Instructions to use imvladikon/het5_small_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imvladikon/het5_small_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="imvladikon/het5_small_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("imvladikon/het5_small_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("imvladikon/het5_small_summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24f30fe4d1e0a96399b8c68a641293779144b27c62dc27d044d0a22840a417ba
- Size of remote file:
- 4.09 kB
- SHA256:
- f7ee933c8d4adc27cb0c3e1e0d0fce5e38fe0c4eea9cb47f843e43410605f744
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.