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 flax_model.msgpack from anthonymirand/haha_2019_primary_task: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://anthonymirand/haha_2019_primary_task/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/anthonymirand/haha_2019_primary_task/resolve/main/flax_model.msgpack
711 MB
- Xet hash:
- c0f85aa13e4f78a78e6766fe4ab7156b06901dbc277e647971afa4f31242a6c8
- Size of remote file:
- 711 MB
- SHA256:
- f8afee9fc67d38daec125a01849519c7ba7ebeb7a0031976ae9b5b0fb56601a9
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