Instructions to use RogerKam/BERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerKam/BERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerKam/BERT-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerKam/BERT-large") model = AutoModelForSequenceClassification.from_pretrained("RogerKam/BERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RogerKam/BERT-large: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/RogerKam/BERT-large/resolve/main/training_args.bin
- Command line
-
hf download hf://RogerKam/BERT-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RogerKam/BERT-large/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- c7b20b39240bf7b5294babd2baa3ab0fde8417c3a33dabc3b3b27691dc37500e
- Size of remote file:
- 4.6 kB
- SHA256:
- ace866dbd8c7687079a456f43c886604d38e6aca8b03d55b58211418bf9e5f31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.