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adding images from 2024-01-27 audio generation run

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  1. README.md +80 -1
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README.md CHANGED
@@ -1,3 +1,82 @@
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  ---
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- license: unknown
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license:
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+ - other
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+ pretty_name: >-
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+ python copilot audio training using class with knowledge graphs collected on 2024-01-27
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+ dataset_info:
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+ - config_name: view_schema
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+ splits:
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+ - name: view_schema
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+ configs:
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+ - config_name: view_schema
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+ data_files:
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+ - split: view_schema
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+ path: files/lok-python-copilot-audio.class-v1_00000717.parquet
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+ size_categories:
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+ - 100K<n<1M
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+ tags:
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+ - python-copilot
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+ - python-coding
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+ - python-architecture
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+ - knowledge-graphs
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+ - multimodal
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+ - text-image-audio
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+ - fine-tuning
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+ - training
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+ - question-answering
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+ - image-knowledge-graph
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+ - alpaca
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+ - mp3
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+ - png
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+ - text
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+ - instruct
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+ - class
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+ - classes
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+ # supported task_categories
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+ # 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
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+ task_categories:
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+ - text-to-audio
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+ - audio-to-audio
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+ - question-answering
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+ # supported task_ids
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+ # 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
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+ task_ids:
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+ - parsing
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  ---
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+
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+ ## Python Copilot Audio Training using Class with Knowledge Graphs
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+
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+ 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.
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+
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+ ### Details
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+
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+ Each class method 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.
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+
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+ - Rows: 211020
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+ - Size: 284.6 GB
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+ - Data type: mp3
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+ - Format: narrated alpaca question and answer pairs using two voices
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+
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+ ### Schema
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+
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+ ```
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+ {
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+ "audio_path": "string",
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+ "audio_type": "string",
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+ "dbytes": "string",
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+ "dbytes_len": "int64",
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+ "file_path": "string",
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+ "file_path_len": "int64",
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+ "lang": "string",
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+ "lang_len": "int64",
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+ "recsize": "int64"
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+ }
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+ ```
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+
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+ ### How to use the dataset
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("matlok/python-audio-copilot-training-using-class-knowledge-graphs-2024-01-27", data_dir="files")
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+ ```
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