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# Prebuilt Language Models
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Prebuilt language models have been trained towards more sophisticated tasks for both monolingual as well as multilingual scenarios, including intent prediction and entity extraction.
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Entity extraction is currently experimental and not yet readt for production use.
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Prebuilt language models have been trained towards more sophisticated tasks for both monolingual as well as multilingual scenarios, including intent prediction and entity extraction. Entity extraction is currently experimental and not yet ready for production use.
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The following prebuilt language models are now available in [versions repository][2].
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See the [References](#references) section for technical descriptions of the AI technology behind the models.
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See the [References](#references) section for technical descriptions of the AI technology behind the models .
This is a high quality EN-only base model for intent detection that strikes the balance between size,
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speed and predictive performance.
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It is a 6-layer pretrained [Transformer][7] model optimized for conversation.
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Its architecture is pretrained for example-based use ([KNN][3]),
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thus it can be used out of box. This is the default model used if none explicitly specified.
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This is a high quality EN-only base model for intent detection that strikes the balance between size, speed and predictive performance. It is a 6-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box. This is the default model used if none explicitly specified.
This is a high quality multilingual base model for intent detection. It's smaller and faster than its 12-layer alternative.
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It is a 6-layer pretrained pretrained [Transformer][7] model optimized for conversation.
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Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box. The model supports in total 100 languages (full list can be found at [XLMR Supported Languages][8]). 8 languages (EN, ES, DE, FR, IT, JA, PT, and ZH) are fine-tuned with additional data (performance can be found [here](#multilingual-intent-detection-models-evaluation)).
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This is a high quality multilingual base model for intent detection. It's smaller and faster than its 12-layer alternative. It is a 6-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box. The model supports in total 100 languages (full list can be found at [XLMR Supported Languages][8]). 8 languages (EN, ES, DE, FR, IT, JA, PT, and ZH) are fine-tuned with additional data (performance can be found [here](#multilingual-intent-detection-models-evaluation)).
This is a high quality EN-only base model for entity extraction. It is a 6-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
This is a fast and small EN-only base model for intent detection with sufficient prediction performance.
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We suggest using this model if speed and memory size is critical to your deployment environment,
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otherwise consider other options. It is a generic 3-layer pretrained
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[Transformer][7] model optimized for conversation.
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Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
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This is a fast and small EN-only base model for intent detection with sufficient prediction performance. We suggest using this model if speed and memory size is critical to your deployment environment, otherwise consider other options. It is a generic 3-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
This is a high quality EN-only base model for intent detection, but is larger and slower than other options.
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It is a 12-layer pretrained pretrained [Transformer][7] model optimized for conversation.
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Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
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This is a high quality EN-only base model for intent detection, but is larger and slower than other options. It is a 12-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
This is a high quality quantized EN-only base model for intent detection, and it is smaller and faster than other options. It is a 6-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box.
This is a high quality multilingual base model for intent detection.
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It is a 12-layer pretrained pretrained [Transformer][7] model optimized for conversation.
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This is a high quality multilingual base model for intent detection. It is a 12-layer pretrained [Transformer][7] model optimized for conversation.
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Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box. The model supports in total 100 languages (full list can be found at [XLMR Supported Languages][8]). 8 languages (EN, ES, DE, FR, IT, JA, PT, and ZH) are fine-tuned with additional data (performance can be found [here](#multilingual-intent-detection-models-evaluation)).
This is a high quality quantized multilingual base model for intent detection. It is a 6-layer pretrained [Transformer][7] model optimized for conversation. Its architecture is pretrained for example-based use ([KNN][3]), thus it can be used out of box. The model supports in total 100 languages (full list can be found at [XLMR Supported Languages][8]). 8 languages (EN, ES, DE, FR, IT, JA, PT, and ZH) are fine-tuned with additional data (performance can be found [here](#multilingual-intent-detection-models-evaluation)).
The following table shows how accurate is each model relative to provided training sample size using [Snips NLU][4] system, evaluated by **micro-average-accuracy**.
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- The following table shows how accurate is each model by training and testing on the same language, evaluated by **micro-average-accuracy** on an internal dataset.
- The following table shows how accurate is each model by training on **en-us** and testing on the different languages, evaluated by **micro-average-accuracy** on an internal dataset.
- The following table shows how accurate is each model relative to provided training sample size using [Snips NLU][4] system, evaluated by **macro-average-F1**.
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| Training samples per entity type | 10 | 20 | 50 | 100 | 200 |
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