chore: init readme

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+  - en
+tags:
+  - reranker
+  - cross-encoder
+---
+
+<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
+
+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
+}'
+```
+
+Alternatively, you can 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)
+```
+
+# 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-rereanker-v1-base-en`  | **52.45**                  | **87.31**                 | **85.53**                 |
+| `jina-rereanker-v1-turbo-en` | **49.60**                  | **69.21**                 | **85.13**                 |
+| `jina-rereanker-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 page](https://jina.ai/reranker/).
+
+# Contact
+
+Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas.