Instructions to use Anwaarma/BP-server02andinteger03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anwaarma/BP-server02andinteger03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Anwaarma/BP-server02andinteger03")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Anwaarma/BP-server02andinteger03") model = AutoModelForSequenceClassification.from_pretrained("Anwaarma/BP-server02andinteger03", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c36ad7e473138924918b8d40d42859648b22452293fb2f4d674ac3d596470565
- Size of remote file:
- 4.6 kB
- SHA256:
- 60dcdb0494204dc455db5bccf95bd43d6125daf5c41c673f1c7e30ef89d9b2a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.