Instructions to use jonaskoenig/destillbert-statementsaboutfuture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonaskoenig/destillbert-statementsaboutfuture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonaskoenig/destillbert-statementsaboutfuture")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonaskoenig/destillbert-statementsaboutfuture") model = AutoModelForSequenceClassification.from_pretrained("jonaskoenig/destillbert-statementsaboutfuture") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b78f1e2294be89a31851ceab6623e62e1b12c3d0eddff141396817f1eb57fad8
- Size of remote file:
- 268 MB
- SHA256:
- cbab1fb6012193715a85ef55a7cf044377e380bad58c8b957e34ef993feb7e39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.