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library_name: transformers
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# Model Card for Model ID
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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##
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[
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### Compute Infrastructure
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[More Information Needed]
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## Model Card Authors [optional]
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---
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library_name: transformers
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license: cc-by-nc-sa-4.0
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---
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# Model Card for Model ID
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๊ธฐ์กด์ DopeorNope/Ko-Mixtral-v1.3-MoE-7Bx2 ๋ชจ๋ธ์์ ํฅ์๋ 1.4๋ฒ์ ์
๋๋ค.
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์ถ๊ฐ๋ ์ฌํญ์ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.
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1. ํ๋ จ์ ํ์ฉ๋ ์ฝํผ์ค๋ฅผ ๋งค๋ด์ผํ๊ฒ ๊ฒํ ํ๊ณ ์ด์ํ ์ฝํผ์ค๋ฅผ ์์ ํ๊ณ ์ ์ ํ์์ต๋๋ค.
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2. Near dudup ์๊ณ ๋ฆฌ์ฆ์ ์ ์ฉํ์ฌ ์ค๋ณต๋๋ ์ฝํผ์ค๋ฅผ ์ ๊ฑฐํ์์ต๋๋ค.
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3. ๊ธฐ์กด์ 3๊ฐ์ง task์์ ํ๊ฐ์ง task๋ฅผ ์ถ๊ฐํ์์ต๋๋ค.
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## Model Details
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### Model Description
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- **Developed by:** DopeorNope(Seungyoo Lee), kyujinpy(Kyujin Han)
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- **Model type:** Mixtral
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- **Language:** English based model but finetuned with Korean corpus
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- **License:** cc-by-nc-sa-4.0
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- **Finetuned from model:** DopeorNope/Ko-Mixtral-v1.3-MoE-7Bx2
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- **funded by:** the Ministry of Science and ICT(MSIT, Korea) & Gwangju Metropolitan City
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## Training
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#### Testing Data
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AI-HUB์์ ์ ๊ณต๋ ์ฝํผ์ค๋ฅผ ๊ฐ์ง๊ณ ๋ค์๊ณผ ๊ฐ์ 4๊ฐ์ง task๋ฅผ text mining์ผ๋ก ๋ง๋ค์ด ์ ์ฉํ์์ต๋๋ค.
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- **1.Mask prediction Task**
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```python
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#Mask prediction
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#๋ฌธ์ฅ์์ ํ๊ตญ์ด ๋จ์ด๋ฅผ ๋ง์คํน ํ ์ดํ, ์ด ๋จ์ด๋ฅผ ์์ธกํ๊ฒ ๋ง๋๋ Task์
๋๋ค.
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Text='์ง๋ฅ(ๆบ่ฝ) ๋๋ ์ธํ
๋ฆฌ์ ์ค(intelligence)๏ฟฝ๏ฟฝ ์ธ๊ฐ์ <MASK> ๋ฅ๋ ฅ์ ๋งํ๋ค.'
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Response='์ง์ '
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Complete_text='์ง๋ฅ(ๆบ่ฝ) ๋๋ ์ธํ
๋ฆฌ์ ์ค(intelligence)๋ ์ธ๊ฐ์ ์ง์ ๋ฅ๋ ฅ์ ๋งํ๋ค.'
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```
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- **2.Text-allign Task**
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```python
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#Text-allign Task
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#๋ฌธ๋จ์์ ๊ฐ ๋ฌธ์ฅ๋ค์ ์ถ์ถํ๊ณ ์ถ์ถํ ๋ฌธ์ฅ๋ค์ ๋ฌด์์๋ก ์์ ํ ์์ ๋ฌธ์ฅ๋ค์ ๋ฌธ๋งฅ์ ์ ์ ํ๊ฒ ๋ฐฐ์ดํ๋ ํ์คํธ ์
๋๋ค.
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Text_list=['๋ณต์๋ช
๋ น-๋ณต์์๋ฃ(MIMD,Multiple Instruction, Multiple Data)์ ์ ์ฐ์์ ๋ณ๋ ฌํ์ ํ ๊ธฐ๋ฒ์ด๋ค.',
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'๋ถ์ฐ ๋ฉ๋ชจ๋ฆฌ์ ์๋ MPP(massively parallel processors)์ COW (Clusters of Workstations)์ด๋ค.',
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'MIMD๊ธฐ๊ณ๋ ๊ณต์ ๋ฉ๋ชจ๋ฆฌ์ด๊ฑฐ๋ ๋ถ์ฐ ๋ฉ๋ชจ๋ฆฌ์ด๋ฉฐ ์ด๋ฌํ ๋ถ๋ฅ๋ MIMD๊ฐ ์ด๋ป๊ฒ ๋ฉ๋ชจ๋ฆฌ๋ฅผ ์ด์ฉํ๋๋์ ๋ฐ๋ผ ๋๋๋ค.']
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Response='๋ณต์๋ช
๋ น-๋ณต์์๋ฃ(MIMD,Multiple Instruction, Multiple Data)์ ์ ์ฐ์์ ๋ณ๋ ฌํ์ ํ ๊ธฐ๋ฒ์ด๋ค.
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MIMD๊ธฐ๊ณ๋ ๊ณต์ ๋ฉ๋ชจ๋ฆฌ์ด๊ฑฐ๋ ๋ถ์ฐ ๋ฉ๋ชจ๋ฆฌ์ด๋ฉฐ ์ด๋ฌํ ๋ถ๋ฅ๋ MIMD๊ฐ ์ด๋ป๊ฒ ๋ฉ๋ชจ๋ฆฌ๋ฅผ ์ด์ฉํ๋๋์ ๋ฐ๋ผ ๋๋๋ค.
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๋ถ์ฐ ๋ฉ๋ชจ๋ฆฌ์ ์๋ MPP(massively parallel processors)์ COW (Clusters of Workstations)์ด๋ค.'
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```
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- **3.Text completion Task**
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```python
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#Text Completion
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# ๋ฌธ๋จ์์ ๋ง์ง๋ง ๋ฌธ์ฅ์ ์ถ์ถํ๊ณ , ์ถ์ถ๋ ๋ฌธ์ฅ์ ์ด์ ์ ๋ฌธ๋จ๊น์ง๋ฅผ input์ผ๋ก ํ์ฌ ๋ง์ง๋ง ๋ฌธ์ฅ์ ์์ธกํ๊ฒ ํ๋ ๊ณผ์ ์
๋๋ค.
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Text= '๊ทธ๋ฆฐ๋ธ๋ผ์ฐ์ (GreenBrowser)๋ ์ธํฐ๋ท ์ต์คํ๋ก๋ฌ์์ ์ฌ์ฉํ๋ ํธ๋ผ์ด๋ํธ ๋ ์ด์์ ์์ง์ ๋ฐํ์ผ๋ก ํ๋ฉฐ ์ค๊ตญ์ ๊ธฐ๋ฐ์ ๋ ์ํํธ์จ์ด ํ์ฌ์ธ ๋ชจ์ดํต(morequick)์์ ๋ง๋ ๋ฌด๋ฃ ์น ๋ธ๋ผ์ฐ์ ๋ค. ๊ฐ์ฒด์ ์ค๊ตญ์ด๊ฐ ์น ๋ธ๋ผ์ฐ์ ์ ๋ด์ฅ๋์ด ์๋ค.
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๋งฅ์คํค ์น ๋ธ๋ผ์ฐ์ ์ ๋น์ทํ์ฌ MyIE์ ๋ฐ์ ํ๊ฒ ๊ด๋ จ๋์ด ์๋ค. ๋งฅ์คํค์ฉ์ ์ผ๋ถ ํ๋ฌ๊ทธ์ธ์ด ๊ทธ๋ฆฐ๋ธ๋ผ์ฐ์ ์์๋ ์๋ํ ๊ฒ์ด๋ค.'
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Response= '์๋ ์คํฌ๋กค, ์๋ ๋ฆฌํ๋ ์, ์๋ ์ ์ฅ, ์๋ ํผ ์ฑ์ฐ๊ธฐ์ ๊ฐ์ ๋ง์ ์๋ํ ๊ธฐ๋ฅ์ด ์๋ค.'
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```
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- **4. Sentence Genration**
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```python
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#Text Completion
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# ๋ฌธ์ฅ์์ ๋ชจ๋ ๋จ์ด๋ค์ ์ถ์ถํ๊ณ ๋ฌด์์๋ก ์์ ํ ์ค๋ณต๋ ๋จ์ด๋ฅผ ์ ๊ฑฐํ๊ณ , ์ ์๋ ๋จ์ด ๋ฆฌ์คํธ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์๋ฒฝํ ๋ฌธ์ฅ์ ์์ฑํด๋ด๋ task์
๋๋ค.
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Word_List: ['ฯ์', '์ ์ด์์๋', '์ ์ด์', 'ํํ์ด', 'ฯ', '๋ก๋ด', '์ฐ์ธ๋ค', 'ฮธ', '๊ฐ์', '์์ฃผ', '๊ธฐ๊ธฐ']
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response= '์๋ ์คํฌ๋กค, ์๋ ๋ฆฌํ๋ ์, ์๋ ์ ์ฅ, ์๋ ํผ ์ฑ์ฐ๊ธฐ์ ๊ฐ์ ๋ง์ ์๋ํ ๊ธฐ๋ฅ์ด ์๋ค.'
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```
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### Environments
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- **Hardware Type:** Nvidia A100 x 4
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- **Training hours:** 3 Days
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