Instructions to use ukr-models/xlm-roberta-base-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ukr-models/xlm-roberta-base-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ukr-models/xlm-roberta-base-uk")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ukr-models/xlm-roberta-base-uk") model = AutoModelForMaskedLM.from_pretrained("ukr-models/xlm-roberta-base-uk") - Notebooks
- Google Colab
- Kaggle
This is a smaller version of the XLM-RoBERTa model with only Ukrainian and some English embeddings left.
- The original model has 470M parameters, with 384M of them being input and output embeddings.
- After shrinking the
sentencepiecevocabulary from 250K to 31K (top 25K Ukrainian tokens and top English tokens) the number of model parameters reduced to 134M parameters, and model size reduced from 1GB to 400MB.
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