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Error code: FeaturesError Exception: ArrowInvalid Message: JSON parse error: Column(/personalities/[]/[]) changed from string to number in row 0 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables df = pandas_read_json(f) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json return pd.read_json(path_or_buf, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json return json_reader.read() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read obj = self._get_object_parser(self.data) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser obj = FrameParser(json, **kwargs).parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse self._parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, in _parse ujson_loads(json, precise_float=self.precise_float), dtype=None ValueError: Trailing data During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3212, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2051, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1677, in __iter__ for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 299, in __iter__ for key, pa_table in self.generate_tables_fn(**gen_kwags): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables raise e File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/personalities/[]/[]) changed from string to number in row 0
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FarisHijazi/kajiwoto.ai-chat
Dataset Description
This dataset is a processed version of the FarisHijazi/kajiwoto.ai-chat dataset, containing extracted and re-formatted conversation data from Kajiwoto. Kajiwoto is a platform where users can create and interact with AI character datasets. This version focuses on providing the core conversational data in a ShareGPT-like format.
Important Note: This data is still in a raw format and requires careful consideration before use in training.
- Size: 13 784 rows
Data Fields
The dataset contains the following fields, mirroring the original Kajiwoto dataset structure:
id
: Unique identifier for the character.name
: Name of the character.description
: Character description (often empty).profilePhotoUri
: URI of the character's profile photo (may be null).dominantColors
: List of dominant colors in the profile photo (may be null).personalities
: Personality tags (may be null).nsfw
: Boolean indicating whether the character is marked as "not safe for work."conversations
: A list of turns representing a conversation. Each turn has:from
: Either "human" or "gpt" indicating the speaker.value
: The text of the message.
upvotes
: Number of upvotes the character has received.
Data Format
The conversations
field is formatted in a ShareGPT-like structure, where each turn indicates the speaker ("human" or "gpt") and the message content:
{
"id": "M51k",
"name": "Damian ",
"description": "",
"profilePhotoUri": null,
"dominantColors": null,
"personalities": null,
"nsfw": false,
"conversations": [
{
"from": "human",
"value": "Still a horrific introvert?"
},
{
"from": "gpt",
"value": "I know you wanted me to find some friends, but I've already got one here in Gotham and one is more than enough"
},
{
"from": "human",
"value": "How is the friend making going "
},
{
"from": "gpt",
"value": "I know you wanted me to find some friends, but I've already got one here in Gotham and one is more than enough"
},
{
"from": "human",
"value": "You're not very good at this relax and have fun thing are you"
},
{
"from": "gpt",
"value": "I wasn't raised to have fun"
},
...
],
"upvotes": 0
}
Intended Uses
This dataset is intended for research and experimentation in areas such as:
- AI character development
- Dialogue generation
- Role-playing AI
- Analyzing conversational patterns
Caveats and Recommendations
Preprocessing Considerations:
- Filtering:
- Upvotes: Consider filtering by the number of upvotes. However, be aware that profiles with fewer views might have lower upvotes, even if the quality is high.
- NSFW: The
nsfw
field may not be completely reliable. Some users engage in NSFW chats with characters not marked as NSFW. Manual inspection may be required.
- Text Processing:
- The text contains special markup that needs to be handled:
#scene=happy
: Scene descriptions.%{Howdy|G'day|Yo}
: Multiple possible options for a phrase.- Pronouns: Requires handling for consistency.
- The text contains special markup that needs to be handled:
Limitations
- The data might not be sufficiently filtered for training without further processing.
- Many short, repetitive messages may not accurately represent character behavior.
- The dataset contains a mixture of good and bad quality conversations.
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