Instructions to use dtorber/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtorber/bert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtorber/bert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("dtorber/bert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dtorber/bert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/dtorber/bert-base-cased/resolve/main/training_args.bin
- Command line
-
hf download hf://dtorber/bert-base-cased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dtorber/bert-base-cased/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 640fddc61af033b2c4ac999eeab68f0e5eed5caa22bcfafa2f3c0509620205fe
- Size of remote file:
- 5.11 kB
- SHA256:
- f3a427e58d86f0dfca403b8841b461deb6177fc29e874c66654c576085c23201
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.