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74,603

HTML2Markdown Training Data Builder

  1. Download the logs from the jina-reader via gsutil:
gsutil -m cp -r \
 "gs://reader-6b7dc.appspot.com/crunched-pages/r7" \
 /path/to/r7-raw
  1. Parse the logs and build the training data:
python datasets/parse_dataset.py /path/to/r7-raw /path/to/target_data

It is recommended to use the jina to build the dataset parallelly:

pip install -U jina
python datasets/parallel_parse_dataset.py \
    /path/to/r7-raw \
    /path/to/outputs \
    --num-workers 32 \
    --batch-size 256
  1. Then, you can push the data to huggingface datasets:
huggingface-cli login
huggingface-cli dataset create jinaai/html2markdown /path/to/target_data

Filtered Data for Training

JINA_MP_START_METHOD=spawn \
CUDA_VISIBLE_DEVICES=0,1,2,3,4 \
python datasets/parallel_filter_by_ppl.py \
    jinaai/jina-reader-html-v2 \
    xxsmall \
    --sample-size 1000 \
    --num-workers 3

Note that

  • the jinaai/jina-reader-html-v2 is the dataset name on huggingface datasets.
  • the xxsmall is the config name in the jina-reader-html config file.
  • the --sample-size is the number of samples to be filtered.
  • the --num-workers is the number of workers to be used in the filtering process.

You can refer to here for more details about how the jina use multiple GPUs with replicas.

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