Instructions to use hotchpotch/luke-japanese-base-lite-xlm-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hotchpotch/luke-japanese-base-lite-xlm-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hotchpotch/luke-japanese-base-lite-xlm-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hotchpotch/luke-japanese-base-lite-xlm-roberta") model = AutoModelForMaskedLM.from_pretrained("hotchpotch/luke-japanese-base-lite-xlm-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
luke-japanese-base-lite-xlm-roberta
studio-ousia/luke-japanese-base-liteの重みの名前をXLMRoberta形式に置き換え、XLMRobertaモデルとして扱えるようにした物です。
from transformers import XLMRobertaForMaskedLM, XLMRobertaTokenizer
model_name = "hotchpotch/luke-japanese-base-lite-xlm-roberta"
model = XLMRobertaForMaskedLM.from_pretrained(model_name)
tokenizer = XLMRobertaTokenizer.from_pretrained(model_name)
- Downloads last month
- 52
Model tree for hotchpotch/luke-japanese-base-lite-xlm-roberta
Base model
studio-ousia/luke-japanese-base-lite