Add transformers.js example code (#6)

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	---
	library_name: transformers
	license: apache-2.0
	language:
	- en
	tags:
	- reranker
	- cross-encoder
	- transformers.js
	---

	<br><br>

	<p align="center">
	<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
	</p>

	<p align="center">
	<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
	</p>

	# jina-reranker-v1-turbo-en

	This model is designed for blazing-fast reranking while maintaining competitive performance. What's more, it leverages the power of our [JinaBERT](https://arxiv.org/abs/2310.19923) model as its foundation. `JinaBERT` itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409). This allows `jina-reranker-v1-turbo-en` to process significantly longer sequences of text compared to other reranking models, up to an impressive 8,192 tokens.

	To achieve the remarkable speed, the `jina-reranker-v1-turbo-en` employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original [jina-reranker-v1-base-en](https://jina.ai/reranker/)) acts as a teacher, condensing its knowledge into a smaller, faster student model. This student retains most of the teacher's knowledge, allowing it to deliver similar accuracy in a fraction of the time.

	Here's a breakdown of the reranker models we provide:

	\| Model Name \| Layers \| Hidden Size \| Parameters (Millions) \|
	\| ------------------------------------------------------------------------------------ \| ------ \| ----------- \| --------------------- \|
	\| [jina-reranker-v1-base-en](https://jina.ai/reranker/) \| 12 \| 768 \| 137.0 \|
	\| [jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) \| 6 \| 384 \| 37.8 \|
	\| [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en) \| 4 \| 384 \| 33.0 \|

	> Currently, the `jina-reranker-v1-base-en` model is not available on Hugging Face. You can access it via the [Jina AI Reranker API](https://jina.ai/reranker/).

	As you can see, the `jina-reranker-v1-turbo-en` offers a balanced approach with 6 layers and 37.8 million parameters. This translates to fast search and reranking while preserving a high degree of accuracy. The `jina-reranker-v1-tiny-en` prioritizes speed even further, achieving the fastest inference speeds with its 4-layer, 33.0 million parameter architecture. This makes it ideal for scenarios where absolute top accuracy is less crucial.

	# Usage

	1. The easiest way to starting using `jina-reranker-v1-turbo-en` is to use Jina AI's [Reranker API](https://jina.ai/reranker/).

	```bash
	curl https://api.jina.ai/v1/rerank \
	-H "Content-Type: application/json" \
	-H "Authorization: Bearer YOUR_API_KEY" \
	-d '{
	"model": "jina-reranker-v1-turbo-en",
	"query": "Organic skincare products for sensitive skin",
	"documents": [
	"Eco-friendly kitchenware for modern homes",
	"Biodegradable cleaning supplies for eco-conscious consumers",
	"Organic cotton baby clothes for sensitive skin",
	"Natural organic skincare range for sensitive skin",
	"Tech gadgets for smart homes: 2024 edition",
	"Sustainable gardening tools and compost solutions",
	"Sensitive skin-friendly facial cleansers and toners",
	"Organic food wraps and storage solutions",
	"All-natural pet food for dogs with allergies",
	"Yoga mats made from recycled materials"
	],
	"top_n": 3
	}'
	```

	2. Alternatively, you can use the latest version of the `sentence-transformers>=0.27.0` library. You can install it via pip:

	```bash
	pip install -U sentence-transformers
	```

	Then, you can use the following code to interact with the model:

	```python
	from sentence_transformers import CrossEncoder

	# Load the model, here we use our turbo sized model
	model = CrossEncoder("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True)

	# Example query and documents
	query = "Organic skincare products for sensitive skin"
	documents = [
	"Eco-friendly kitchenware for modern homes",
	"Biodegradable cleaning supplies for eco-conscious consumers",
	"Organic cotton baby clothes for sensitive skin",
	"Natural organic skincare range for sensitive skin",
	"Tech gadgets for smart homes: 2024 edition",
	"Sustainable gardening tools and compost solutions",
	"Sensitive skin-friendly facial cleansers and toners",
	"Organic food wraps and storage solutions",
	"All-natural pet food for dogs with allergies",
	"Yoga mats made from recycled materials"
	]

	results = model.rank(query, documents, return_documents=True, top_k=3)
	```

	3. You can also use the `transformers` library to interact with the model programmatically.

	```python
	!pip install transformers
	from transformers import AutoModelForSequenceClassification

	model = AutoModelForSequenceClassification.from_pretrained(
	'jinaai/jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True
	)

	# Example query and documents
	query = "Organic skincare products for sensitive skin"
	documents = [
	"Eco-friendly kitchenware for modern homes",
	"Biodegradable cleaning supplies for eco-conscious consumers",
	"Organic cotton baby clothes for sensitive skin",
	"Natural organic skincare range for sensitive skin",
	"Tech gadgets for smart homes: 2024 edition",
	"Sustainable gardening tools and compost solutions",
	"Sensitive skin-friendly facial cleansers and toners",
	"Organic food wraps and storage solutions",
	"All-natural pet food for dogs with allergies",
	"Yoga mats made from recycled materials"
	]

	# construct sentence pairs
	sentence_pairs = [[query, doc] for doc in documents]

	scores = model.compute_score(sentence_pairs)
	```

	4. You can also use the `transformers.js` library to run the model directly in JavaScript (in-browser, Node.js, Deno, etc.)!

	If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
	```bash
	npm i @xenova/transformers
	```

	Then, you can use the following code to interact with the model:
	```js
	import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';

	const model_id = 'jinaai/jina-reranker-v1-turbo-en';
	const model = await AutoModelForSequenceClassification.from_pretrained(model_id, { quantized: false });
	const tokenizer = await AutoTokenizer.from_pretrained(model_id);

	/**
	* Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores.
	* @param {string} query A single query
	* @param {string[]} documents A list of documents
	* @param {Object} options Options for ranking
	* @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned.
	* @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores.
	*/
	async function rank(query, documents, {
	top_k = undefined,
	return_documents = false,
	} = {}) {
	const inputs = tokenizer(
	new Array(documents.length).fill(query),
	{ text_pair: documents, padding: true, truncation: true }
	)
	const { logits } = await model(inputs);
	return logits.sigmoid().tolist()
	.map(([score], i) => ({
	corpus_id: i,
	score,
	...(return_documents ? { text: documents[i] } : {})
	})).sort((a, b) => b.score - a.score).slice(0, top_k);
	}

	// Example usage:
	const query = "Organic skincare products for sensitive skin"
	const documents = [
	"Eco-friendly kitchenware for modern homes",
	"Biodegradable cleaning supplies for eco-conscious consumers",
	"Organic cotton baby clothes for sensitive skin",
	"Natural organic skincare range for sensitive skin",
	"Tech gadgets for smart homes: 2024 edition",
	"Sustainable gardening tools and compost solutions",
	"Sensitive skin-friendly facial cleansers and toners",
	"Organic food wraps and storage solutions",
	"All-natural pet food for dogs with allergies",
	"Yoga mats made from recycled materials",
	]

	const results = await rank(query, documents, { return_documents: true, top_k: 3 });
	console.log(results);
	```

	That's it! You can now use the `jina-reranker-v1-turbo-en` model in your projects.

	# Evaluation

	We evaluated Jina Reranker on 3 key benchmarks to ensure top-tier performance and search relevance.

	\| Model Name \| NDCG@10 (17 BEIR datasets) \| NDCG@10 (5 LoCo datasets) \| Hit Rate (LlamaIndex RAG) \|
	\| ------------------------------------------- \| -------------------------- \| ------------------------- \| ------------------------- \|
	\| `jina-reranker-v1-base-en` \| 52.45 \| 87.31 \| 85.53 \|
	\| `jina-reranker-v1-turbo-en` (you are here) \| 49.60 \| 69.21 \| 85.13 \|
	\| `jina-reranker-v1-tiny-en` \| 48.54 \| 70.29 \| 85.00 \|
	\| `mxbai-rerank-base-v1` \| 49.19 \| - \| 82.50 \|
	\| `mxbai-rerank-xsmall-v1` \| 48.80 \| - \| 83.69 \|
	\| `ms-marco-MiniLM-L-6-v2` \| 48.64 \| - \| 82.63 \|
	\| `ms-marco-MiniLM-L-4-v2` \| 47.81 \| - \| 83.82 \|
	\| `bge-reranker-base` \| 47.89 \| - \| 83.03 \|

	Note:

	- `NDCG@10` is a measure of ranking quality, with higher scores indicating better search results. `Hit Rate` measures the percentage of relevant documents that appear in the top 10 search results.
	- The results of LoCo datasets on other models are not available since they do not support long documents more than 512 tokens.

	For more details, please refer to our [benchmarking sheets](https://docs.google.com/spreadsheets/d/1V8pZjENdBBqrKMzZzOWc2aL60wtnR0yrEBY3urfO5P4/edit?usp=sharing).

	# Contact

	Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas.