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--- |
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language: en |
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tags: |
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- data augmentation |
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- keywords-to-text generation |
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- sketch-to-text generation |
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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> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>" |
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example_title: "Example 1" |
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- text: "<mask> machine learning <mask> my research interest <mask> data science <mask>" |
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example_title: "Example 2" |
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- text: "<mask> play basketball <mask> a strong team <mask> Shanghai University of Finance and Economics <mask> last Sunday <mask>" |
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example_title: "Example 3" |
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- text: "Good news: <mask> the European Union <mask> month by EU <mask> Farm Commissioner Franz <mask>" |
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example_title: "Example with a prompt 1" |
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- text: "Bad news: <mask> the European Union <mask> month by EU <mask> Farm Commissioner Franz <mask>" |
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example_title: "Example with a prompt 2" |
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inference: |
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parameters: |
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max_length: 200 |
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num_beams: 3 |
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do_sample: True |
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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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### How to use |
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```python |
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from transformers import pipeline |
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# 1. load the model with the huggingface `pipeline` |
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sega = pipeline("text2text-generation", model='beyond/sega-large', device=0) |
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# 2. provide a sketch (joint by <mask> tokens) |
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sketch = "<mask> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>" |
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# 3. just do it! |
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generated_text = sega(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text'] |
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print(generated_text) |
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``` |
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```shell |
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'The Conference on Empirical Methods welcomes the submission of research papers. Abstracts should be in the form of a paper or presentation. Please submit abstracts to the following email address: eemml.stanford.edu. The conference will be held at Stanford University on April 1618, 2019. The theme of the conference is Deep Learning.' |
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``` |
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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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### 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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