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
Download tokenizer.json from slauw87/bart_summarisation: direct link, hf CLI and curl.
- Browser
- Download file 1.36 MB
-
https://huggingface.co/slauw87/bart_summarisation/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://slauw87/bart_summarisation@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/slauw87/bart_summarisation/resolve/refs%2Fpr%2F1/tokenizer.json
1.36 MB
File too large to display, you can check the raw version instead.