Dataset Preview
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
- -