Summarization
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
mt5
text2text-generation
italian
sequence-to-sequence
fanpage
ilpost
Instructions to use gsarti/mt5-base-news-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/mt5-base-news-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-base-news-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/mt5-base-news-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/mt5-base-news-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gsarti/mt5-base-news-summarization: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/gsarti/mt5-base-news-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gsarti/mt5-base-news-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gsarti/mt5-base-news-summarization/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 46baccf5c9cb7df8552e819dafb19063618484de18fc6613f41163c6b71d8344
- Size of remote file:
- 2.33 GB
- SHA256:
- 71f6daac0c85287d3f2ec23c57df0969aa6c11233230cc79fe7efc6fca6b13ca
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