Instructions to use aekupor/adding_on with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aekupor/adding_on with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aekupor/adding_on")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aekupor/adding_on") model = AutoModelForSequenceClassification.from_pretrained("aekupor/adding_on") - Notebooks
- Google Colab
- Kaggle
| from handler import EndpointHandler | |
| # init handler | |
| my_handler = EndpointHandler(path=".") | |
| # prepare sample payload | |
| test_payload = 'test.transcript.vtt' | |
| # test the handler | |
| test_pred=my_handler(test_payload) | |
| # show results | |
| print("test_pred", test_pred) | |