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Signed-off-by: thxCode <[email protected]>

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+tags:
+- reranker
+- cross-encoder
+- transformers.js
+pipeline_tag: text-classification
+---
+
+# jina-reranker-v1-tiny-en-GGUF
+
+**Model creator**: [Jina AI](https://huggingface.co/jinaai)
+**Original model**: [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en)
+**GGUF quantization**: based on llama.cpp release [f4d2b](https://github.com/ggerganov/llama.cpp/commit/f4d2b8846a6b34419ff9e9491aee6cd95e444bfc)
+
+---
+
+<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-tiny-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-tiny-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-tiny-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-tiny-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-tiny-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 tiny sized model
+model = CrossEncoder("jinaai/jina-reranker-v1-tiny-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-tiny-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-tiny-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-tiny-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`                | **49.60**                  | **69.21**                 | **85.13**                 |
+| `jina-reranker-v1-tiny-en` (you are here)  | **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.

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