Comments (3)
Hi,
you can read our original paper at https://aclanthology.org/2022.acl-long.534/. As explained in it, LiLT-base+En-Roberta are pre-trained using English docs. And the provided "lilt-only-base" is exactly the pre-trained LiLT-base part. It can be used to combine different textual models to deal with docs in different languages during fine-tuning.
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Hi, you can read our original paper at https://aclanthology.org/2022.acl-long.534/. As explained in it, LiLT-base+En-Roberta are pre-trained using English docs. And the provided "lilt-only-base" is exactly the pre-trained LiLT-base part. It can be used to combine different textual models to deal with docs in different languages during fine-tuning.
I understand the usage. But I am very curious how the file "lilt-only-base" is created. As you have mentioned what is "pre-trained LiLT-base part" how that specific base part is created.
We all know how roberta-en is created and from your provided code how "gen_weight_roberta_like.py" generates the base + roberta model.
What does the base part contains.
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Pytorch uses a dict-like format to store weight name-value pairs in 'pytorch_model.bin' files. We just filter out the name-value pairs of the LiLT part by weight names from the pre-trained checkpoint to create "lilt-only-base".
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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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