Instructions to use test992/colfixture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use test992/colfixture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="test992/colfixture")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("test992/colfixture", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from test992/colfixture: direct link, hf CLI and curl.
- Browser
- Download file 316 Bytes
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https://huggingface.co/test992/colfixture/resolve/main/README.md
- Command line
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hf download hf://test992/colfixture/README.md
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curl -L -o README.md https://huggingface.co/test992/colfixture/resolve/main/README.md
316 Bytes
| library_name: transformers | |
| pipeline_tag: text-classification | |
| widget: | |
| - text: "I love this movie" | |
| example_title: "Example 1" | |
| output: | |
| - label: "<img src=x onerror=window.WX1=1>" | |
| score: 0.91 | |
| - text: "I hate this movie" | |
| example_title: "Example 2" | |
| output: | |
| - label: "NEGATIVE" | |
| score: 0.88 | |
| # wx | |