Instructions to use owen198/esgbert1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use owen198/esgbert1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="owen198/esgbert1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("owen198/esgbert1") model = AutoModelForSequenceClassification.from_pretrained("owen198/esgbert1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from owen198/esgbert1: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/owen198/esgbert1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://owen198/esgbert1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/owen198/esgbert1/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 0e4f332b3d9848bde0395cd322873a04326f00051e12a546af5eddf55456c1b3
- Size of remote file:
- 433 MB
- SHA256:
- ef6b8bcb5b148df6fd218aaa1fa5584e801809124ac0a8830d12422a5c4e0859
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