Instructions to use OliverHeine/bert-base-uncased_fold_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/bert-base-uncased_fold_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/bert-base-uncased_fold_9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/bert-base-uncased_fold_9") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/bert-base-uncased_fold_9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebce4025a8e8fe67f1761f126bcf99546f99ad33e57cad4ca564f6577a5f7ad8
- Size of remote file:
- 5.27 kB
- SHA256:
- aae37faa582edeaff2853aedb2898ed7fed8ab0b90c7e029abcd1631fa6b7d74
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.