Instructions to use anthonymirand/haha_2019_primary_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anthonymirand/haha_2019_primary_task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anthonymirand/haha_2019_primary_task")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anthonymirand/haha_2019_primary_task") model = AutoModelForSequenceClassification.from_pretrained("anthonymirand/haha_2019_primary_task", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anthonymirand/haha_2019_primary_task: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anthonymirand/haha_2019_primary_task/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 33c8ed2bfb8de92a6e71074d822d5570fce59a35b9845ca684262fd15e63b0e6
- Size of remote file:
- 712 MB
- SHA256:
- 2398a7d6ba732239b2bbb0f10d0ff5e5523417b7eec3ce193fa2e43ca2958210
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