Summarization
Transformers
ONNX
Safetensors
bart
feature-extraction
Generated from Trainer
seq2seq
Eval Results (legacy)
Instructions to use AdamCodd/bart-large-xsum-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/bart-large-xsum-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-xsum-samsum")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AdamCodd/bart-large-xsum-samsum") model = AutoModel.from_pretrained("AdamCodd/bart-large-xsum-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding ONNX file of this model
#1
by AdamCodd - opened
Beep boop I am the ONNX export bot π€ποΈ. On behalf of AdamCodd, I would like to add to this repository the model converted to ONNX.
What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at onnx.ai!
The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with π€ Optimum through ONNX Runtime, check out how here!
AdamCodd changed pull request status to merged