Comments (2)
patil-suraj
commented on June 11, 2024
2
Yes, beam search is especially useful for summarization, translation, and other fixed length generation tasks. Also all the generative results in the T5 paper are given using beam search.
And thank you for building awesome project! This is going to be super useful as people are using T5 for lot's of tasks both generative and discriminative.
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abelriboulot
commented on June 11, 2024
Thanks for the help on the generation patil, that was very useful!
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Related Issues (16)
- int() argument must be a string , when running exemple. HOT 3
- Use OnnxRuntime IO Binding to improve GPU inference performance HOT 3
- Can this model suitable for multilingual-t5 accelerate? HOT 2
- quantized models HOT 1
- How to suppress output
- Inference time on gpu vs onnxt5-gpu HOT 1
- Running example "export_pretrained_model.py" as-is fails (See details) HOT 3
- cpu only inferencing
- Repeat variables assignment both
- Can this be used with Flan-T5?
- Build a progress bar for the download of the initial files of the model
- Given model could not be parsed while creating inference session. Error message: Protobuf parsing failed. HOT 6
- Default T5 summary contains <extra_id_2>.<extra_id_3>.<extra_id_4> HOT 5
- Limit input ingestion to context length of the model
- Cosine similarity between embeddings HOT 7
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