Update model card with metadata and links
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by
nielsr
HF Staff
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README.md
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license: mit
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datasets:
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- HuggingFaceTB/smollm-corpus
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language:
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- en
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---
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# Raw 1B Shared
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Citation
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```
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@article{yamaguchi2026enhancinglinguisticcompetencelanguage,
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title={Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks},
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author={Atsuki Yamaguchi and Maggie Mi and Nikolaos Aletras},
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journal={arXiv},
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volume={abs/2601.03448}
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}
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```
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---
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datasets:
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- HuggingFaceTB/smollm-corpus
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language:
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- en
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license: mit
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Raw 1B Shared
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This model is a 1B parameter language model pre-trained as a baseline for the research presented in the paper [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://huggingface.co/papers/2601.03448).
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L2T (Language Learning Tasks) is a pre-training framework that integrates structured linguistic tasks alongside standard next-token prediction to explicitly optimize for linguistic competence in Large Language Models (LLMs). This specific checkpoint is the baseline model trained on raw text.
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- **Paper:** [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://huggingface.co/papers/2601.03448)
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- **Repository:** [gucci-j/l2t](https://github.com/gucci-j/l2t)
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Citation
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```bibtex
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@article{yamaguchi2026enhancinglinguisticcompetencelanguage,
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title={Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks},
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author={Atsuki Yamaguchi and Maggie Mi and Nikolaos Aletras},
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journal={arXiv},
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volume={abs/2601.03448}
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}
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```
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