Instructions to use j-hartmann/ambiguity-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use j-hartmann/ambiguity-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="j-hartmann/ambiguity-distilroberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("j-hartmann/ambiguity-distilroberta-base") model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/ambiguity-distilroberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b20fab7e4793fc1b6277045a09f108bbca6762a3180427f27bb819604a0eecdc
- Size of remote file:
- 3.25 kB
- SHA256:
- a94cc81433c2f4029d4528531e7ce5d8f138caade12bb3b70766f1d28010f0c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.