Instructions to use Gerwin/bert-for-pac with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gerwin/bert-for-pac with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gerwin/bert-for-pac")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gerwin/bert-for-pac") model = AutoModelForSequenceClassification.from_pretrained("Gerwin/bert-for-pac", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Gerwin/bert-for-pac: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Gerwin/bert-for-pac/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Gerwin/bert-for-pac/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Gerwin/bert-for-pac/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 926d0f88b383537c17234a9e34a357ec13afd2e4a492f37dbe0e7ee35b16bed0
- Size of remote file:
- 437 MB
- SHA256:
- 745bbee3491656f334ae3ea098b6f3765519809c39bd54d07766d461d918f9a7
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