Instructions to use OliverHeine/bert-large-uncased_fold_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/bert-large-uncased_fold_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/bert-large-uncased_fold_0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/bert-large-uncased_fold_0") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/bert-large-uncased_fold_0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d30faf57c832ef50d5acf031dac2a498e33884f9b17ee3162a37110500f4274
- Size of remote file:
- 5.84 kB
- SHA256:
- 12921b67682d9fec5b46922da9de77280fe7c7901b9ad059e17e830eb93fb84f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.