Instructions to use Namronaldo2004/results_codebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Namronaldo2004/results_codebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Namronaldo2004/results_codebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Namronaldo2004/results_codebert") model = AutoModelForSequenceClassification.from_pretrained("Namronaldo2004/results_codebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d068bcde26dbed273a5c7caca8e79e43fcc071dff72429771132b3b16d57229
- Size of remote file:
- 5.2 kB
- SHA256:
- 53d77ebaa41b26dc4f6b80d4ed2808176ac6b4f01dadf564a2ea059d52d6e093
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.