# Dialog-KoELECTRA
Github : [https://github.com/skplanetml/Dialog-KoELECTRA](https://github.com/skplanetml/Dialog-KoELECTRA)
## Introduction
**Dialog-KoELECTRA** is a language model specialized for dialogue. It was trained with 22GB colloquial and written style Korean text data. Dialog-ELECTRA model is made based on the [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) model. ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU.
## Released Models
We are initially releasing small version pre-trained model.
The model was trained on Korean text. We hope to release other models, such as base/large models, in the future.
| Model | Layers | Hidden Size | Params | Max Seq Len | Learning Rate | Batch Size | Train Steps |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Dialog-KoELECTRA-Small | 12 | 256 | 14M | 128 | 1e-4 | 512 | 700K |
## Model Performance
Dialog-KoELECTRA shows strong performance in conversational downstream tasks.
| | **NSMC** (acc) | **Question Pair** (acc) | **Korean-Hate-Speech** (F1) | **Naver NER** (F1) | **KorNLI** (acc) | **KorSTS** (spearman) |
| :--------------------- | :----------------: | :--------------------: | :----------------: | :------------------: | :-----------------------: | :-------------------------: |
| DistilKoBERT | 88.60 | 92.48 | 60.72 | 84.65 | 72.00 | 72.59 |
| **Dialog-KoELECTRA-Small** | **90.01** | **94.99** | **68.26** | **85.51** | **78.54** | **78.96** |
## Train Data
## Vocabulary
We applied morpheme analysis using [huggingface_konlpy](https://github.com/lovit/huggingface_konlpy) when creating a vocabulary dictionary.
As a result of the experiment, it showed better performance than a vocabulary dictionary created without applying morpheme analysis.