Instructions to use n0w0f/MatText-atom-seq-plusplus-2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n0w0f/MatText-atom-seq-plusplus-2m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="n0w0f/MatText-atom-seq-plusplus-2m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("n0w0f/MatText-atom-seq-plusplus-2m") model = AutoModel.from_pretrained("n0w0f/MatText-atom-seq-plusplus-2m") - Notebooks
- Google Colab
- Kaggle
add citation block
Browse files
README.md
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Model Card Authors
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## Citation
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If you use MatText in your work, please cite
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```
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@misc{alampara2024mattextlanguagemodelsneed,
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title={MatText: Do Language Models Need More than Text & Scale for Materials Modeling?},
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author={Nawaf Alampara and Santiago Miret and Kevin Maik Jablonka},
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year={2024},
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eprint={2406.17295},
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archivePrefix={arXiv},
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primaryClass={cond-mat.mtrl-sci}
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url={https://arxiv.org/abs/2406.17295},
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}
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```
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## Model Card Authors
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