Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slauw87/bart_summarisation 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="slauw87/bart_summarisation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
f39fcca
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Upload generated_predictions.txt
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Upload merges.txt
61757ab -
Upload trainer_state.json
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Upload train_results.json
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Upload all_results.json
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Upload special_tokens_map.json
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Upload eval_results.json
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Upload vocab.json
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Upload training_args.bin with git-lfs
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Upload tokenizer_config.json
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Upload tokenizer.json
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Upload config.json
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Upload pytorch_model.bin
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Create README.md
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initial commit
4ac1b34