Instructions to use egerber1/classifier-de2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egerber1/classifier-de2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="egerber1/classifier-de2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("egerber1/classifier-de2") model = AutoModelForSequenceClassification.from_pretrained("egerber1/classifier-de2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from egerber1/classifier-de2: direct link, hf CLI and curl.
- Browser
- Download file 5.65 kB
-
https://huggingface.co/egerber1/classifier-de2/resolve/main/training_args.bin
- Command line
-
hf download hf://egerber1/classifier-de2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/egerber1/classifier-de2/resolve/main/training_args.bin
5.65 kB
- Xet hash:
- cd64bb7dd507847793602628ad3d489f8034d81a4143ba5a3d0a399ca8b5d6e8
- Size of remote file:
- 5.65 kB
- SHA256:
- 9604bbb1bef454773450efc3c7ca64fd12f387ef5baffccc8394aacfc56c142b
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