Comments (2)
Sorry for the delay in sharing the pre-training codes. We used and slightly modified the MosaicBERT implementation for DNABERT-2 https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert . You should be able to replicate the model training following the instructions.
Or you can use the run_mlm.py at https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling by importing the BertModelForMaskedLM from https://huggingface.co/zhihan1996/DNABERT-2-117M/blob/main/bert_layers.py. It should produce a very similar model.
The training data is available here. https://drive.google.com/file/d/1dSXJfwGpDSJ59ry9KAp8SugQLK35V83f/view?usp=sharing.
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Thanks a lot!
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Related Issues (20)
- About the pretrain data HOT 1
- Data distribution in pretraining dataset HOT 1
- Instability in reproducing GUE dataset result HOT 1
- Unable to reproduce covid results HOT 1
- Is there a way to turn off the setting to use flash attention/triton library? HOT 2
- How to specifically implement the task of Enhancer promoter interaction? HOT 1
- Whether huggingface released model has been further pretrained on GUE benchmark HOT 1
- Is it possible to publish the detailed requirement file?
- Is it neccessary to train a specific BPE tokenizer on own datasets? HOT 1
- Getting embedding of a sequence HOT 2
- CUDA out of memory HOT 8
- About random factor in the embedding/tokenization process HOT 7
- TypeError: __init__() got an unexpected keyword argument 'token' HOT 1
- GUE labels
- About the motif prediction function HOT 3
- Random issues still come up in use HOT 1
- Attention error HOT 1
- Got negative train loss when do pretrain process HOT 3
- What is the loss of pre-training of the published model? HOT 2
- EPI datasets, not getting published results HOT 3
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