Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use sambydlo/bart-large-scientific-lay-summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sambydlo/bart-large-scientific-lay-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="sambydlo/bart-large-scientific-lay-summarisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sambydlo/bart-large-scientific-lay-summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("sambydlo/bart-large-scientific-lay-summarisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a98adb3bab573162f7a44bc0cd45396555d1628bb2aecf6def336f35edd9d8
- Size of remote file:
- 1.63 GB
- SHA256:
- 1564333254dd2bfe0d7d3e61f4ddaee6007cb9df0fce3c9aa5f18fe493979671
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