Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
spatial-language
spatial-reasoning
text-embeddings-inference
Instructions to use SamAgnoli/deberta-v3-base-spatial-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamAgnoli/deberta-v3-base-spatial-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SamAgnoli/deberta-v3-base-spatial-language-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SamAgnoli/deberta-v3-base-spatial-language-detection") model = AutoModelForSequenceClassification.from_pretrained("SamAgnoli/deberta-v3-base-spatial-language-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Retrain on revised dataset (33,284 tokens, 3,516 candidates); kappa .812 candidates, T=1.461
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