Instructions to use modularStarEncoder/ModularStarEncoder-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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| [Layer 27](https://huggingface.co/modularStarEncoder/ModularStarEncoder-finetuned-27)* | 80.3 |
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| [Layer 36](https://huggingface.co/modularStarEncoder/ModularStarEncoder-finetuned)* | 79.6 |
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## Licence
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The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
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| [Layer 27](https://huggingface.co/modularStarEncoder/ModularStarEncoder-finetuned-27)* | 80.3 |
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| [Layer 36](https://huggingface.co/modularStarEncoder/ModularStarEncoder-finetuned)* | 79.6 |
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## Licence
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The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
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