Instructions to use DaviadiAF/T5-Small_AbsSumm_XSumCNN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaviadiAF/T5-Small_AbsSumm_XSumCNN 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="DaviadiAF/T5-Small_AbsSumm_XSumCNN")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DaviadiAF/T5-Small_AbsSumm_XSumCNN") model = AutoModelForSeq2SeqLM.from_pretrained("DaviadiAF/T5-Small_AbsSumm_XSumCNN") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c1d52c13cbd2cea5529a04d462d740d74897db6b9e13e016da26a92f5b84941
- Size of remote file:
- 4.22 kB
- SHA256:
- 7caf23864f4516be6dde058af4d2b0d024f196e55469cb53776af30f29d9908b
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