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Copy file name to clipboardExpand all lines: docs/reference/enrich-processor/inference-processor.md
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@@ -127,8 +127,8 @@ Classification configuration for inference.
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview]`xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview]`bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview``xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview``bert_ja`: Use for BERT-style models trained for the Japanese language.
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::::{dropdown} Properties of tokenization
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`bert`
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview] `xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview] `bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `bert_ja`: Use for BERT-style models trained for the Japanese language.
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::::{dropdown} Properties of tokenization
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`bert`
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview] `xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview] `bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `bert_ja`: Use for BERT-style models trained for the Japanese language.
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::::{dropdown} Properties of tokenization
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`bert`
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview] `xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview] `bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `bert_ja`: Use for BERT-style models trained for the Japanese language.
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::::{dropdown} Properties of tokenization
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`bert`
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview] `xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview] `bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `bert_ja`: Use for BERT-style models trained for the Japanese language.
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::::{dropdown} Properties of tokenization
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`bert`
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*`deberta_v2`: Use for DeBERTa v2 and v3-style models
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*`mpnet`: Use for MPNet-style models
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*`roberta`: Use for RoBERTa-style and BART-style models
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*[preview]`xlm_roberta`: Use for XLMRoBERTa-style models
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*[preview]`bert_ja`: Use for BERT-style models trained for the Japanese language.
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*{applies_to}`stack: preview` {applies_to}`serverless: preview``xlm_roberta`: Use for XLMRoBERTa-style models
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*{applies_to}`stack: preview` {applies_to}`serverless: preview``bert_ja`: Use for BERT-style models trained for the Japanese language.
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Refer to [Properties of `tokenizaton`](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-trained-model) to review the properties of the `tokenization` object.
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::::
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* `deberta_v2`: Use for DeBERTa v2 and v3-style models
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* `mpnet`: Use for MPNet-style models
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* `roberta`: Use for RoBERTa-style and BART-style models
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* [preview] `xlm_roberta`: Use for XLMRoBERTa-style models
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* [preview] `bert_ja`: Use for BERT-style models trained for the Japanese language.
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `xlm_roberta`: Use for XLMRoBERTa-style models
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* {applies_to}`stack: preview` {applies_to}`serverless: preview` `bert_ja`: Use for BERT-style models trained for the Japanese language.
Copy file name to clipboardExpand all lines: docs/reference/enrich-processor/user-agent-processor.md
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@@ -21,7 +21,7 @@ $$$ingest-user-agent-options$$$
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|`target_field`| no | user_agent | The field that will be filled with the user agent details. |
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|`regex_file`| no | - | The name of the file in the `config/ingest-user-agent` directory containing the regular expressions for parsing the user agent string. Both the directory and the file have to be created before starting Elasticsearch. If not specified, ingest-user-agent will use the regexes.yaml from uap-core it ships with (see below). |
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|`properties`| no |[`name`, `os`, `device`, `original`, `version`]| Controls what properties are added to `target_field`. |
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|`extract_device_type`| no |`false`|[beta] Extracts device type from the user agent string on a best-effort basis. |
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|`extract_device_type`| no |`false`|{applies_to}`stack: beta` {applies_to}`serverless: beta` Extracts device type from the user agent string on a best-effort basis. |
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|`ignore_missing`| no |`false`| If `true` and `field` does not exist, the processor quietly exits without modifying the document |
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Here is an example that adds the user agent details to the `user_agent` field based on the `agent` field:
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