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 pytorch_model.bin from RogerKam/BERT-large: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/RogerKam/BERT-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RogerKam/BERT-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RogerKam/BERT-large/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- f9982d15877618cf98418feda1bfb7f0c4ebd3e0356451644a19dd2437469dee
- Size of remote file:
- 268 MB
- SHA256:
- bfac0cb20d1070c40e39a42d515f3aa9015c94461fc32a688b0a08e74f3d4898
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