Instructions to use kneis/distilbert-sentiment-adversarial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kneis/distilbert-sentiment-adversarial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kneis/distilbert-sentiment-adversarial")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kneis/distilbert-sentiment-adversarial") model = AutoModelForSequenceClassification.from_pretrained("kneis/distilbert-sentiment-adversarial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4c8cddc5e1dbb2b0a9517b35b1828d25a4dbd97aac1f505ad975729dda98895
- Size of remote file:
- 268 MB
- SHA256:
- 6a51c6f88636988fb8bf911615c939a2c9fde493be439a63caa0b7f078642a55
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