Summarization
Transformers
PyTorch
JAX
Safetensors
Spanish
mt5
text2text-generation
sagemaker
spanish
Eval Results (legacy)
Instructions to use LeoCordoba/mt5-small-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeoCordoba/mt5-small-mlsum 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="LeoCordoba/mt5-small-mlsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LeoCordoba/mt5-small-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("LeoCordoba/mt5-small-mlsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LeoCordoba/mt5-small-mlsum: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/LeoCordoba/mt5-small-mlsum/resolve/main/training_args.bin
- Command line
-
hf download hf://LeoCordoba/mt5-small-mlsum/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/LeoCordoba/mt5-small-mlsum/resolve/main/training_args.bin
2.35 kB
- Xet hash:
- 1bdbd63e45bab9e4a5e609e855cc7d1baf4fd64799dd1fe5486f26759b8b0448
- Size of remote file:
- 2.35 kB
- SHA256:
- 5a06ccbebdbae67cab496e1bec18b38558efc6e9c7bf93f835222fe5f03dccf9
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