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language: en |
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tags: |
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- augmentation |
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license: apache-2.0 |
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datasets: |
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- C4 |
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widget: |
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- text: "<mask> machine learning <mask> my reserach interest <mask> data science <mask>" |
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--- |
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# SEGA-large model |
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SEGA: SkEtch-based Generative Augmentation |
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SEGA is a general text augmentation model that can be used for data augmentation for various NLP tasks (including sentiment analysis, topic classification, NER, and QA). SEGA uses an encoder-decoder structure (based on the BART architecture) and is pre-trained on the C4-realnewslike corpus. |
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- Paper: [this paper](to_be_added) |
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- Github: [this repository](to_be_added). |
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## Model description |
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## Model variations |
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| Model | #params | Language | |
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|------------------------|--------------------------------|-------| |
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| [`sega-large`]() | xM | English | |
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| [`sega-base`]() | xM | English | |
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| [`sega-small`]() | xM | English | |
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| [`sega-large-chinese`]() | xM | Chinese | |
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| [`sega-base-chinese`]() | xM | Chinese | |
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| [`sega-small-chinese`]() | xM | Chinese | |
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## Intended uses & limitations |
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### How to use |
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### Limitations and bias |
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## Training data |
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## Training procedure |
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### Preprocessing |
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### Pretraining |
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## Evaluation results |
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### BibTeX entry and citation info |
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