Instructions to use faaany/bert-base-uncased-float32-traced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faaany/bert-base-uncased-float32-traced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="faaany/bert-base-uncased-float32-traced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("faaany/bert-base-uncased-float32-traced") model = AutoModelForMaskedLM.from_pretrained("faaany/bert-base-uncased-float32-traced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("faaany/bert-base-uncased-float32-traced")
model = AutoModelForMaskedLM.from_pretrained("faaany/bert-base-uncased-float32-traced", device_map="auto")Quick Links
No model card
- Downloads last month
- 5
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="faaany/bert-base-uncased-float32-traced")