Comments (3)
Hi,
I think the main steps should be:
- Organize your dataset into the format of FUNSD/XFUND, depending on your dataset is monolingual/multilingual.
- Put
YourDataset.py
underLiLTfinetune/data/datasets/
. You can refer to funsd.py/xfun.py. - Put
run_YourDataset_YourTask.py
underexamples/
. You can refer to run_funsd.py/run_xfun_re.py/run_xfun_ser.py.
If you want to do something beyond training/evaluating, You can add your code to the lines after the model makes predictions, such as https://github.com/jpWang/LiLT/blob/main/examples/run_funsd.py#L345 in run_funsd.py.
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Hi,
See also my demo notebooks here: https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT
from lilt.
Hello,
Could you let me know when you have a Custom dataset and how to organize your dataset into the format of FUNSD/XFUND?
and do you recommend any tutorial for this step?
Thank you in advance.
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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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