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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d52a696a6553263e266d52aabf3f7473f6d719ecd4858e666801d8ec17acfc6
- Size of remote file:
- 329 MB
- SHA256:
- e41fe231157e3658a4a15a783c9756ba17be1d3e3b7c355d300cfcd79bf4972e
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