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Added information about the use of slow tokenizers #2517

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Added information about the use of slow tokenizers #2517

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25cacb9
Added information about the use of slow tokenizers
ppf2 Aug 11, 2025
2de8432
Update explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md
ppf2 Aug 13, 2025
4dc1a63
Merge branch 'main' into ppf2-slow-tokenizers
vishaangelova Aug 15, 2025
29ec13c
Update explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md
ppf2 Aug 15, 2025
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2 changes: 1 addition & 1 deletion explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md
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Expand Up @@ -15,7 +15,7 @@ products:
The minimum dedicated ML node size for deploying and using the {{nlp}} models is 16 GB in {{ech}} if [deployment autoscaling](../../../deploy-manage/autoscaling.md) is turned off. Turning on autoscaling is recommended because it allows your deployment to dynamically adjust resources based on demand. Better performance can be achieved by using more allocations or more threads per allocation, which requires bigger ML nodes. Autoscaling provides bigger nodes when required. If autoscaling is turned off, you must provide suitably sized nodes yourself.
::::

The {{stack-ml-features}} support transformer models that conform to the standard BERT model interface and use the WordPiece tokenization algorithm.
The {{stack-ml-features}} support transformer models that conform to the standard BERT model interface and use the WordPiece tokenization algorithm. {{stack-ml-features}} will always use the non-fast ("slow") tokenizer variant for all supported models. This ensures deterministic and stable tokenization results across different platforms and avoids potential differences in handling between fast and slow implementations.

The current list of supported architectures is:

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