Instructions to use OliverHeine/albert-base-v2_train_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/albert-base-v2_train_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/albert-base-v2_train_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/albert-base-v2_train_v2") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/albert-base-v2_train_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba893cbccee0b52808cc8e91bc684ab4aba575fc13f0fff8dd04a8de3b4643e5
- Size of remote file:
- 5.27 kB
- SHA256:
- f4ee675f540d8632673a824b9eba3816135eaf952d9460d0935da1d0741a4fe7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.