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 checkpoint-1000/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/checkpoint-1000/pytorch_model.bin
- Command line
-
hf download hf://tmnam20/videberta-base_1024/checkpoint-1000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/checkpoint-1000/pytorch_model.bin
737 MB
- Xet hash:
- c5efee46f203cc24c41ba49bd5a7e582cd47c56eba6e4bebc4a5d23c1a3fa055
- Size of remote file:
- 737 MB
- SHA256:
- e6baa0ed3c0013d58345b0e3c81e7ffae409f63038ae73b3a5056a229c83bff9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.