Instructions to use joonhan/roberta-roa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joonhan/roberta-roa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="joonhan/roberta-roa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("joonhan/roberta-roa") model = AutoModelForTokenClassification.from_pretrained("joonhan/roberta-roa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ajskdlf64 commited on
Commit ·
46b4e23
1
Parent(s): bc8dd0d
add tokenizer
Browse files- added_tokens.json +1 -1
- tokenizer.json +0 -0
added_tokens.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"
|
|
|
|
| 1 |
+
{"블루런벤처스": 32021, "끌림벤처스": 32018, "파미오": 32009, "아이도트": 32012, "GE헬스케어": 32030, "마그나": 32019, "신한금융지주": 32020, "우드매킨지": 32008, "핀다": 32027, "가우리안": 32013, "우드맥킨지": 32007, "맥스트": 32015, "이그잭스": 32002, "야놀자": 32017, "큐라클": 32010, "무신사": 32011, "삼성정밀화학": 32014, "삼성바이오로직스": 32005, "한미글로벌": 32016, "웅진북센": 32006, "카뱅": 32022, "휴온스바이오파마": 32028, "바이오젠": 32000, "픽사": 32025, "비보": 32026, "쏘카": 32029, "휴마시스": 32003, "제주맥주": 32001, "모더나": 32004, "D램익스체인지": 32024, "보령컨슈머헬스케어": 32023}
|
tokenizer.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|