Comments (2)
Hi,
(1) BERT-E is short for the "Enhanced BERT baseline". You can implement it by removing rs
in model.py
(line 98 and 99) and replacing the adaptive thresholding loss with binary cross entropy.
(2) "-Adaptive-thresholding loss" means using the adaptive thresholding strategy in inference (output all classes with larger logits than NA) while training the model with binary cross entropy. It demonstrates that adaptive thresholding only works in models trained with our loss function.
from atlop.
Thx for your reply!
from atlop.
Related Issues (20)
- Instruction to extract relation on a custom dataset
- The problem of setting environment HOT 1
- Inference HOT 1
- The main purpose of the function: get_label HOT 5
- How should I be running the Enhanced BERT Baseline model? HOT 2
- Where did the "/meta/rel2id.json" come from? HOT 2
- Apply model on free text data HOT 1
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- end_tokens for RoBERTa? HOT 1
- Mention embedding HOT 4
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from atlop.