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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)

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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'}
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