Instructions to use cnmoro/ptt5-base-ptbr-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnmoro/ptt5-base-ptbr-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="cnmoro/ptt5-base-ptbr-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cnmoro/ptt5-base-ptbr-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("cnmoro/ptt5-base-ptbr-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c84c6518fa2975293dabf52a432d2247aea8b615495d46cb40130d6fca40047
- Size of remote file:
- 892 MB
- SHA256:
- 71dfd0fc4ce9329b703afcf12d4f63e6077d3155e5565607f1379617d2b4e4ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.