Instructions to use Hyoungjun-yk/legal-instrument-doro-ner-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hyoungjun-yk/legal-instrument-doro-ner-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Hyoungjun-yk/legal-instrument-doro-ner-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Hyoungjun-yk/legal-instrument-doro-ner-finetuned") model = AutoModelForTokenClassification.from_pretrained("Hyoungjun-yk/legal-instrument-doro-ner-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
legal-instrument-doro-ner-finetuned
legal_instrument_generation ๋ฐํ์์์ ์ฌ์ฉํ๋ ํ๊ตญ์ด ์ฃผ์/์์น ๋น์๋ณํ NER ๋ชจ๋ธ์
๋๋ค.
์ฉ๋
๋ฒ๋ฅ ๋ฌธ์์ ํฌํจ๋ ์ฃผ์์ฑ ํํ์ ํ๊น ํด ๋น์๋ณํ์ ์ฌ์ฉํฉ๋๋ค.
๋ํ label ์์๋ ์๋์ ๊ฐ์ต๋๋ค.
B-LOC_DO,B-LOC_SI,B-LOC_GU,B-LOC_DONGB-ROAD,B-ROAD_NOB-BLD_NAME,B-BLD_NAME_DONG,B-BLD_NAME_HO
ํจํค์ง๋ ์ค์ ๊ธฐ์ค label ์๋ 20๊ฐ์
๋๋ค.
๊ธฐ๋ฐ ๋ชจ๋ธ / ๊ตฌ์กฐ
- architecture:
BertForTokenClassification - hidden size:
768 - max position embeddings:
512 - vocab size:
32000
ํ์ต ํ๋ผ๋ฏธํฐ
- learning rate:
5e-5 - epochs:
10 - train batch size:
8 - eval batch size:
8 - weight decay:
0.01 - scheduler:
linear - gradient accumulation:
1 - logging steps:
20 - seed:
42 - fp16:
False - bf16:
False
๋ฐํ์ ์ฌ์ฉ ์์น
์ด ๋ชจ๋ธ์ ์๋ ๊ฒฝ๋ก๋ก ๋ด๋ ค๋ฐ์ ์ฌ์ฉํฉ๋๋ค.
- local path:
models/anonymization/doro_ner_finetuned
์๋ฒ ์ฝ๋์์๋ legal-ai-server/app/anonymization/service.py์์ ๋ก๋ํฉ๋๋ค.
์ฐธ๊ณ
์ด ๋ฆฌํฌ๋ ๋ฐํ์ ์ฒดํฌํฌ์ธํธ ๋ฐฐํฌ ๋ชฉ์ ์
๋๋ค.
ํ์ต ๋ฐ์ดํฐ์
๊ณผ ์ ์ฒด ์คํ ๋ก๊ทธ๋ ํฌํจํ์ง ์์ต๋๋ค.
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