Instructions to use mbzuai-ugrip-statement-tuning/MBERT_revised_1e-06_32_0.1_0.01_110k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbzuai-ugrip-statement-tuning/MBERT_revised_1e-06_32_0.1_0.01_110k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbzuai-ugrip-statement-tuning/MBERT_revised_1e-06_32_0.1_0.01_110k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbzuai-ugrip-statement-tuning/MBERT_revised_1e-06_32_0.1_0.01_110k") model = AutoModelForSequenceClassification.from_pretrained("mbzuai-ugrip-statement-tuning/MBERT_revised_1e-06_32_0.1_0.01_110k", device_map="auto") - Notebooks
- Google Colab
- Kaggle