chore: init readme

README.md

@@ -20,7 +20,7 @@ tags:
 
 # 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.
+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.
 
@@ -32,9 +32,11 @@ Here's a breakdown of the reranker models we provide:
 | [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                  |
 
+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
 
-You can use Jina Reranker models directly from transformers package:
+You can use `jina-reranker-v1-turbo-en` directly from transformers package:
 
 ```python
 !pip install transformers
@@ -65,6 +67,26 @@ 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 |
+
+For more details, please refer to our [benchmarking page](https://jina.ai/reranker/).
+
+> `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.
+
 # Contact
 
 Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas.