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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LeoCordoba/mt5-small-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("LeoCordoba/mt5-small-mlsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 550059230b0c88c82394359b718bd6b4fd4b68a2c3a7ac824f20dde065af932d
- Size of remote file:
- 1.2 GB
- SHA256:
- 4685a5d2dd964b1d4b17aa216f5c93a518328e5a4040b8a1a52eb3cd105addc9
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