Instructions to use reyhane/bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reyhane/bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="reyhane/bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("reyhane/bert") model = AutoModelForSequenceClassification.from_pretrained("reyhane/bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85fa2935177d06a3f96b0fbbbdb1a26d9dbb994ab0f83601067fc887b695a7f2
- Size of remote file:
- 433 MB
- SHA256:
- b01c45482932044321743bf13aa125d648b2ea7d6a11fa9b98814cf157160dbe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.