Instructions to use aekupor/eliciting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aekupor/eliciting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aekupor/eliciting")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aekupor/eliciting") model = AutoModelForSequenceClassification.from_pretrained("aekupor/eliciting", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_run_handler.py from aekupor/eliciting: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
-
https://huggingface.co/aekupor/eliciting/resolve/refs%2Fpr%2F1/test_run_handler.py
- Command line
-
hf download hf://aekupor/eliciting@refs/pr/1/test_run_handler.py
-
curl -L -o test_run_handler.py https://huggingface.co/aekupor/eliciting/resolve/refs%2Fpr%2F1/test_run_handler.py
255 Bytes
| 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) | |