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
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README.md
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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**MatText** model pretrained using Masked Language Modelling on crystal structures mined from NOMAD and represented using MatText -
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- **Developed by:** [Lamalab](https://github.com/lamalab-org)
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```python
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from transformers import AutoModel
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model = AutoModel.from_pretrained("n0w0f/MatText-
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```
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## Training Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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**MatText** model pretrained using Masked Language Modelling on crystal structures mined from NOMAD and represented using MatText - Atom Sequences plusplus represntation (A space-separated enumeration of element symbols and the lattice parameters).
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- **Developed by:** [Lamalab](https://github.com/lamalab-org)
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```python
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from transformers import AutoModel
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model = AutoModel.from_pretrained("n0w0f/MatText-seq-plusplus-2m")
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```
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## Training Details
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