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Add TextReranker documentation (#2500)
Ticket: 170852
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‎samples/cpp/rag/README.md‎

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@@ -12,12 +12,18 @@ Install [../../export-requirements.txt](../../export-requirements.txt) to conver
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pip install --upgrade-strategy eager -r ../../export-requirements.txt
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```
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Then, run the export with Optimum CLI:
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To export text embedding model run Optimum CLI command:
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```sh
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optimum-cli export openvino --trust-remote-code --model BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5
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```
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To export text reranking model run Optimum CLI command:
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```sh
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optimum-cli export openvino --trust-remote-code --model cross-encoder/ms-marco-MiniLM-L6-v2 cross-encoder/ms-marco-MiniLM-L6-v2
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```
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## Run
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‎samples/python/rag/README.md‎

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pip install --upgrade-strategy eager -r ../../export-requirements.txt
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```
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Then, run the export with Optimum CLI:
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To export text embedding model run Optimum CLI command:
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```sh
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optimum-cli export openvino --trust-remote-code --model BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5
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```
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To export text reranking model run Optimum CLI command:
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```sh
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optimum-cli export openvino --trust-remote-code --model cross-encoder/ms-marco-MiniLM-L6-v2 cross-encoder/ms-marco-MiniLM-L6-v2
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```
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Alternatively, do it in Python code:
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```python

‎site/docs/supported-models/_components/text-rerank-models-table/index.tsx‎

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import React from 'react';
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import { TEXT_RERANK_MODELS } from './models';
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import { BaseModelsTable, LinksCell } from '../base-models-table';
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export default function TextRerankModelsTable(): React.JSX.Element {
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const headers = ['Architecture', 'Example HuggingFace Models'];
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const rows = TEXT_RERANK_MODELS.map(({ architecture, models }) => (
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<>
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<tr key={architecture}>
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<td rowSpan={models.length}>
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<code>{architecture}</code>
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</td>
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<LinksCell links={models[0].links} />
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</tr>
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</>
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));
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return <BaseModelsTable headers={headers} rows={rows} />;
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}

‎site/docs/supported-models/_components/text-rerank-models-table/models.ts‎

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type TextRerankModelType = {
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architecture: string;
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models: Array<{
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links: string[];
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}>;
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};
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export const TEXT_RERANK_MODELS: TextRerankModelType[] = [
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{
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architecture: 'BertForSequenceClassification',
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models: [
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{
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links: [
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'https://huggingface.co/cross-encoder/ms-marco-MiniLM-L2-v2',
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'https://huggingface.co/cross-encoder/ms-marco-MiniLM-L4-v2',
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'https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2',
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'https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2',
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'https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L2-v2',
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'https://huggingface.co/tomaarsen/reranker-MiniLM-L12-gooaq-bce',
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],
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},
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],
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},
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{
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architecture: 'XLMRobertaForSequenceClassification',
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models: [
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{
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links: [
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'https://huggingface.co/BAAI/bge-reranker-v2-m3',
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'https://huggingface.co/BAAI/bge-reranker-base',
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],
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},
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],
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},
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{
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architecture: 'GemmaForCausalLM',
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models: [
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{
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links: ['https://huggingface.co/BAAI/bge-reranker-v2-gemma'],
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},
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],
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},
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{
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architecture: 'ModernBertForSequenceClassification',
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models: [
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{
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links: [
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'https://huggingface.co/tomaarsen/reranker-ModernBERT-base-gooaq-bce',
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'https://huggingface.co/tomaarsen/reranker-ModernBERT-large-gooaq-bce',
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'https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base',
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],
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},
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],
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},
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{
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architecture: 'ModernBertForMaskedLM',
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models: [
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{
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links: ['https://huggingface.co/answerdotai/ModernBERT-base'],
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},
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],
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},
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];

‎site/docs/supported-models/index.mdx‎

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import WhisperModelsTable from './_components/whisper-models-table';
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import TextEmbeddingsModelsTable from './_components/text-embeddings-models-table';
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import SpeechGenerationModelsTable from './_components/speech-generation-models-table';
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import TextRerankModelsTable from './_components/text-rerank-models-table';
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# Supported Models
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<SpeechGenerationModelsTable />
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## Text Rerank Models
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<TextRerankModelsTable />
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:::info
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LoRA adapters are not supported.
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:::
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:::info
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Some models may require access request submission on the Hugging Face page to be downloaded.

‎site/docs/use-cases/text-rerank/_sections/_run_model/_code_example_cpp.mdx‎

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import CodeBlock from '@theme/CodeBlock';
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<CodeBlock language="cpp" showLineNumbers>
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{`#include "openvino/genai/rag/text_rerank_pipeline.hpp"
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int main(int argc, char* argv[]) {
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std::vector<std::string> documents(argv + 3, argv + argc);
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std::string models_path = argv[1], query = argv[2];
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ov::genai::TextRerankPipeline pipeline(models_path, "${props.device || 'CPU'}", ov::genai::top_n(3));
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auto rerank_result = pipeline.rerank(query, documents);
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std::cout << "Reranked documents:\\n";
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for (const auto& [index, score] : rerank_result) {
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std::cout << "Document " << index << " (score: " << score << "): " << documents[index] << '\\n';
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}
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}
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`}
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</CodeBlock>

‎site/docs/use-cases/text-rerank/_sections/_run_model/_code_example_python.mdx‎

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import CodeBlock from '@theme/CodeBlock';
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<CodeBlock language="python" showLineNumbers>
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{`import openvino_genai
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pipeline = openvino_genai.TextRerankPipeline(model_path, "${props.device || 'CPU'}", top_n=3)
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rerank_result = pipeline.rerank(query, documents)
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print("Reranked documents:")
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for index, score in rerank_result:
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print(f"Document {index} (score: {score:.4f}): {documents[index]}")
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`}
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</CodeBlock>

‎site/docs/use-cases/text-rerank/_sections/_run_model/index.mdx‎

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import CodeExampleCPP from './_code_example_cpp.mdx';
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import CodeExamplePython from './_code_example_python.mdx';
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## Run Model Using OpenVINO GenAI
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The `TextRerankPipeline` enables you to reorder candidate documents or passages by semantic relevance to a query using a cross-encoder or reranker model. You can control how many top results are returned using the `top_n` parameter.
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<LanguageTabs>
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<TabItemPython>
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<Tabs groupId="device">
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<TabItem label="CPU" value="cpu">
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<CodeExamplePython device="CPU" />
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</TabItem>
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<TabItem label="GPU" value="gpu">
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<CodeExamplePython device="GPU" />
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</TabItem>
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</Tabs>
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</TabItemPython>
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<TabItemCpp>
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<Tabs groupId="device">
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<TabItem label="CPU" value="cpu">
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<CodeExampleCPP device="CPU" />
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</TabItem>
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<TabItem label="GPU" value="gpu">
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<CodeExampleCPP device="GPU" />
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</TabItem>
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</Tabs>
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</TabItemCpp>
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</LanguageTabs>

‎site/docs/use-cases/text-rerank/index.mdx‎

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---
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sidebar_position: 2
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---
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import OptimumCLI from '@site/src/components/OptimumCLI';
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import ConvertModelSection from '../_shared/_convert_model.mdx';
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import RunModelSection from './_sections/_run_model/index.mdx';
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# Text Reranking
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<ConvertModelSection>
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Download and convert a reranker model (e.g. [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2)) to OpenVINO format from Hugging Face:
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<OptimumCLI model='cross-encoder/ms-marco-MiniLM-L6-v2' outputDir='cross-encoder/ms-marco-MiniLM-L6-v2' trustRemoteCode />
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See all supported [Reranker Models](/docs/supported-models/#text-rerank-models).
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</ConvertModelSection>
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<RunModelSection />

‎site/src/pages/_sections/UseCasesSection/components/text-rerank.tsx‎

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import Button from '@site/src/components/Button';
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import { LanguageTabs, TabItemCpp, TabItemPython } from '@site/src/components/LanguageTabs';
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import UseCaseCard from './UseCaseCard';
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import CodeExampleCpp from '@site/docs/use-cases/text-rerank/_sections/_run_model/_code_example_cpp.mdx';
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import CodeExamplePython from '@site/docs/use-cases/text-rerank/_sections/_run_model/_code_example_python.mdx';
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export const TextRerank = () => (
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<UseCaseCard>
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<UseCaseCard.Title>Text Rerank for RAG</UseCaseCard.Title>
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<UseCaseCard.Description>
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Boost the relevance and accuracy of your Retrieval-Augmented Generation (RAG) workflows by
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reranking retrieved documents with the TextRerankPipeline.
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</UseCaseCard.Description>
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<UseCaseCard.Features>
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<li>Reorder search results by semantic relevance to the query</li>
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</UseCaseCard.Features>
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<UseCaseCard.Code>
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<LanguageTabs>
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<TabItemPython>
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<CodeExamplePython />
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</TabItemPython>
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<TabItemCpp>
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<CodeExampleCpp />
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</TabItemCpp>
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</LanguageTabs>
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</UseCaseCard.Code>
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<UseCaseCard.Actions>
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<Button label="Explore Use Case" link="docs/use-cases/text-rerank" variant="primary" />
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<Button label="View Code Samples" link="docs/samples" variant="primary" outline />
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</UseCaseCard.Actions>
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</UseCaseCard>
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);

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