Instructions to use Chesscorner/bert_ru_de_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chesscorner/bert_ru_de_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chesscorner/bert_ru_de_small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chesscorner/bert_ru_de_small") model = AutoModelForSequenceClassification.from_pretrained("Chesscorner/bert_ru_de_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a83f59808442eab9545a4aff2c7b549940789f1c53355688bb2d566da12ceb6
- Size of remote file:
- 711 MB
- SHA256:
- 375d82e0cec71d823324074089097f2fef14c6043cd18611d574ac0fa5b9d89d
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