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
Download training_args.bin from SamAgnoli/deberta-v3-base-spatial-language-detection: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/SamAgnoli/deberta-v3-base-spatial-language-detection/resolve/main/training_args.bin
- Command line
-
hf download hf://SamAgnoli/deberta-v3-base-spatial-language-detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SamAgnoli/deberta-v3-base-spatial-language-detection/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 20bef0a40670f17de36240b25805fa52fadfec6ee732b5cbd6222912761f17f2
- Size of remote file:
- 5.27 kB
- SHA256:
- 882d6d50d109e8553ea017de5966c9173f8ee37d4a5cd52b8956406594db47c0
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