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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'check_flagged_words_criteria', 'check_stop_word_ratio_criteria', 'check_char_repetition_criteria'})
This happened while the json dataset builder was generating data using
hf://datasets/CarperAI/pile-v2-small-filtered/data/CodePileReddit2019/data.json (at revision e2f37e95cc5eb38359b6aefc2cbf98a50fd1b7e4)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
meta: string
id: int64
check_char_repetition_criteria: double
check_flagged_words_criteria: double
check_stop_word_ratio_criteria: double
to
{'id': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'meta': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'check_flagged_words_criteria', 'check_stop_word_ratio_criteria', 'check_char_repetition_criteria'})
This happened while the json dataset builder was generating data using
hf://datasets/CarperAI/pile-v2-small-filtered/data/CodePileReddit2019/data.json (at revision e2f37e95cc5eb38359b6aefc2cbf98a50fd1b7e4)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | text string | meta string |
|---|---|---|
97090 | """
## Binary Classification using Graduate Admission Dataset
This notebook compares performance of various Machine Learning classifiers on the "Graduate Admission" data. I'm still just a naive student implementing Machine Learning techniques. You're most welcome to suggest me edits on this kernel, I am happy to learn... | {'source': 'AI4Code', 'id': 'b24cf5394d60f5'} |
116041 | """
#### this notebook is part of the documentation on my HPA approach
-> main notebook: https://www.kaggle.com/philipjamessullivan/0-hpa-approach-summary
## 7: network training
-> https://www.kaggle.com/philipjamessullivan/7-train-effnetb0-version-a-part-1
-> https://www.kaggle.com/philipjamessulliva... | {'source': 'AI4Code', 'id': 'd5668563d2cdd3'} |
37319 | """
* V14: train with tri grams and generate new vocab, num feat = 15000
"""
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import num... | {'source': 'AI4Code', 'id': '44b1ea84dff48e'} |
48500 | """
# NGBoost やってみたメモ
* modelのチューニングはきちんとやっていません。なのでどちらが性能がいいかはわかりませんが、えいやっと使った感触ではこれくらいのデータなら遜色なかったです。
* 分布が算出できるのは使いどころがあるかもですね。
"""
!pip install ngboost
# basic libraries
import pandas as pd
import numpy as np
import numpy.random as rd
import gc
import multiprocessing as mp
import os
import sys
import pickle
from ... | {'source': 'AI4Code', 'id': '5947bd9ad5be6f'} |
106369 | """
## Attention
I'm not good at English. If you find a mistake, let me know, please.
## 0. Abstract
Interestingly, it's a very interesting phenomenon that global transformation by a the Moon's tide stress seems to be a trigger of occurrence for a disastrous earthquake (M>=5.5).
It is found out that some statistica... | {'source': 'AI4Code', 'id': 'c367d886e07c8d'} |
124491 | """
<div align='center'><font size="5" color='#353B47'>A Notebook dedicated to Stacking/Ensemble methods</font></div>
<div align='center'><font size="4" color="#353B47">Unity is strength</font></div>
<br>
<hr>
"""
"""
In this notebook, i'm going to cover various Prediction Averaging/Blending Techniques:
1. Simple Aver... | {'source': 'AI4Code', 'id': 'e4f4a0ec2c64df'} |
21198 | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.metrics import classification_report
from sklearn.metrics import ... | {'source': 'AI4Code', 'id': '26e50916853f51'} |
11111 | """
Used methods:
* Convolutional Neural Network
* Data Augmentation
"""
"""
**Scientists want an automatic system to recognize whale species when monitoring their activities with a surveillance system. Thus, in this competition, numerous pictures of whales’ tales are given to identify whales species.
In the train se... | {'source': 'AI4Code', 'id': '14691788621618'} |
85257 | """
To enable autocomplete in kaggel just run this in consol
%config Completer.use_jedi = False
"""
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages... | {'source': 'AI4Code', 'id': '9c762b92119e2d'} |
60268 | """
## HOG, or Histogram of Oriented Gradients, is a feature descriptor that is often used to extract features from image data. It is widely used in computer vision tasks for object detection**
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotl... | {'source': 'AI4Code', 'id': '6f155818a7cfec'} |
87716 | """
# <Center>Premier League Player Analysis<Center>
"""
"""
# Importing the Libraries
"""
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import plotly.figure_factory as ff
import plotly.graph_objects as go
import numpy as np
import plotly.express as px
import os
for dirname, _, filenames in ... | {'source': 'AI4Code', 'id': 'a0da8db1b2bc00'} |
119119 | # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g... | {'source': 'AI4Code', 'id': 'db24f9fd5ba6bb'} |
End of preview.
Dataset Description
A small subset in each dataset of pile-v2(~1000 samples) of pile-v2 dataset, each has 1,000 random samples from the original dataset. The dataset has 255MB of text (code and english).
Languages
The dataset contains technical text on programming languages and natural language with the following subsets,
- Bible
- TED2020
- PileOfLaw
- StackExchange
- GithubIssues
- Opensubtitles
- USPTO
- S2ORC
- DevDocs
- CodePileReddit2022
- USENET
- GNOME
- ASFPublicMail
- PileV2Reddit2020
- CodePilePosts
- Discourse
- Tanzil
- arXiv
- UbuntuIRC
- PubMed
- CodePileReddit2020
- CodePileReddit2021
- GlobalVoices
- FreeLaw_Options
- PileV2Posts
Dataset Structure
from datasets import load_dataset
load_dataset("CarperAI/pile-v2-small")
How to use it
You can either load the whole dataset like above, or load a specific subset such as arxiv by specifying the folder directory:
load_dataset("CarperAI/pile-v2-small", data_dir="data/arxiv")
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