Instructions to use OliverHeine/albert-base-v2_fold_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/albert-base-v2_fold_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/albert-base-v2_fold_5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/albert-base-v2_fold_5") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/albert-base-v2_fold_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0808b11f48455a6c95acba6644d8bcb657ae2df6d4fb0ac115e6617d34b827b
- Size of remote file:
- 5.27 kB
- SHA256:
- 76982e146094e34244372995079714a822c193f9f1de127fbda1668d1b221d48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.