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 training_args.bin from anthonymirand/haha_2019_primary_task: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/training_args.bin
- Command line
-
hf download hf://anthonymirand/haha_2019_primary_task/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/training_args.bin
2.35 kB
- Xet hash:
- 40f09b2bda459298eb258ce9a259c3862c07ac88a3d5b3b343e6f792d59bec2d
- Size of remote file:
- 2.35 kB
- SHA256:
- b9606c8cdf0e05e2da782bc8bc877d6d1c9618bbb98116f14893a46e3726e2c8
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