Instructions to use joelb/custom-handler-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelb/custom-handler-tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joelb/custom-handler-tutorial")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joelb/custom-handler-tutorial") model = AutoModelForSequenceClassification.from_pretrained("joelb/custom-handler-tutorial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from joelb/custom-handler-tutorial: direct link, hf CLI and curl.
- Browser
- Download file 285 Bytes
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https://huggingface.co/joelb/custom-handler-tutorial/resolve/main/README.md
- Command line
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hf download hf://joelb/custom-handler-tutorial/README.md
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curl -L -o README.md https://huggingface.co/joelb/custom-handler-tutorial/resolve/main/README.md
285 Bytes
metadata
language:
- en
tags:
- text-classification
- emotion
- endpoints-template
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score