Difference between revisions of "Eosc/NorBERT3 corpus"
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== Workflow == | == Workflow == | ||
| + | * [https://github.com/ChenghaoMou/text-dedup/tree/main/text_dedup De-duplication]: essentially, removing identical paragraphs using SimHash (similar to the NearDup approach in [https://aclanthology.org/2022.acl-long.577/ this paper], although they used MinHash; [https://pypi.org/project/mmh3/ MurMurHash] is another option). | ||
* [https://arxiv.org/abs/2112.11446 Cleaning] | * [https://arxiv.org/abs/2112.11446 Cleaning] | ||
| − | + | ||
| − | + | There are [https://github.com/ekzhu/datasketch other de-duplication packages] | |
== Sampling experiment == | == Sampling experiment == | ||
Revision as of 23:57, 22 October 2022
Workflow
- De-duplication: essentially, removing identical paragraphs using SimHash (similar to the NearDup approach in this paper, although they used MinHash; MurMurHash is another option).
- Cleaning
There are other de-duplication packages
Sampling experiment
We plan to create two versions of the training corpus:
- baseline (as is)
- Wikipedia+NCC+NAK multiplied by two to match the C4 size (oversampling quality data)
Vocabulary
Starting with 50K, following NorBERT-2. May be later experiment with other values.
To Decide
The size of NBDigital is 662M tokens. Should we use it? It probably overlaps a lot with NCC.
How should we split training corpora: one sentence per line, one paragraph per line, one document per line?
A: BERT assumes that there is one sentence per line.
