Instructions to use mbzuai-ugrip-statement-tuning/MBERT_multilingual_2e-06_32_0.1_0.01_15k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbzuai-ugrip-statement-tuning/MBERT_multilingual_2e-06_32_0.1_0.01_15k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbzuai-ugrip-statement-tuning/MBERT_multilingual_2e-06_32_0.1_0.01_15k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbzuai-ugrip-statement-tuning/MBERT_multilingual_2e-06_32_0.1_0.01_15k") model = AutoModelForSequenceClassification.from_pretrained("mbzuai-ugrip-statement-tuning/MBERT_multilingual_2e-06_32_0.1_0.01_15k", device_map="auto") - Notebooks
- Google Colab
- Kaggle