Instructions to use baikalai/dbert-eth2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baikalai/dbert-eth2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baikalai/dbert-eth2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("baikalai/dbert-eth2") model = AutoModelForSequenceClassification.from_pretrained("baikalai/dbert-eth2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from baikalai/dbert-eth2: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/baikalai/dbert-eth2/resolve/main/training_args.bin
- Command line
-
hf download hf://baikalai/dbert-eth2/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/baikalai/dbert-eth2/resolve/main/training_args.bin
1.46 kB
- Xet hash:
- c8ee7ade288d5dcd0f2731a16667e19629d386673e34dd770a711c2ecef67e6b
- Size of remote file:
- 1.46 kB
- SHA256:
- 4c5d4e96f5fbd1ffde83c16876dc8cf94a1ecc5cd0846318d913ea7cd307dbbf
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