Instructions to use hf-internal-testing/tiny-random-ModernBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ModernBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-ModernBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ModernBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-ModernBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c61d8931bf1cf1a4a417f81d6852c70457a2ee453108237c914c67bc0740d3d
- Size of remote file:
- 6.62 MB
- SHA256:
- 6f7e37a19582776431c8c0619bcc7b2c563170de146082fe2d474ac42b8a18c2
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