Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 1 new columns ({'embedding'})

This happened while the json dataset builder was generating data using

hf://datasets/Shouryxx12/Rag-ai/embedding.json (at revision 44dd560c8a806d014c81b42901ce9d8b84af278c), ['hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/combined.json', 'hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/embedding.json'], ['hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/combined.json', 'hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/embedding.json']

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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              chunk_id: int64
              text: string
              start_time: double
              end_time: double
              embedding: list<item: double>
                child 0, item: double
              to
              {'chunk_id': Value('int64'), 'text': Value('string'), 'start_time': Value('float64'), 'end_time': Value('float64')}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 1 new columns ({'embedding'})
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/Shouryxx12/Rag-ai/embedding.json (at revision 44dd560c8a806d014c81b42901ce9d8b84af278c), ['hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/combined.json', 'hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/embedding.json'], ['hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/combined.json', 'hf://datasets/Shouryxx12/Rag-ai@44dd560c8a806d014c81b42901ce9d8b84af278c/embedding.json']
              
              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.

chunk_id
int64
text
string
start_time
float64
end_time
float64
0
This is the son of Sallu Bhai.
0
2
1
He didn't get the break in the polywood.
2
4
2
That's why he learnt Python for two days.
4
7
3
He thought that he would learn Python for two days and get the job of 90LPA.
7
11
4
He got the job of 90LPA.
11
13
5
He got the job of 90LPA.
13
14
6
Hey, why does this always happen?
22
25
7
I don't want to do anything.
25
27
8
Hey, Harry, you.
27
28
9
Hey, how is the show going?
28
30
10
I'm trying to learn Python.
30
32
11
I can't do anything.
32
33
12
When this is the second time, how can I learn Python?
33
36
13
I'm following the road map.
36
38
14
It's not difficult.
38
39
15
These things are all theory-touric.
39
41
16
I'm going to the big loss project.
41
44
17
I don't want to do anything.
44
45
18
I think I'll have to take a new Python course.
50
53
19
How will I learn AI and how to use it?
54
56
20
I think I'll learn Python from this aspect.
56
58
21
Complete Python course with handwritten notes.
69
71
22
How can I learn how to use Python?
71
74
23
This is already ready with all my knowledge.
74
76
24
I've only created this course with this method of learning.
76
80
25
If you learn Python, you'll definitely get the job.
80
85
26
I'm sure you'll find the job.
85
88
27
I've read many chapters of this course.
88
91
28
You've done many practice sets for practice.
91
95
29
We're going to make amazing projects.
95
98
30
They're not called halke.
98
100
31
They're amazing AI projects.
100
102
32
This course has only one prerequisite.
102
104
33
And it's your time.
104
106
34
If you're going to get the time,
106
108
35
you'll have to learn how to use Python from this.
108
112
36
You'll have to learn artificial intelligence programs.
112
114
37
Data science, web development, general scripting.
114
117
38
And this list goes on and on.
117
119
39
How can I do all this?
119
120
40
I'll teach you.
120
121
41
So, let's not waste time.
121
123
42
Let's go to the computer screen and let's get started.
123
126
43
If you want to become a Stack Over Floggy,
131
132
44
Python is the most loved and easiest language.
132
135
45
You don't need to come to any language before doing this course.
135
138
46
If you're a Python, you can easily and easily get the first language.
138
144
47
The aim of this course is that
144
146
48
I'll catch your first programming language.
146
149
49
I'll open my Python Handbook and talk about what is programming.
149
155
50
I'll give you the ultimate Python Handbook.
155
157
51
I'll give you a long-wit cheat sheet along with notes.
157
160
52
I've written a handwritten.
160
161
53
For you, there are many more things like source code
161
164
54
I'll tell you in the next time.
164
166
55
But let's focus on what is programming.
166
169
56
Just like we use Hindi or English.
169
172
57
You communicate with each other.
172
174
58
We use programming language like Python to communicate with the computer.
174
177
59
If we want to communicate with the computer,
177
180
60
then we can't do this.
180
182
61
You can't do this.
182
184
62
No, we can't do this.
184
186
63
We'll have to write a proper program.
186
188
64
And we'll write a programming language.
188
190
65
Just like a Chinese person.
190
193
66
You talk about Chinese.
193
195
67
You talk about French.
195
197
68
The language that you like is called programming language.
197
200
69
It's like a computer.
200
202
70
It's called programming language.
202
204
71
See, C++, Java, Rust, Ruby, GoLang, JavaScript.
204
208
72
These are all programming languages.
208
210
73
But in this video, we'll learn Python.
210
212
74
I've chosen Python as your first language.
212
216
75
Because Python is simple.
216
218
76
It's easy to understand.
218
219
77
And it feels like you're reading simple English.
219
222
78
And I'm not saying that.
222
224
79
People are saying that they've learned Python.
224
226
80
And this is the general opinion of the industry.
226
228
81
Python is so easy to understand that it's easy to learn simple English.
228
231
82
Now, you'll see the whole course.
231
233
83
This course is very big.
233
235
84
In fact, this course is very small.
235
237
85
This course is not very big.
237
238
86
Because I've learned all the Python along with projects.
238
241
87
Along with my experience.
241
243
88
Which I've gained after struggling so much.
243
246
89
I've learned so much from all the things I've learned.
246
248
90
I'll teach you a video.
248
250
91
So, this video is a small boss.
250
252
92
This pseudo code nature Python.
252
254
93
The easy to understand nature.
254
256
94
It feels like you're reading English.
256
258
95
This nature makes this understandable among beginners.
258
261
96
As you can see, this is the 5 numbers program.
261
265
97
Which I've written 10 lines of phone.
265
268
98
Now, you can see this program.
268
270
99
In these 3 programs, we'll distribute a trip expense.
270
273
End of preview.

No dataset card yet

Downloads last month
-