Instructions to use asparius/convbert-combined-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asparius/convbert-combined-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asparius/convbert-combined-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asparius/convbert-combined-large") model = AutoModelForSequenceClassification.from_pretrained("asparius/convbert-combined-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad0a84b309aa09b567d1a4361610867266dcdb1270df4844ea8240c5102a1043
- Size of remote file:
- 3.38 kB
- SHA256:
- 1f959cc2b9ddd2daa631f1e46b10476702874c5bb82ceaaa2ba6aed4c4f01deb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.