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
| language: | |
| - en | |
| tags: | |
| - text-classification | |
| - emotion | |
| - endpoints-template | |
| license: apache-2.0 | |
| datasets: | |
| - emotion | |
| metrics: | |
| - Accuracy, F1 Score | |
| # Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) |