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 pytorch_model.bin from tmnam20/videberta-base_1024: direct link, hf CLI and curl.
- Browser
- Download file 737 MB
-
https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tmnam20/videberta-base_1024/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/pytorch_model.bin
737 MB
- Xet hash:
- 6401f53acb5b3878085711f9f9244fb459e215338f25cf0f3ec33344db76a719
- Size of remote file:
- 737 MB
- SHA256:
- 057eda2c682bf9d9c903eb92f7c38cd743ca463e0c19fef70ca36d331551a8b1
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