Comments (4)
Hi,
the provided LiLT-based
checkpoint is trained as described in the origin paper. It should be combined with the off-the-shelf plain text pre-trained models for fine-tuning.
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I went over the paper again, so LiLT-based initialize the text flow from the existing pre-trained English RoBERTa-BASE and when finetuning, it need to load RoBERTa-BASE again as the paper say "combine LiLTBASE with a new pre-trained RoBERTaBASE for finetuning"
If I am right, when finetuning in English, why reload RoBERTa-BASE again, it seems to be a little redundant
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It's a nice question. As you said, the pre-trained LiLT can be directly applied in English without re-load the text-part weights, and it can get a better result. However, we reload English RoBERTa-BASE again for consistency comparison in different languages.
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OK,I see, thanks for your reply
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Related Issues (20)
- Word or segment position embeddings? HOT 6
- Is LiLT-Large possible? HOT 1
- Pre-training code? HOT 5
- post custom dataset training ser on funsd model, inference issue HOT 1
- 代码运行问题 HOT 8
- Config error in Multi-task Semantic Entity Recognition on XFUND HOT 4
- Export model using distilroberta-base HOT 2
- Possibility to combine lilt-only-base with roberta-large HOT 2
- Usage with BigBird-Roberta-Base HOT 1
- Improve relation extraction HOT 7
- How we can use it for unstructured data HOT 1
- pip install -e . error
- how to train from scratch
- pre-processed data HOT 2
- How to decrease inference time of LiLT?
- LiLT can not make inference with the Half (float16) dtype on CPU
- Pretraining with other ROBERTa model HOT 1
- dataset format of FUNSD/XFUND
- Use LiLT / an alternative model with more than 512 tokens HOT 1
- RuntimeError: CUDA error: device-side assert triggered HOT 1
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