The ML framework supports a variety of open-source pretrained models that can assist with a range of machine learning (ML) search and analytics use cases.
To use a pretrained model in your OpenSearch cluster:
1. Select the model you want to upload. For a list of pretrained models, see [supported pretrained models](#supported-pretrained-models).
2. Upload the model using the [upload API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework#upload-model-to-opensearch). Because a pretrained model originates from the ML Commons model repository, you only need to provide the `name`, `version`, and `model_format` in the upload API request.
"description": "This is a neural sparse encoding model: It transfers text into sparse vector, and then extract nonzero index and value to entry and weights. It serves only in ingestion and customer should use tokenizer model in query.",
You can find the `url` and `model_content_hash_value` in the model config link for each model. For more information, see the [Supported pretrained models section](#supported-pretrained-models). Set the `function_name` to `SPARSE_ENCODING` or `SPARSE_TOKENIZE`.
Note that the `function_name` parameter in the request corresponds to the `model_task_type` parameter in the model config. When using a pretrained model, make sure to change the name of the parameter from `model_task_type` to `function_name` in the model upload request.
For more information about how to upload and use ML models, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework/).
OpenSearch supports the following models, categorized by type. Text embedding models are sourced from [Hugging Face](https://huggingface.co/). Sparse encoding models are trained by OpenSearch. Although models with the same type will have similar use cases, each model has a different model size and will perform differently depending on your cluster setup. For a performance comparison of some pretrained models, see the [SBERT documentation](https://www.sbert.net/docs/pretrained_models.html#model-overview).
Sentence transformer models map sentences and paragraphs across a dimensional dense vector space. The number of vectors depends on the type of model. You can use these models for use cases such as clustering or semantic search.
The following table provides a list of sentence transformer models and artifact links you can use to download them. Note that you must prefix the model name with `huggingface/`, as shown in the **Model name** column. As of OpenSearch 2.6, all artifacts are set to version 1.0.1.
Sparse encoding models transfer text into a sparse vector and convert the vector to a list of `<token: weight>` pairs representing the text entry and its corresponding weight in the sparse vector. You can use these models for use cases such as clustering or sparse neural search.
| Model name | Auto-truncation | TorchScript artifact | Description |
|---|---|---|
| `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-v1-1.0.0-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `<entry, weight>` pairs, where each entry corresponds to a non-zero element index. |
| `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-doc-v1-1.0.0-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `<entry, weight>` pairs, where each entry corresponds to a non-zero element index. |
| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/opensearch-neural-sparse-tokenizer-v1-1.0.0.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/config.json) | A neural sparse tokenizer model. The model tokenizes text into tokens and assigns each token a predefined weight, which is the token's IDF (if the IDF file is not provided, the weight defaults to 1). For more information, see [Uploading your own model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/#uploading-your-own-model). |
