Instructions to use classla/xlm-r-bertic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/xlm-r-bertic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="classla/xlm-r-bertic")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("classla/xlm-r-bertic") model = AutoModelForMaskedLM.from_pretrained("classla/xlm-r-bertic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## NER
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## NER
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Average macro-F1 scores from three runs were used to evaluate performance. Datasets used: [hr500k](https://huggingface.co/datasets/classla/hr500k), [ReLDI-sr](https://huggingface.co/datasets/classla/reldi_sr), [ReLDI-hr](https://huggingface.co/datasets/classla/reldi_hr), and [SETimes.SR](https://huggingface.co/datasets/classla/setimes_sr).
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