Instructions to use mbzuai-ugrip-statement-tuning/XLMR_large2-multif_2e-06_16_0.1_0.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbzuai-ugrip-statement-tuning/XLMR_large2-multif_2e-06_16_0.1_0.01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbzuai-ugrip-statement-tuning/XLMR_large2-multif_2e-06_16_0.1_0.01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbzuai-ugrip-statement-tuning/XLMR_large2-multif_2e-06_16_0.1_0.01") model = AutoModelForSequenceClassification.from_pretrained("mbzuai-ugrip-statement-tuning/XLMR_large2-multif_2e-06_16_0.1_0.01") - Notebooks
- Google Colab
- Kaggle