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-semeval-2017-task-7-23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Humor-Research/humor-detection-semeval-2017-task-7-23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Humor-Research/humor-detection-semeval-2017-task-7-23")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Humor-Research/humor-detection-semeval-2017-task-7-23") model = AutoModelForSequenceClassification.from_pretrained("Humor-Research/humor-detection-semeval-2017-task-7-23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed25607debac6df3c8a5fbf63fe877015a4954b3c33c19969c023062c2d11c81
- Size of remote file:
- 499 MB
- SHA256:
- ed3b3dc1aa29ebdea4144b5ef24d92c88fa5fde99c5d1a97ef3e1682c849f075
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