Translation
Transformers
PyTorch
Safetensors
Belarusian
bart
text2text-generation
seq2seq
lemmatisation
Instructions to use djulian13/be-tiny-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djulian13/be-tiny-bart with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="djulian13/be-tiny-bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("djulian13/be-tiny-bart") model = AutoModelForSeq2SeqLM.from_pretrained("djulian13/be-tiny-bart") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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### Out-of-Scope Use
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Downstream use and further fine-tuning (for instance, for text-to-SQL transformation) seem to be
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## Bias, Risks, and Limitations
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#### Summary
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Generally, it is possible to use this model for the preliminary tagging of Belarusian. However, if there are better options (for instance, disambiguation of existing multiple
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## Environmental Impact
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### Out-of-Scope Use
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Downstream use and further fine-tuning (for instance, for text-to-SQL transformation) seem to be not fruitful: the model has been fine-tuned for a very specific task, which is not scalable to the other types of sequence-to-sequence transformations.
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## Bias, Risks, and Limitations
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#### Summary
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Generally, it is possible to use this model for the preliminary tagging of Belarusian. However, if there are better options (for instance, disambiguation of existing multiple tag candidates with LLMs), it is better to go with them.
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## Environmental Impact
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