Instructions to use RogerKam/BERT-large2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerKam/BERT-large2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerKam/BERT-large2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerKam/BERT-large2") model = AutoModelForSequenceClassification.from_pretrained("RogerKam/BERT-large2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RogerKam/BERT-large2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/RogerKam/BERT-large2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RogerKam/BERT-large2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RogerKam/BERT-large2/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 33f44bad4a3ca80a6da6de6e233991a2507f6b13d041fda9995a89703fa9ab41
- Size of remote file:
- 499 MB
- SHA256:
- bc2ebba963cc9314518d529a28c8a0096dc2a9ffcf9d14608385fb1793b3de5e
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