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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 094783c15d488b860e6fd356bbf922545cfe980950031d5c5b850257948fdeb5
- Size of remote file:
- 2.48 kB
- SHA256:
- d3729b6a59dcbb6d26024f1d67745c3a52c14ac2937d8bd6042eb1e9c6a15f1c
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