Instructions to use anthonymirand/haha_2019_adaptation_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anthonymirand/haha_2019_adaptation_task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anthonymirand/haha_2019_adaptation_task")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anthonymirand/haha_2019_adaptation_task") model = AutoModelForSequenceClassification.from_pretrained("anthonymirand/haha_2019_adaptation_task", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anthonymirand/haha_2019_adaptation_task: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/anthonymirand/haha_2019_adaptation_task/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anthonymirand/haha_2019_adaptation_task/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anthonymirand/haha_2019_adaptation_task/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- b88235b0f39b8e3254687cde4aac5607b19f969a37870fff618e43ff793cb44b
- Size of remote file:
- 712 MB
- SHA256:
- deb8592003db164afea546729823a4e0b326b988114a22c53525b1b439021ac4
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