Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use philschmid/bart-large-cnn-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/bart-large-cnn-samsum 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="philschmid/bart-large-cnn-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("philschmid/bart-large-cnn-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("philschmid/bart-large-cnn-samsum", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from philschmid/bart-large-cnn-samsum: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://philschmid/bart-large-cnn-samsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 51cf744ca3a7a9610981ab86aa109d979fa51fdc2f2c77a4a766a6b3a9b65d7d
- Size of remote file:
- 1.63 GB
- SHA256:
- 9f453aa6edef4dba1893723b7313b57b06b60214442d308a8acc3baa9583dd7b
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