Comments (1)
Thanks @GeorgeS2019 for your suggestion.
I know sub-word tokenization is really useful for text generation tasks, such as MT task could get 2~3pt BLEU scores gain on average and some NN frameworks did integrate sub-word tokenization, such as Marian uses built-in SentencePiece for data processing,
However, since it's a part of data processing, and include several key steps, such as model training, encoding and decoding, I prefer to create separate project for it rather than integrating it to Seq2SeqSharp project.
So, in my opinion, my plan would be 1) Create a project for BPE training/encoding/decoding called SubwordSharp. :) 2) Create a training pipeline to integrate SubwordSharp BPE model training, BPE encoding, and Seq2SeqSharp training, BPE decoding and evaluation steps together, and 3) Create a runtime pipeline to integrate BPE encoding, Seq2SeqSharp inference, BPE decoding together.
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Related Issues (20)
- Didn't save the model? HOT 7
- Error: C# 8.0 language feature HOT 1
- sentencepiece.dll problem in the API HOT 2
- SeqClassification Validation HOT 16
- Exception: 'The weight '.LayerNorm' has been released, you cannot access it.' HOT 10
- CPU_MKL Error converting value "CPU_MKL" to type 'Seq2SeqSharp.ProcessorTypeEnums HOT 6
- sqc.m_srcEmbedding_p.GetNetworkOnDevice(k).GetWeightAt() HOT 1
- GPTconsole HOT 4
- Target vocabulary size fixed to 45000 HOT 5
- Contextual embeddings HOT 22
- Train with general sequences of symbols HOT 2
- Moment of updating weights HOT 4
- Issues to get started with "Seq2SeqClassificationConsole" HOT 33
- Matrix initialization method HOT 4
- No requirement.txt in this repo HOT 2
- Sudden high increase in memory consumption while training a seq2seq model and validation happens HOT 11
- Setting FocalLossGamma = 2 causes weight corruption in the beginning of the seq2seq model training
- The checkpoint to save the model regularly should not depend on validation HOT 1
- Serialization of Seq2seq model is wrong
- SeqLabel model backward compatibility is broken by latest update HOT 3
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