Summarization
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
mt5
text2text-generation
italian
sequence-to-sequence
wikipedia
wits
Instructions to use gsarti/mt5-small-wiki-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/mt5-small-wiki-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="gsarti/mt5-small-wiki-summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/mt5-small-wiki-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/mt5-small-wiki-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gsarti/mt5-small-wiki-summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/gsarti/mt5-small-wiki-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gsarti/mt5-small-wiki-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gsarti/mt5-small-wiki-summarization/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 2434c8c6cac7e7ac290ed73708a2172e9ab4a8ecc314e8dca9248f09802182b6
- Size of remote file:
- 1.2 GB
- SHA256:
- 1b558b379ba1fc517d2c4919d0a1d86391fa038cdc85940a285c7c5d629a3259
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