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 pytorch_model.bin from baikalai/dbert-eth2: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/baikalai/dbert-eth2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://baikalai/dbert-eth2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/baikalai/dbert-eth2/resolve/main/pytorch_model.bin
452 MB
- Xet hash:
- 7e78dec11ff08171491bf327a616f6065bed8e4df6654c6f2b00f753d3b0ec90
- Size of remote file:
- 452 MB
- SHA256:
- c242591e89c08474e062dda23f03919833ed412e608c28d072e9a31d40b8ba7a
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