dippatel11/autotrain-dippatel_summarizer-2331873599
Summarization • Updated • 3
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YAML Metadata Warning:The task_categories "conditional-text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, 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-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-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, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
AutoTrain Dataset for project: dippatel_summarizer
Dataset Description
This dataset has been automatically processed by AutoTrain for project dippatel_summarizer.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"feat_id": "13864393",
"text": "Peter: So have you gone to see the wedding?\nHolly: of course, it was so exciting\nRuby: I really don't understand what's so exciting about it\nAngela: me neither\nHolly: because it's the first person of colour in any Western royal family\nRuby: is she?\nPeter: it's not true\nHolly: no?\nPeter: there is a princess in Liechtenstein\nPeter: I think a few years ago a prince of Liechtenstein married a woman from Africa\nPeter: and it was the first case of this kind among European ruling dynasties\nHolly: what? I've never heard of it\nPeter: wait, I'll google it\nRuby: interesting\nPeter: here: <file_other>\nPeter: Princess Angela von Liechtenstein, born Angela Gisela Brown\nPeter: sorry, she's from Panama, but anyway of African descent\nRuby: right! but who cares about Liechtenstein?!\nPeter: lol, I just noticed that it's not true, what you wrote\nRuby: I'm excited anyway, she's the first in the UK for sure",
"target": "Holly went to see the royal wedding. Prince of Liechtenstein married a Panamanian woman of African descent."
},
{
"feat_id": "13716378",
"text": "Max: I'm so sorry Lucas. I don't know what got into me.\nLucas: .......\nLucas: I don't know either.\nMason: that was really fucked up Max\nMax: I know. I'm so sorry :(.\nLucas: I don't know, man.\nMason: what were you thinking??\nMax: I wasn't.\nMason: yea\nMax: Can we please meet and talk this through? Please.\nLucas: Ok. I'll think about it and let you know.\nMax: Thanks...",
"target": "Max is sorry about his behaviour so wants to meet up with Lucas and Mason. Lucas will let him know. "
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"feat_id": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
|---|---|
| train | 2400 |
| valid | 600 |
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