Summarization
Transformers
Safetensors
Portuguese
t5
text2text-generation
Generated from Trainer
text2text
text-generation-inference
Instructions to use rhaymison/t5-portuguese-small-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rhaymison/t5-portuguese-small-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="rhaymison/t5-portuguese-small-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rhaymison/t5-portuguese-small-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("rhaymison/t5-portuguese-small-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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This model aims to help supply the needs of models in the Portuguese language for certain tasks. The model presents a good performance for summary tasks.
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Some errors due to word accentuation may occasionally occur due to the small version of the model.
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model_max_length = 512
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Card example 1 with t5-portuguese-small-summarization
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This model aims to help supply the needs of models in the Portuguese language for certain tasks. The model presents a good performance for summary tasks.
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Some errors due to word accentuation may occasionally occur due to the small version of the model.
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model_max_length = 512
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Card example 1 with t5-portuguese-small-summarization
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