Instructions to use OliverHeine/albert-base-v2_fold_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/albert-base-v2_fold_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/albert-base-v2_fold_9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/albert-base-v2_fold_9") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/albert-base-v2_fold_9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63c41afae21d7a0973574059834b4742e07b734644387b2b62dc5a4d21bc8f24
- Size of remote file:
- 5.27 kB
- SHA256:
- f7399e5854ca824cfcbde6d44c904af994d31691db40c8a2a6d41c0f0a4dbbb8
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