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--- |
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language: en |
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tags: |
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- SEGA |
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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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Output: |
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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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## Data Augmentation for Text Classification Tasks: |
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- Setting: Low-resource setting, where only n={50,100,200,500,1000} labeled samples are available for training. The below results are the average of all training sizes. |
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- Datasets: [HuffPost](https://huggingface.co/datasets/khalidalt/HuffPost), [BBC](https://huggingface.co/datasets/SetFit/bbc-news), [SST2](https://huggingface.co/datasets/glue), [IMDB](https://huggingface.co/datasets/imdb), [Yahoo](https://huggingface.co/datasets/yahoo_answers_topics), [20NG](https://huggingface.co/datasets/newsgroup). |
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- Base classifier: [DistilBERT](https://huggingface.co/distilbert-base-cased) |
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| Method | HuffPost | BBC | SST2 | IMDB | Yahoo | 20NG | avg. | |
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|---------|:------------------:|:------------------:|:----------------------:|:----------------------:|:----------:|:----------:|:----------:| |
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| | ID / OOD (BBC) | ID / OOD (Huff) | ID / OOD (IMDB) | ID / OOD (SST2) | | | | |
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| none | 79.17 / 62.32 | **96.16** / 62.00 | 76.67 / 73.16 | 77.87 / 74.43 | 45.77 | 46.67 | 69.42 | |
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| EDA | 79.63 / 67.48 | 95.11 / 58.92 | 75.52 / 69.46 | 77.88 / 75.88 | 45.10 | 46.15 | 69.11 | |
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| STA | 80.74 / 69.31 | 95.64 / 64.82 | 77.80 / 73.66 | 77.88 / 74.77 | 46.96 | 47.27 | 70.88 | |
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| Back | 80.48 / 67.75 | 95.28 / 63.10 | 76.96 / 72.23 | 78.35 / 75.96 | 46.10 | 46.61 | 70.28 | |
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| MLM | 80.04 / 66.80 | 96.07 / 65.39 | 76.61/ 73.11 | 75.73 / 73.70 | 45.35 | 46.53 | 69.93 | |
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| C-MLM | 79.96 / 65.10 | 96.13 / **67.80** | 76.91 / 71.83 | 77.31 / 75.02 | 45.29 | 46.36 | 70.17 | |
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| LAMBADA | 81.03 / 68.89 | 93.75 / 52.79 | 77.87 / 74.54 | 77.49 / 74.33 | 50.66 | 47.72 | 69.91 | |
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| **SEGA (Ours)** | 81.43 / 74.87 | 95.61 / 67.79 | 77.87 / 72.94 | **79.51** / **76.75** | 49.43 | 50.47 | 72.67 | |
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| **SEGA-f (Ours)** | **81.82** / **76.18** | 95.78 / 67.79 | **80.59** / **80.32** | 79.37 / 76.61 | **50.12** | **50.81** | **73.94** | |
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### BibTeX entry and citation info |
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