Comments (6)
Is it ok to use roberta-large models also?
from lilt.
Hi,
due to the limitation of computing resources, we haven't trained the large LiLT model yet. It is considered for future work.
from lilt.
+1 for training LiLT-Large ๐๐ป
from lilt.
will this ever be done? I am trying to use LILT but we have some forms that go over the 512 limit and we can't just truncate as the data could be anywhere in the tokens.
from lilt.
@AnQuethit it seems this project is mostly dead in terms of development.
My solution for that was to just tokenize longer documents in chunks with some overlap
The doc_stride parameter in huggingface tokenizer is very useful for this
from lilt.
i ran across the stride parameter 30 minutes ago but i haven't figured out how to get around the error it causes. do you have working code with it save me some time lol ?
Couldn't cast array of type
list<item: int64>
to
int64
from lilt.
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
- ไปฃ็ ่ฟ่ก้ฎ้ข 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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from lilt.