Instructions to use paragon-analytics/debert_empathy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paragon-analytics/debert_empathy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paragon-analytics/debert_empathy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/debert_empathy") model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/debert_empathy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from paragon-analytics/debert_empathy: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/paragon-analytics/debert_empathy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://paragon-analytics/debert_empathy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/paragon-analytics/debert_empathy/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- c46b97bbeec1dc9c9eecf0a3a64bace744a4166c4cc92a79bfea08a4c748ea49
- Size of remote file:
- 557 MB
- SHA256:
- 1cdc800af87fe98ae6b55ca3aa1ffb49e988ba41a0bbb27ed7e6034a887ce31f
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