Instructions to use mrfriedpotato/effect_t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrfriedpotato/effect_t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrfriedpotato/effect_t")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrfriedpotato/effect_t") model = AutoModelForSequenceClassification.from_pretrained("mrfriedpotato/effect_t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mrfriedpotato/effect_t: direct link, hf CLI and curl.
- Browser
- Download file 291 Bytes
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https://huggingface.co/mrfriedpotato/effect_t/resolve/main/README.md
- Command line
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hf download hf://mrfriedpotato/effect_t/README.md
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curl -L -o README.md https://huggingface.co/mrfriedpotato/effect_t/resolve/main/README.md
291 Bytes
| tags: | |
| - text classification | |
| widget: | |
| - text: "How to clean up the house. Take out the trash." | |
| example_title: "Example 1" | |
| - text: "How to make a Pizza. Cut the tomato." | |
| example_title: "Example 2" | |
| # Temporal Action Prediction | |
| Prediction of action effect time from simple sentences. | |