Instructions to use kasperchux/Bart-Base-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kasperchux/Bart-Base-Summarization 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="kasperchux/Bart-Base-Summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kasperchux/Bart-Base-Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("kasperchux/Bart-Base-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 136 Bytes
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license: mit
datasets:
- cnn_dailymail
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: summarization
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