Instructions to use paragon-analytics/deberta_empathy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paragon-analytics/deberta_empathy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paragon-analytics/deberta_empathy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/deberta_empathy") model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/deberta_empathy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9725948417d77bdef789d4d29001504058d92b870a517435843777a0b831d0b9
- Size of remote file:
- 557 MB
- SHA256:
- 5609bac260450a78c2cd808bc84f8afb4c7ccc7247fc9ab8a7ce86f95fd2fe84
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