Summarization
Transformers
PyTorch
TensorBoard
English
pegasus
text2text-generation
Generated from Trainer
Instructions to use ChaniM/tst-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChaniM/tst-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="ChaniM/tst-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ChaniM/tst-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ChaniM/tst-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1e002fe64865abde1e7142dc2f62ebe310fd632a108727759e1af237a31ff8a
- Size of remote file:
- 2.28 GB
- SHA256:
- f0fb142bca8a6b46c090c7396cdd377f31fc8166bc499fdb685f2c629c94d92e
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