Closed yhcc closed 2 years ago
Hi,
To reproduce our results, you may first setup the environment following README
and this link, and then run:
$ python scripts/entity_recognition.py @scripts/options/with_bert.opt \
--dataset conll2003 \
--doc_level \
--num_epochs 50 \
--lr 2e-3 \
--finetune_lr 2e-5 \
--batch_size 48 \
--ck_decoder boundary_selection \
--sb_epsilon 0.3 \
--sb_size 1 \
--bert_drop_rate 0.2 \
--use_interm2 \
--bert_arch RoBERTa_base
You may change the sb_epsilon
to 0.2 for OntoNotes 5, and the other hyper-parameters are the same.
You will easily get F1-score of 93.5+ on CoNLL 2003, and 91.5+ on OntoNotes 5.
Further improvements may be achieved by using smaller batch sizes or tuning the learning rates.
May I ask what is the gpu used to run this parameter?
Sure. We used V100.
If your device does not have large memory, you may use gradient accumulation to simulate the batch size, e.g.,
$ python scripts/entity_recognition.py @scripts/options/with_bert.opt \
--dataset conll2003 \
--batch_size 4 \
--num_grad_acc_steps 12 \
[other options]
Sure. We used V100.
If your device does not have large memory, you may use gradient accumulation to simulate the batch size, e.g.,
$ python scripts/entity_recognition.py @scripts/options/with_bert.opt \ --dataset conll2003 \ --batch_size 4 \ --num_grad_acc_steps 12 \ [other options]
你好,我目前在CoNLL 2003上进行尝试。请问使用--doc_level参数,将sequence合并变成文档级别的sequence,你所设置的文档级别的max_len是什么?我尝试了一下将max_len设置为510,然后其他参数按照您上面给出的参数配置,验证时只在test_data上得到了92.95%的F1分数,我想这可能与max_len的大小有关。
Hi, after reading your paper "Boundary Smoothing for Named Entity Recognition", I was glad to see that the performance was further promoted by your method. I want to reproduce these results, is there any detailed introduction to rerun the experiments by the eznlp framework, especially the specific hyper-parameters used.