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-unfun-me-693 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Humor-Research/humor-detection-unfun-me-693 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Humor-Research/humor-detection-unfun-me-693")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Humor-Research/humor-detection-unfun-me-693") model = AutoModelForSequenceClassification.from_pretrained("Humor-Research/humor-detection-unfun-me-693", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2c6690b29b0a1dcc272bc5de0c55aa22bc307c75ffbc566b7287809030e9f2b
- Size of remote file:
- 627 Bytes
- SHA256:
- a1f74a7e5b71d852dac0f6e514e8d07ac7c529ef85e3ab43fed9392885da2d63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.