README.md

---
license: apache-2.0
language:
- ru
- en
base_model:
- jinaai/jina-embeddings-v3
---

## **JinaJudge: Proxy Judgement for Russian LLM Arena**

### **Description**
This model is trained to replicate the judgement patterns of GPT-4-1106-Preview in the [Russian LLM Arena](https://huggingface.co/spaces/Vikhrmodels/arenahardlb), designed for faster and more cost-effective evaluation of language models. While the model's focus is on Russian LLM evaluation, it can also be used for English-centric models.

---

### **Model Details**

This is an iterative update of [kaleinaNyan/jina-v3-rullmarena-judge-300924](https://huggingface.co/kaleinaNyan/jina-v3-rullmarena-judge-300924) model:
- Increased amount of training data (not by much, approaximately 1.5x times).
- Updated data composition to fix erroneous judgements where GPT-4 picked English responses over Russian ones.
- Validation set was updated as well to exclude such errors.
- Test set did not change (no bad judgements in that regard).

---

### **Evaluation**
The validation process was based on **existing judgements** from the Russian LLM Arena, which were already available. These judgements were filtered and simplified to match the three-class structure used in training.

NOTE: values in parenthesis show relative improvement compared to previous model.

**Models evaluated**:
- **gemma-2-9b-it-sppo-iter3**
- **glm-4-9b-chat**
- **gpt-3.5-turbo-1106**
- **mistral-7b-instruct-v0.3**
- **storm-7b**

**Validation Performance (old validation set)**:
- **Accuracy**: 79.97% (-0.78)
- **Precision**: 78.25% (-0.31)
- **Recall**: 78.25% (-1.23)
- **F1-score**: 78.25% (-0.75)

NOTE: will report later what actually caused the drop (the subset of fixed judgements or smth else)

**Validation Performance (new validation set)**:
- **Accuracy**: 83.59% (+2.48)
- **Precision**: 80.97% (+2.14)
- **Recall**: 80.97% (+1.22)
- **F1-score**: 80.97% (+1.77)

For the **test** phase, new judgements were generated using GPT-4 for the `kolibri-mistral-0427-upd` model.

**Test Performance**:
- **Accuracy**: 85.09% (+2.37)
- **Precision**: 83.20% (+3.09)
- **Recall**: 83.20% (+0.78)
- **F1-score**: 83.20% (+2.02)

---

### **Usage Example**

```python
from transformers import AutoModel

jina = AutoModel.from_pretrained("kaleinaNyan/jina-v3-rullmarena-judge-041024", trust_remote_code=True)

prompt_template = """
<user prompt>
{user_prompt}
<end>
<assistant A answer>
{assistant_a}
<end>
<assistant B answer>
{assistant_b}
<end>
""".strip()

prompt = "your prompt"
assistant_a = "assistant a response"
assistant_b = "assistant b response"

example = prompt_template.format(
    user_prompt=user_prompt,
    assistant_a=assistant_a,
    assistant_b=assistant_b,
)

judgement = jina([example])[0].argmax()

judgement_map = {
  0: "A is better than B",
  1: "A == B",
  2: "B is better than A"
}

print(judgement_map[judgement])
```