license: | |
- other | |
pretty_name: >- | |
python copilot audio training using inheritance and polymorphism with knowledge graphs | |
dataset_info: | |
- config_name: view_schema | |
splits: | |
- name: schema | |
configs: | |
- config_name: view_schema | |
data_files: | |
- split: view_schema | |
path: files/lok-python-copilot-audio.base-v1_00000291.parquet | |
size_categories: | |
- 0<n<1k | |
tags: | |
- python-copilot | |
- python-coding | |
- python-architecture | |
- knowledge-graphs | |
- multimodal | |
- text-image-audio | |
- fine-tuning | |
- training | |
- question-answering | |
- image-knowledge-graph | |
- alpaca | |
- mp3 | |
- png | |
- text | |
- instruct | |
- inheritance | |
# supported task_categories | |
# text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other | |
task_categories: | |
- text-to-audio | |
- audio-to-audio | |
- question-answering | |
# supported task_ids | |
# acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering | |
task_ids: | |
- parsing | |
## Python Copilot Audio Training using Inheritance and Polymorphism with Knowledge Graphs | |
This dataset is a subset of the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset. | |
### Details | |
Each base class for each unique class in each module file has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet **dbytes** column and the associated source code **file_path** identifier. | |
- Size: 29.9 GB | |
- Audio playtime: ~103 days | |
- Data type: mp3 | |
- Format: yaml with alpaca | |
### Schema | |
``` | |
{ | |
"audio_path": "string", | |
"audio_type": "string", | |
"dbytes": "binary", | |
"dbytes_len": "int64", | |
"file_path": "string", | |
"file_path_len": "int64", | |
"lang": "string", | |
"lang_len": "int64", | |
"recsize": "int64" | |
} | |
``` | |
### How to use the dataset | |
```python | |
from datasets import load_dataset | |
ds = load_dataset("matlok/python-audio-copilot-training-using-inheritance-knowledge-graphs", data_dir="files") | |
``` | |