Difference between revisions of "Eosc/NorBERT3 corpus"
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== To Decide == | == To Decide == | ||
| − | The size of NBDigital is 662M tokens. Should we use it? It probably overlaps a lot with NCC. | + | Q: 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: No, it isn't. Only 60 paragraphs out of total 18M in NCC are duplicates of paragraphs in NBDigital. Thus, we definitely should use it. |
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| + | Q: 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. | A: BERT assumes that there is one sentence per line. | ||
Revision as of 18:49, 24 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
Q: The size of NBDigital is 662M tokens. Should we use it? It probably overlaps a lot with NCC.
A: No, it isn't. Only 60 paragraphs out of total 18M in NCC are duplicates of paragraphs in NBDigital. Thus, we definitely should use it.
Q: 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.
