cjymz886 / gfner Goto Github PK
View Code? Open in Web Editor NEWA Unified Global Feature-aware Framework for Flat and Nested Named Entity Recognition
License: MIT License
A Unified Global Feature-aware Framework for Flat and Nested Named Entity Recognition
License: MIT License
我发了一封邮件,希望获取到您所用的数据集~
发件人:[[email protected]]
Can you please share the dataset you used, the dataset I found so far is handled differently than yours and the code doesn't make sense!
Hi, I cannot reproduce the CoNLL2003 dataset results for f1=93.85 in your paper; the results only reached f1=91.
The parameters and model I used are from your code.:
vocab_size=30290
embedding_size=128
seq_len=127
random_seed = 2009
hidden_dropout_prob=0.1
learning_rate=3e-5
weight_decay=0.0
max_grad_norm=1.0
warmup=0.0
fix_bert_embeddings=False
min_num=1e-7
use_bert = True
batch_size=6
epochs=50
device = torch.device("cuda")
#bert_model = 'biobert-base-cased-v1.2'
#bert_model='biobert-large-cased-v1.1'
bert_model = 'bert-large-cased'
If available, could you provide me with a detailed list of the parameters you used for training?
Thank you !
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