opensearch-docs-cn/_ml-commons-plugin/pretrained-models.md

layout	title	parent	nav_order
default	Pretrained models	Using custom models within OpenSearch	120

Pretrained models are generally available in OpenSearch 2.9 and later. Sparse encoding models are generally available in OpenSearch 2.11 and later. {: .note}

Pretrained models

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.

Uploading pretrained models

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.
2. Upload the model using the upload API. 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.

POST /_plugins/_ml/models/_upload
{
  "name": "huggingface/sentence-transformers/all-MiniLM-L12-v2",
  "version": "1.0.1",
  "model_format": "TORCH_SCRIPT"
}

Note that for sparse encoding models, you still need to upload the full request body, as shown in the following example:

POST /_plugins/_ml/models/_upload
{
    "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1",
    "version": "1.0.0",
    "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.",
    "model_format": "TORCH_SCRIPT",
    "function_name": "SPARSE_ENCODING",
    "model_content_hash_value": "9a41adb6c13cf49a7e3eff91aef62ed5035487a6eca99c996156d25be2800a9a",
    "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"
}

{% include copy-curl.html %}

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. 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. {: .important}

For more information about how to upload and use ML models, see Using custom models within OpenSearch.

Supported pretrained models

OpenSearch supports the following models, categorized by type. Text embedding models are sourced from Hugging Face. 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.

Sentence transformers

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.

| Model name | Vector dimensions | Auto-truncation | TorchScript artifact | ONNX artifact | |---|---|---|---| | huggingface/sentence-transformers/all-distilroberta-v1 | 768-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-MiniLM-L6-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/all-mpnet-base-v2 | 768-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/msmarco-distilbert-base-tas-b | 768-dimensional dense vector space. Optimized for semantic search. | No | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | 384-dimensional dense vector space. Designed for semantic search and trained on 215 million question/answer pairs. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url | | huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | 384-dimensional dense vector space. | Yes | - model_url
- config_url | - model_url
- config_url |

Sparse encoding models

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.

We recommend the following models for optimal performance:

• Use the amazon/neural-sparse/opensearch-neural-sparse-encoding-v1 model during both ingestion and search.
• Use the amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1 model during ingestion and the amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1 model during search.

The following table provides a list of sparse encoding models and artifact links you can use to download them.

| Model name | Auto-truncation | TorchScript artifact | Description | |---|---|---| | amazon/neural-sparse/opensearch-neural-sparse-encoding-v1 | Yes | - model_url
- config_url | 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
- config_url | 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
- config_url | 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. |