Summarization
Transformers
PyTorch
TensorBoard
Swedish
bart
text2text-generation
Eval Results (legacy)
Instructions to use Gabriel/bart-base-cnn-swe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gabriel/bart-base-cnn-swe with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="Gabriel/bart-base-cnn-swe")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gabriel/bart-base-cnn-swe") model = AutoModelForSeq2SeqLM.from_pretrained("Gabriel/bart-base-cnn-swe") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# bart-base-cnn-swe
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This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1656
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# bart-base-cnn-swe
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WORK IN PROGRESS!
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- 1. Further fine-tune on checkpoint with cnn-daily-swe.
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- 2. Further fine-tune on xsum-swe.
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- 3. Lastly fine-tune on smaller domain dataset.
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This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1656
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