Datasets:
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Update README.md
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README.md
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@@ -209,8 +209,8 @@ RedPajama-V2 is an open dataset for training large language models and includes
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| ccnet_language_score | score of the language identification model | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_length | number of characters | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_nlines | number of lines | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_original_length | number of characters before
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| ccnet_original_nlines | number of lines before
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| ccnet_perplexity | perplexity of an LM trained on Wikipedia | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| rps_doc_books_importance | Given a bag of {1,2}-wordgram model trained on Books p, and a model trained on the source domain q, This is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
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| rps_doc_openwebtext_importance | Given a bag of {1,2}-wordgram model trained on OpenWebText p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
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| ccnet_language_score | score of the language identification model | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_length | number of characters | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_nlines | number of lines | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_original_length | number of characters before line-level deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_original_nlines | number of lines before line-level deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| ccnet_perplexity | perplexity of an LM trained on Wikipedia | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
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| rps_doc_books_importance | Given a bag of {1,2}-wordgram model trained on Books p, and a model trained on the source domain q, This is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
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| rps_doc_openwebtext_importance | Given a bag of {1,2}-wordgram model trained on OpenWebText p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
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