Instructions to use modularStarEncoder/ModularStarEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -81,4 +81,18 @@ The pre-training and fine-tuning were conducted on 512 NVIDIA Ampere (64GB) GPUs
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|Multi-layer loss | yes |
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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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|Multi-layer loss | yes |
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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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# Citation
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```
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@article{gurioli2025modeltrainallhierarchical,
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title={One Model to Train them All: Hierarchical Self-Distillation for Enhanced Early Layer Embeddings},
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author={Andrea Gurioli and Federico Pennino and João Monteiro and Maurizio Gabbrielli},
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year={2025},
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eprint={2503.03008},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2503.03008},
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}
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```
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