# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Turka/dummy-model8")
model = AutoModelForMaskedLM.from_pretrained("Turka/dummy-model8")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-descriptionmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Turka/dummy-model8")