Instructions to use hajili/bert-base-cased-azerbaijani with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hajili/bert-base-cased-azerbaijani with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hajili/bert-base-cased-azerbaijani")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hajili/bert-base-cased-azerbaijani") model = AutoModelForMaskedLM.from_pretrained("hajili/bert-base-cased-azerbaijani", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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It achieves the following results on the evaluation set:
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- Loss: 0.7046
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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It achieves the following results on the evaluation set:
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- Loss: 0.7046
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We thank Microsoft Accelerating Foundation Models Research Program for supporting our research.
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Authors: Mammad Hajili, Duygu Ataman
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## Training procedure
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