Instructions to use textattack/albert-base-v2-CoLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-CoLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-CoLA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-CoLA") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-CoLA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82e346f35c277d9defbcd34ad7a28c7147cc892d530f953dcd4c101e0511d9d7
- Size of remote file:
- 46.7 MB
- SHA256:
- c8ea6503e27f038ffb059d71614e8e5dcde8f1071a15bddb3a55b6e399e25673
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.