Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use tmnam20/videberta-base_1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/videberta-base_1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/videberta-base_1024")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/videberta-base_1024") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/videberta-base_1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tmnam20/videberta-base_1024: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/training_args.bin
- Command line
-
hf download hf://tmnam20/videberta-base_1024/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 3d4ce9296958f5a5853b4b335ade3f4acba5a8088d8716286e040537700a03cd
- Size of remote file:
- 4.03 kB
- SHA256:
- cd0372335b8d69875fb40a5865f6914bab9ff539640d01b439e8f707b1e5a43d
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