more details to the documentation of data preprocessing #94
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README.md
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| @@ -98,11 +98,11 @@ To get started with DCLM, follow these steps: | |||
| We recommend the use of Python 3.10 with DCLM. | |||
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| ## Selecting Raw Sources | |||
| If you are creating a new source: | |||
| If you are creating a new source (for example, Wikipedia, GitHub, etc.): | |||
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Maybe creating -> registering?
README.md
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| - Key names should be consistent with those in [here](baselines/core/constants.py). | ||
| - Create a reference JSON in [exp_data/datasets/raw_sources](exp_data/datasets/raw_sources). | ||
| - Ensure your data is stored in JSONL format (ideally compressed with zstandard), where each line correspond to single page. | ||
| - Key names in these JSONL should be consistent with those in [here](baselines/core/constants.py). |
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Maybe we can explain these in bit more detail here?
| @@ -119,6 +119,7 @@ To process raw data, follow these steps: | |||
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| 2. **Set up a Ray cluster**: | |||
| The data processing script relies on Ray for distributed processing of data. This cluster can be either launched on a single node (for small scale data processing) or using AWS EC2 instances. | |||
| There is also work to [deploy Ray on slurm setups](https://docs.ray.io/en/latest/cluster/vms/user-guides/community/slurm.html), though this effort is still a work-in-progres. | |||
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I feel like this isn't really WIP anymore, in the sense that it is possible to do so if you follow the guide.
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has one of us been able to get our scripts to work on slurm?
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I've been launching Ray jobs on TACC, so that part is stable - but I suppose you're right, best not to claim something that might be false
jeffreywpli
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README.md
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| - Key names should be consistent with those in [here](baselines/core/constants.py). | ||
| - Create a reference JSON in [exp_data/datasets/raw_sources](exp_data/datasets/raw_sources). | ||
| - Ensure your data is stored in JSONL format, ideally compressed with zstandard (though uncompressed or gzip-compressed files will also work), where each line corresponds to a single page/document. | ||
| - Key names in these JSONL should be consistent with those in [here](baselines/core/constants.py). Most importantly, there should be a ``"text"`` key for each line that contains the acftual content of the page. |
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typo: acftual - > actual
README.md
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| - Key names should be consistent with those in [here](baselines/core/constants.py). | ||
| - Create a reference JSON in [exp_data/datasets/raw_sources](exp_data/datasets/raw_sources). | ||
| - Ensure your data is stored in JSONL format, ideally compressed with zstandard (though uncompressed or gzip-compressed files will also work), where each line corresponds to a single page/document. | ||
| - Key names in these JSONL should be consistent with those in [here](baselines/core/constants.py). Most importantly, there should be a ``"text"`` key for each line that contains the acftual content of the page. |
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I would rephrase as follows:
Each row in these JSONL files corresponds to a document. Each row should contain keys consistent with those at ... and at minimum should contain at least a "text" key containing the actual content.
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