DIR']
os.makedirs(DATASET_DIR, exist_ok=True)
os.makedirs(DATASET_SOURCE_DIR, exist_ok=True)
os.makedirs(DATASET_SOURCE_DIR, exist_ok=True)

0
sensai_gen/__init__.py
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123
sensai_gen/cli.py
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@ -1,123 +0,0 @@
import gc
from glob import iglob
import argparse
import shutil
from os.path import basename, join, splitext
import numpy as np
import pandas as pd
from sensai_gen.constants import DATASET_DIR, DATASET_SOURCE_DIR
def load_channels(**kwargs):
dtype_dict = {
'channelId': 'category',
'name': 'category',
'englishName': 'category',
'affiliation': 'category',
'group': 'category',
'subscriptionCount': 'int32',
'videoCount': 'int32',
'photo': 'category'
}
channels = pd.read_csv(join(DATASET_SOURCE_DIR, 'channels.csv'),
dtype=dtype_dict,
**kwargs)
return channels
def generate_dataset(matcher):
print('[generate_sensai_dataset]')
delet_path = join(DATASET_SOURCE_DIR, 'deletion_events.csv')
del_events = pd.read_csv(delet_path, usecols=['id', 'retracted'])
del_events = del_events.query('retracted == 0').copy()
del_events.drop(columns=['retracted'], inplace=True)
del_events['deleted'] = True
ban_path = join(DATASET_SOURCE_DIR, 'ban_events.csv')
ban_events = pd.read_csv(ban_path, usecols=['authorChannelId', 'videoId'])
ban_events['banned'] = True
for f in sorted(iglob(join(DATASET_SOURCE_DIR, matcher))):
period_string = splitext(basename(f))[0].split('_')[1]
print('>>> Period:', period_string)
columns_to_use = [
'body',
'authorChannelId',
'channelId',
'membership',
'id',
'videoId',
]
columns_to_delete = [
'id',
'videoId',
'deleted',
'banned',
]
# load chat
print('>>> Loading chats')
chat_path = join(DATASET_SOURCE_DIR, 'chats_' + period_string + '.csv')
chat_dtype = {
'authorChannelId': 'category',
'membership': 'category',
'videoId': 'category',
'channelId': 'category'
}
chats = pd.read_csv(chat_path, dtype=chat_dtype, usecols=columns_to_use)
# apply mods
print('>>> Merging deletion')
chats = pd.merge(chats, del_events, on='id', how='left')
chats['deleted'].fillna(False, inplace=True)
# apply mods
print('>>> Merging bans')
chats = pd.merge(chats,
ban_events,
on=['authorChannelId', 'videoId'],
how='left')
chats['banned'].fillna(False, inplace=True)
flagged = chats[(chats['deleted'] | chats['banned'])].copy()
# to make balanced dataset
nbFlagged = flagged.shape[0]
if nbFlagged == 0:
continue
print('>>> Sampling nonflagged chats')
print('nbFlagged', nbFlagged)
nonflag = chats[~(chats['deleted'] | chats['banned'])].sample(nbFlagged)
print('>>> Writing dataset')
flagged.drop(columns=columns_to_delete, inplace=True)
flagged.to_csv(join(DATASET_DIR, f'chats_flagged_{period_string}.csv'),
index=False)
nonflag.drop(columns=columns_to_delete, inplace=True)
nonflag.to_csv(join(DATASET_DIR, f'chats_nonflag_{period_string}.csv'),
index=False)
# free up memory
del nonflag
del flagged
del chats
gc.collect()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='dataset generator')
parser.add_argument('-m', '--matcher', type=str, default='chats_*.csv')
args = parser.parse_args()
print('target: ' + DATASET_DIR)
print('source: ' + DATASET_SOURCE_DIR)
shutil.copy(join(DATASET_SOURCE_DIR, 'channels.csv'), DATASET_DIR)
generate_dataset(matcher=args.matcher)