Comments (2)
I am also interested in how many sequences you used to train the DNA tokenizer and what sequences those were
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Hey, the mask token prediction accuracy is about 30 percents and the MLM loss on the evaluation set is about 4.31 after the training. Also, the data used for tokenizer training is the entire pre-training corpus we used.
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Related Issues (20)
- While Doing Quick Start, I Encountered an AssertionError: HOT 2
- When will the pretraining code be available?
- .
- environment about torch version HOT 1
- problem stll in environment HOT 6
- hidden_states = model(inputs)[0] # [1, sequence_length, 768]-- Is the second dimension really the sequence length? HOT 1
- Discuss a question about k-mer
- When will the code for pre-training model and training BPE tokenizer be available?
- Quickstart Does not work and Embedding Dim is not 768
- Pretraining, Pretraining, Pretraining!!! HOT 2
- I always encounter this error during the fine-tuning evaluation phase HOT 1
- Fine-tune for continuous labels HOT 2
- How do I output the attention from the model? HOT 1
- Special token treatment.
- splice site predictions
- Unable to Retrieve ' hidden_states ' Despite ' Setting return_dict=True ' and ' output_hidden_states=True ' HOT 3
- Cannot Reproduce DNA-BERT2‘s Result HOT 1
- GUE+ datasets?
- Is it neccessary to train a specific BPE tokenizer on own datasets? HOT 1
- Getting embedding of a sequence
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