Instructions to use sohidalg/multiun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sohidalg/multiun 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="sohidalg/multiun")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sohidalg/multiun") model = AutoModelForSeq2SeqLM.from_pretrained("sohidalg/multiun") - Notebooks
- Google Colab
- Kaggle
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# multiun
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the
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It achieves the following results on the evaluation set:
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- Loss: 1.0111
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- Bleu: 48.9323
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# multiun
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the Helsinki-NLP/multiun dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0111
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- Bleu: 48.9323
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