Instructions to use emeraldgoose/bert-base-v1-sports with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emeraldgoose/bert-base-v1-sports with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="emeraldgoose/bert-base-v1-sports")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("emeraldgoose/bert-base-v1-sports") model = AutoModelForMaskedLM.from_pretrained("emeraldgoose/bert-base-v1-sports", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Data-annotation-nlp-10 (BoostCamp AI)
위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행
How to use
from transformers import AutoTokenizer, BertForMaskedLM
model = BertForMaskedLM.from_pretrained("emeraldgoose/bert-base-v1-sports")
tokenizer = AutoTokenizer.from_pretrained("emeraldgoose/bert-base-v1-sports")
text = "산악 자전거 경기는 상대적으로 새로운 [MASK] 1990년대에 활성화 되었다."
inputs = tokenizer.encode(text, return_tensors='pt')
model.eval()
outputs = model(inputs)['logits']
predict = outputs.argmax(-1)[0]
print(tokenizer.decode(predict))
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