Instructions to use mor40/BulBERT-chitanka-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mor40/BulBERT-chitanka-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mor40/BulBERT-chitanka-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mor40/BulBERT-chitanka-model") model = AutoModelForMaskedLM.from_pretrained("mor40/BulBERT-chitanka-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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A LLM trained from scratch on bulgarian data.
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The model and the model's tokenizer are trained _from scratch_ on bulgarian data from the **chitanka** dataset.
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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#### Metrics
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Perprelixty - 6.75
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[More Information Needed]
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A LLM trained from scratch on bulgarian data.
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The model and the model's tokenizer are trained _from scratch_ on bulgarian data from the **chitanka** dataset.
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#### Metrics
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Perprelixty - 6.75
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