Summarization
Transformers
PyTorch
ONNX
Safetensors
bart
text2text-generation
Generated from Trainer
seq2seq
Eval Results (legacy)
Instructions to use AdamCodd/bart-large-cnn-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/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="AdamCodd/bart-large-cnn-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AdamCodd/bart-large-cnn-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("AdamCodd/bart-large-cnn-samsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec3b134dc8a819311e846ecc61efb40d489abeb4faf3e7fb369bd1f6d8a97f64
- Size of remote file:
- 3.26 kB
- SHA256:
- ed6f21774000851ba8b9b0c7ac3bedf3c3fcbb69f8ff2cf15d3ed59bb6cfd894
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