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")# 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
- Xet hash:
- e7a9d271dca0dd2b1a0f305045b7c943a39085d92fbf47b67acc35a50e9d4151
- Size of remote file:
- 5.2 kB
- SHA256:
- ab9e7d480ba0332fd953ebcb5573a99851086b5290dc78a9c05cb03467f9df35
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