Instructions to use jacquesle/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacquesle/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacquesle/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacquesle/output") model = AutoModelForSequenceClassification.from_pretrained("jacquesle/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jacquesle/output: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/jacquesle/output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jacquesle/output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jacquesle/output/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- b85ae349301b985779558a20db1a908415174b27176194c9a20de7093bb4255f
- Size of remote file:
- 433 MB
- SHA256:
- de24f2c589bf0553a520959de1db52dd493f34ac27b1d703b629413ec2f2fea3
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