# scibert-wechsel-korean

Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.

### Description
- SciBERT is trained on papers from the corpus of semanticscholar.org. Corpus size is 1.14M papers, 3.1B tokens. 
- Wechsel is converting embedding layer's subword tokens from source language to target language. 
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance.

### Reference
- [Scibert](https://github.com/allenai/scibert)
- [WECHSEL](https://github.com/CPJKU/wechsel)
- [Korean Language Understanding Evaluation](https://github.com/KLUE-benchmark/KLUE)
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transformers pytorch bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# scibert-wechsel-korean

Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.

### Description
- SciBERT is trained on papers from the corpus of URL. Corpus size is 1.14M papers, 3.1B tokens. 
- Wechsel is converting embedding layer's subword tokens from source language to target language. 
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance.

### Reference
- Scibert
- WECHSEL
- Korean Language Understanding Evaluation
# scibert-wechsel-korean

Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique. ### Description
- SciBERT is trained on papers from the corpus of URL. Corpus size is 1.14M papers, 3.1B tokens. 
- Wechsel is converting embedding layer's subword tokens from source language to target language. 
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance. ### Reference
- Scibert
- WECHSEL
- Korean Language Understanding Evaluation
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # scibert-wechsel-korean

Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique. ### Description
- SciBERT is trained on papers from the corpus of URL. Corpus size is 1.14M papers, 3.1B tokens. 
- Wechsel is converting embedding layer's subword tokens from source language to target language. 
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance. ### Reference
- Scibert
- WECHSEL
- Korean Language Understanding Evaluation
passage: TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
# scibert-wechsel-korean

Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.### Description
- SciBERT is trained on papers from the corpus of URL. Corpus size is 1.14M papers, 3.1B tokens. 
- Wechsel is converting embedding layer's subword tokens from source language to target language. 
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance.### Reference
- Scibert
- WECHSEL
- Korean Language Understanding Evaluation