Instructions to use skywalker290/Bert-Stack-Exchange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skywalker290/Bert-Stack-Exchange with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="skywalker290/Bert-Stack-Exchange")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("skywalker290/Bert-Stack-Exchange") model = AutoModelForSequenceClassification.from_pretrained("skywalker290/Bert-Stack-Exchange", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eaf29bc95d8167899b04a7e99dd416b97b6e7e917f5fcdc43528c27e774e472
- Size of remote file:
- 478 MB
- SHA256:
- c107a9a7d4d2de8d7c6b80ccffccf2f006bc27c89a6bf2879f954da3935bfbb1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.