| license: | |
| - other | |
| pretty_name: >- | |
| python copilot audio training using inheritance and polymorphism knowledge graphs | |
| dataset_info: | |
| - config_name: view_schema | |
| splits: | |
| - name: view_schema | |
| configs: | |
| - config_name: view_schema | |
| data_files: | |
| - split: view_schema | |
| path: files/lok-python-copilot-audio.base-v1_00000291.parquet | |
| size_categories: | |
| - 10K<n<100K | |
| 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 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. | |
| - Rows: 96874 | |
| - Size: 29.9 GB | |
| - Data type: mp3 | |
| - Format: narrated alpaca question and answers using two voices | |
| ### 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") | |
| ``` | |