Comments (1)
HonLZL
commented on September 15, 2024
tokenized_inputs = tokenizer(
examples[text_column_name],
padding=padding,
truncation=True,
return_overflowing_tokens=True,
max_length=512,
# We use this argument because the texts in our dataset are lists of words (with a label for each word).
is_split_into_words=True,
)in run_funsd.py
set max_length=512, it works!
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
- 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
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