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--- |
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dataset_info: |
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features: |
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- name: response_words |
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dtype: int64 |
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- name: label |
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dtype: string |
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- name: conversations |
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list: |
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- name: from |
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dtype: string |
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- name: value |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 61858860.826112196 |
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num_examples: 13302 |
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download_size: 39125513 |
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dataset_size: 61858860.826112196 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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I forgot if this dataset is the dirty version of Reddit Writing Prompts or not, it's probably a mix of both. |
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The data was filtered and classified using [Lilac](https://www.lilacml.com/) with two embedding models: |
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- [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) |
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- [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) |
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(Note: Lilac is amazing BTW, and the UI is nice. Highly recommended for data processing tasks) |
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The dataset has been converted to ShareGPT format, including word counts for responses and labeled perspectives. While the labeling may not be 100% accurate, ambiguous cases have been labeled separately with their perspectives excluded from the prompts. |
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Non-story content has been removed, though some examples may have been missed. Some non-story content was purposefully kept if it was closely related to the prompt (like relevant responses) - it's a bit hard to draw a clear line sometimes. Stories containing unwanted words or sentences were filtered based on personal preferences. Since "slop" is subjective and lacks a standardized definition, you may need to perform additional cleaning before using this dataset for training. |
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PS: MY MAIN ACCOUNT IS A MESS AND THE STORAGE IS FULL, SO I CREATED THIS 'ORGANIZATION' TO DUMP MY MODELS AND DATASETS |
