Instructions to use ModelTC/roberta-base-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/roberta-base-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/roberta-base-qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/roberta-base-qnli") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/roberta-base-qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d45ee5eb15e40ce7592d1b1e73d27c3ee63df7b14eae9e79b92bcaba4e721d73
- Size of remote file:
- 997 MB
- SHA256:
- 8ba94e1b9f8291157f2f4782ed87b3c088c1477104b0fc7f93faeba6f73df97c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.