Comments (2)
Hi, it's hard to write out all the steps without knowing where you're stuck. Did you have a look at https://github.com/JonasGeiping/cramming#data-handling?
Basically the steps are as follows for a custom dataset:
- Create a new .yaml file in
config/data
, you can copy from one of the existing ones - Fill out the sources list (The first argument). You can either create a dataset from the sources already implemented (see
config/data/sources
), or add new sources yourself. The easiest sources to add are huggingface datasets (see https://github.com/JonasGeiping/cramming/blob/main/cramming/config/data/sources/bookcorpus.yaml for example) - Fill out all the other information, choosing normalization, tokenizer type etc.
- Run preprocessing by starting a pretraining run. The run will check your base folder and prepare the dataset according to the config if it cannot be found. Depending on your settings and dataset size, this might require larger amounts of RAM, which you can control by setting
impl.threads
to a smaller number andimpl.max_raw_chunk_size
to a smaller number.
from cramming.
Hope this helps. Closing this for now.
from cramming.
Related Issues (20)
- data preprocessing got failed during tokenization on single GPU HOT 9
- RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)` while running evaluation HOT 10
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- GLUE evaluation numbers are very poor, if increase the sequence length to 512 and float 32 HOT 5
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- Issue with torch.compile / dynamo HOT 5
- Question about sparse token prediction HOT 1
- Uploading trained model to HF/saving in HF format locally HOT 8
- Finetuning for SQuAD task HOT 2
- try it on Mac M1 but failed HOT 2
- can't import cramming HOT 2
- TypeError: _load_optimizer() missing 1 required positional argument: 'initial_time' HOT 1
- torch._dynamo error on step 2: calling compiler function 'inductor' HOT 7
- Finetuning for token classification HOT 3
- Configs for GPT? HOT 2
- From PR 43 HOT 5
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from cramming.