In the pre-training stage, we add another level of BERT adaptation on sub-domain data to bridge the gap between domain knowledge and task-specific knowledge. Also, we propose methods to incorporate the ignored knowledge in the last layer of BERT to improve its fine-tuning. Results The ...
BatchSize 4 2 Learning_rate (SciBERT) 1e−5 1e−5 Learning_rate (Other) 1e−3 1e−3 Dropout 0.2 0.2 Warmup_rate 0.1 0.1 Max_Grad_Norm 1.0 1.0 Epoch 100 100 Max_seq_length 150 150 6.3. Main Results To assess our model’s performance, we compared it with various baseline ...
python example/train_bag_cnn.py \ --metric auc \ --dataset nyt10m \ --batch_size 160 \ --lr 0.1 \ --weight_decay 1e-5 \ --max_epoch 100 \ --max_length 128 \ --seed 42 \ --encoder pcnn \ --aggr att Or use the following script to train a BERT model on the Wiki80 dat...
The epoch and minibatch sizes were 50 and 5, respectively. A Nadam optimizer (Dozat, 2016) was used for training, with a learning rate of 0.001. We implemented our model using TensorFlow.6 4.2. Comparative Methods To validate our proposed method, we considered multiple comparative conventional...
The relevant parameters for training on NELL-One and Wiki-One are as follows: entity and relation embedding dimensions are set to 100 and 50, respectively; batch size is set to 128; the initial learning rates (𝑙𝑟lr) are set to 5 × 10−5 and 6 × 10−5 for NELL-One and ...
Medical entity relation extraction is the classification of relation categories between entity pairs in unstructured medical texts. These relations exist in the form of triples (<subject, predicate, object>), which are called entity relations triples. Relation extraction is the key and difficult part ...
Running time is counted on a single Maxwell Titan X GPU (mini-batch size is 1 in inference). Requirements: Software MXNet fromthe offical repository. We tested our code onMXNet v1.1.0@(commit 629bb6). Due to the rapid development of MXNet, it is recommended to checkout this version if...
= [8000,19]21returnDataLoader(ds, arg.BATCH_SIZE, shuffle=True) 在此函数内部第2行我们的数据进行了向量化: 1defpos(x):2'''3map the relative distance between [0, 123)4e1,e2离得过远返回0或者1225默认距离如果超过60返回极端值表示这里的单词与e1关系不大6'''7ifx < -60:8return09if60 >= ...
We train on 4 GPUs (so effective mini- batch size is 8) and each image has 128 sampled anchors, with a ratio of 1:3 of positive to negatives [13]. We im- plement MTOR based on MXNet [3]. Specifically, the net- work weights are trained by SGD op...
python main.py --log_name test --cuda 0 --epoch 100 --weight_decay 0.00001 --label_weights 5 --optimizer SGD --lr 0.1 --bio_embed_dim 25 --dep_embed_dim 50 --num_steps 50 --rnn_dropout 0.6 --gcn_dropout 0.6 --train True --batch_size 30 ...