Instructions to use schnell/bert-small-ipadic_bpe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schnell/bert-small-ipadic_bpe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="schnell/bert-small-ipadic_bpe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("schnell/bert-small-ipadic_bpe") model = AutoModelForMaskedLM.from_pretrained("schnell/bert-small-ipadic_bpe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
update model card README.md
8ae0892 -
Model save
a77be22 -
Training in progress, epoch 14
8004c1a -
Training in progress, epoch 13
acdfbd0 -
Training in progress, epoch 12
a9e5457
-
Training in progress, epoch 11
c2f0eee -
Training in progress, epoch 10
cab3b53 -
Training in progress, epoch 9
52e08b2 -
Training in progress, epoch 8
9bdfdf0 -
Training in progress, epoch 7
6f0d1ff
-
Training in progress, epoch 6
82f2939 -
Training in progress, epoch 5
931bd73 -
Training in progress, epoch 4
5411ce9 -
Training in progress, epoch 3
eee2db4 -
Training in progress, epoch 2
ee97872
-
Training in progress, epoch 1
7f3ae11 -
initial commit
16f32e2