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
File size: 255 Bytes
a373a60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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)
|