Instructions to use RogerKam/BERT2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerKam/BERT2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerKam/BERT2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerKam/BERT2-base") model = AutoModelForSequenceClassification.from_pretrained("RogerKam/BERT2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RogerKam/BERT2-base: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/RogerKam/BERT2-base/resolve/main/training_args.bin
- Command line
-
hf download hf://RogerKam/BERT2-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RogerKam/BERT2-base/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 6c0921628f26d86ed0aba5974247b1c9534fb7c496d98cddade99bc0dfa352c2
- Size of remote file:
- 4.6 kB
- SHA256:
- 1c6490ab72783af7a67ddd39f4e80275865620c1eec815e1e554601c5fe1ec51
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.