Summarization
Transformers
PyTorch
JAX
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Instructions to use efederici/it5-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/it5-base-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="efederici/it5-base-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("efederici/it5-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("efederici/it5-base-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5ce9a76b716cc8c1c4e35c0a7e59176c85a85d2386945e9d7d9cb65eb587c85
- Size of remote file:
- 990 MB
- SHA256:
- 038f6cb0137235996122785104da8cefb86a04d2b56f775031a3d59d5fce6b62
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.