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Testing markdown changes.
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Trent Fowler authored and Trent Fowler committed Sep 2, 2024
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Expand Up @@ -70,13 +70,13 @@ In this table, we provide some important context for using Cohere Command models
These models can be used to generate embeddings from text or classify it based on various parameters. Embeddings can be used for estimating semantic similarity between two sentences, choosing a sentence which is most likely to follow another sentence, or categorizing user feedback, while outputs from the Classify endpoint can be used for any classification or analysis task. The Representation model comes with a variety of helper functions, such as for detecting the language of an input.


| Model Name | Description | Modalities | Dimensions | Context Length | Similarity Metric | Endpoints |
|-------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|----------------|---------------------|-------------------------------------------------------------------------------------------------------------|
| Model Name | Description | Modalities | Dimensions | Context Length | Similarity Metric | Endpoints |
|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|------------|----------------|---------------------|-------------------------------------------------------------------------------------------------------------|
| `embed-english-v3.0` | A model that allows for text to be classified or turned into embeddings. English only. | Text, Images | 1024 | 512 | Cosine Similarity | [Embed](/reference/embed), <br/>[Embed Jobs](/reference/embed-jobs) |
| `embed-english-light-v3.0` | A smaller, faster version of `embed-english-v3.0`. Almost as capable, but a lot faster. English only. | Text, Images | 384 | 512 | Cosine Similarity | [Embed](/reference/embed), <br/>[Embed Jobs](/reference/embed-jobs) |
| `embed-multilingual-v3.0` | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages) | Text, Images | 1024 | 512 | Cosine Similarity | [Embed](/reference/embed), [Embed Jobs](/reference/embed-jobs) |
| `embed-multilingual-light-v3.0` | A smaller, faster version of `embed-multilingual-v3.0`. Almost as capable, but a lot faster. Supports multiple languages. | Text, Images | 384 | 512 | Cosine Similarity | [Embed](/reference/embed), <br/>[Embed Jobs](/reference/embed-jobs) |
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| | | | | | | |
| `embed-english-v2.0` | Our older embeddings model that allows for text to be classified or turned into embeddings. English only | Text | 4096 | 512 | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
| `embed-english-light-v2.0` | A smaller, faster version of embed-english-v2.0. Almost as capable, but a lot faster. English only. | Text | 1024 | 512 | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
| `embed-multilingual-v2.0` | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages) | Text | 768 | 256 | Dot Product Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
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