Instructions to use dtorber/bert-base-cased_K2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/bert-base-cased_K2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtorber/bert-base-cased_K2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtorber/bert-base-cased_K2") model = AutoModelForSequenceClassification.from_pretrained("dtorber/bert-base-cased_K2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dtorber/bert-base-cased_K2: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/dtorber/bert-base-cased_K2/resolve/main/training_args.bin
- Command line
-
hf download hf://dtorber/bert-base-cased_K2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dtorber/bert-base-cased_K2/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- aeb382314ccfdd2324b070cbdbe72772000fa5cf89aa37a0064271729d7eaf49
- Size of remote file:
- 4.79 kB
- SHA256:
- fd8a28bff339fa9d018cceebc76896fee5a22477ddec260cb4cb82559f0f3d9b
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