Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use wesleymorris/language-beyond-the-source with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/language-beyond-the-source with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/language-beyond-the-source")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/language-beyond-the-source") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/language-beyond-the-source") - Notebooks
- Google Colab
- Kaggle
Wesley Morris commited on
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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## Contact
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This model was developed by LEAR Lab at Vanderbilt University.
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For questions or comments about this model, please contact [wesley.g.morris@vanderbilt.edu](wesley.g.morris@vanderbilt.edu).
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