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kogpt2-base-v2

Text Generation
Transformers
PyTorch
JAX
Korean
gpt2
text-generation-inference
Inference Endpoints
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kogpt2-base-v2 / README.md
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metadata
language: ko
license: cc-by-nc-sa-4.0
tags:
  - gpt2

Model Card for kogpt2-base-v2

Model Details

Model Description

GPT-2λŠ” 주어진 ν…μŠ€νŠΈμ˜ λ‹€μŒ 단어λ₯Ό 잘 μ˜ˆμΈ‘ν•  수 μžˆλ„λ‘ ν•™μŠ΅λœ μ–Έμ–΄λͺ¨λΈμ΄λ©° λ¬Έμž₯ 생성에 μ΅œμ ν™” λ˜μ–΄ μžˆμŠ΅λ‹ˆλ‹€. KoGPT2λŠ” λΆ€μ‘±ν•œ ν•œκ΅­μ–΄ μ„±λŠ₯을 κ·Ήλ³΅ν•˜κΈ° μœ„ν•΄ 40GB μ΄μƒμ˜ ν…μŠ€νŠΈλ‘œ ν•™μŠ΅λœ ν•œκ΅­μ–΄ 디코더(decoder) μ–Έμ–΄λͺ¨λΈμž…λ‹ˆλ‹€.

  • Developed by: SK Telecom
  • Shared by [Optional]: SK Telecom
  • Model type: Text Generation
  • Language(s) (NLP): Korean
  • License: cc-by-nc-sa-4.0
  • Parent Model: GPT-2
  • Resources for more information:

Uses

Direct Use

This model can be used for the task of Text Generation

Downstream Use [Optional]

More information needed.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

The model authors also note in the GitHub Repo:

tokenizers νŒ¨ν‚€μ§€μ˜ Character BPE tokenizer둜 ν•™μŠ΅λ˜μ—ˆμŠ΅λ‹ˆλ‹€.

사전 ν¬κΈ°λŠ” 51,200 이며 λŒ€ν™”μ— 자주 μ“°μ΄λŠ” μ•„λž˜μ™€ 같은 이λͺ¨ν‹°μ½˜, 이λͺ¨μ§€ 등을 μΆ”κ°€ν•˜μ—¬ ν•΄λ‹Ή ν† ν°μ˜ 인식 λŠ₯λ ₯을 μ˜¬λ ΈμŠ΅λ‹ˆλ‹€.

πŸ˜€, 😁, πŸ˜†, πŸ˜…, 🀣, .. , :-), :), -), (-:...

ν•œκ΅­μ–΄ μœ„ν‚€ λ°±κ³Ό 이외, λ‰΄μŠ€, λͺ¨λ‘μ˜ λ§λ­‰μΉ˜ v1.0, μ²­μ™€λŒ€ ꡭ민청원 λ“±μ˜ λ‹€μ–‘ν•œ 데이터가 λͺ¨λΈ ν•™μŠ΅μ— μ‚¬μš©λ˜μ—ˆμŠ΅λ‹ˆλ‹€.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

Model # of params Type # of layers # of heads ffn_dim hidden_dims
kogpt2-base-v2 125M Decoder 12 12 3072 768

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

Classification or Regression

NSMC(acc) KorSTS(spearman)
KoGPT2 2.0 89.1 77.8

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: More information needed
  • Hours used: More information needed
  • Cloud Provider: More information needed
  • Compute Region: More information needed
  • Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed.

Citation

BibTeX:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

SK Telecom in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

The model authors also note in the GitHub Repo

KoGPT2 κ΄€λ ¨ μ΄μŠˆλŠ” 이곳에 μ˜¬λ €μ£Όμ„Έμš”.

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand 
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skt/kogpt2-base-v2")

model = AutoModelForCausalLM.from_pretrained("skt/kogpt2-base-v2")