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:
- 33f54a1f967a63bd9c76c30d8276bba1e15fe12a9359769e8bd08ccaa48767ea
- Size of remote file:
- 6.7 MB
- SHA256:
- c971b7ef138c96bf1fd36713daa3b5167fc2f61b2e898ce1e1be02547bee6299
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.