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-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 their 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                  |
+
+# Usage
+
+You can use Jina Reranker models directly from transformers package:
+
+```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)
+```
+
+# Contact
+
+Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas.