Comments (1)
gradient_acc_steps = 2 will wait for 2 batch passes before backpropagating gradients. Allows you to effectively increase batch size by 2 while keeping GPU memory fixed
from bert-relation-extraction.
Related Issues (20)
- Output type rather than [CLS] is supported? HOT 1
- Unable to initialize weights
- Different Embeddings for [E1] [/E1] [E2] [/E2] tokens HOT 1
- bad accuracy on test data HOT 3
- Development Set HOT 1
- BERT_REL with SciBert HOT 3
- Training and prediction on new dataset HOT 3
- Infer from code (args.pkl) HOT 5
- Infer.py function “annotate_sent” has a problem. HOT 1
- FewRel fine-tuning? HOT 1
- FewRel: meta_labels[-1].item() is always 4 HOT 2
- Pre-trained final model HOT 2
- Why is num_classes a command line argument and not directly inferred? HOT 1
- Can't infer on my own sentences after finetuning model HOT 2
- No File of BioBERT exist
- Files of BioBERT Lost HOT 1
- Missing ./data/BERT_tokenizer.pkl file HOT 5
- Standard batching vs NCE?
- Error while Running the files HOT 2
- Change the way "en_core_web_sm" is loaded to fix OSError: [E050] HOT 1
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from bert-relation-extraction.