Instructions to use mtreviso/roberta-base-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtreviso/roberta-base-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtreviso/roberta-base-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtreviso/roberta-base-snli") model = AutoModelForSequenceClassification.from_pretrained("mtreviso/roberta-base-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 874c70dfa24014c53403be080f81b69fc1976f65353c69a7ebfc94850649745b
- Size of remote file:
- 2.42 kB
- SHA256:
- e25aca90058b828e8681314d06a4c93dc8871b4ac24c52382b111dd5c3b8b040
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.