Text Classification
Transformers
PyTorch
English
roberta
humor-detection
humor-classification
joke-detection
humor-vs-non-humor
binary-classification
english
nlp
computational-humor
Instructions to use Humor-Research/humor-detection-funlines-and-human-microedit-paper-2023-453 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Humor-Research/humor-detection-funlines-and-human-microedit-paper-2023-453 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Humor-Research/humor-detection-funlines-and-human-microedit-paper-2023-453")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Humor-Research/humor-detection-funlines-and-human-microedit-paper-2023-453") model = AutoModelForSequenceClassification.from_pretrained("Humor-Research/humor-detection-funlines-and-human-microedit-paper-2023-453", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9252d218ec94991d2f42555ccf5225ac65c5de32c5a83516db0bba11abb17303
- Size of remote file:
- 557 Bytes
- SHA256:
- 778bb859a203729d6b260c29c35208e7cad1824d27e9a20498a70b0bccc01710
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