Instructions to use csatapathy/interview-ratings-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csatapathy/interview-ratings-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="csatapathy/interview-ratings-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("csatapathy/interview-ratings-bert") model = AutoModelForSequenceClassification.from_pretrained("csatapathy/interview-ratings-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54f89c418fff8cef209ff1b594e17e8ee0dd7566ccdf0698ac9f985316635188
- Size of remote file:
- 438 MB
- SHA256:
- 44281127d4631666510709681f54b52332d66eda6c7da44b4e368829664e6452
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.