Update README.md

README.md

@@ -1,199 +1,101 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- ru
+- en
+base_model:
+- jinaai/jina-embeddings-v3
 ---
 
-# Model Card for Model ID
+## **JinaJudge: Proxy Judgement for Russian LLM Arena**
 
-<!-- Provide a quick summary of what the model is/does. -->
+### **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**
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+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).
 
-**APA:**
+---
 
-[More Information Needed]
+### **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.
 
-## Glossary [optional]
+NOTE: values in parenthesis show relative improvement compared to previous model.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+**Models evaluated**:
+- **gemma-2-9b-it-sppo-iter3**
+- **glm-4-9b-chat**
+- **gpt-3.5-turbo-1106**
+- **mistral-7b-instruct-v0.3**
+- **storm-7b**
 
-[More Information Needed]
+**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)
 
-## More Information [optional]
+NOTE: will report later what actually caused the drop (the subset of fixed judgements or smth else)
 
-[More Information Needed]
+**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)
 
-## Model Card Authors [optional]
+For the **test** phase, new judgements were generated using GPT-4 for the `kolibri-mistral-0427-upd` model.
 
-[More Information Needed]
+**Test Performance**:
+- **Accuracy**: 85.09% (+2.37)
+- **Precision**: 83.20% (+3.09)
+- **Recall**: 83.20% (+0.78)
+- **F1-score**: 83.20% (+2.02)
 
-## Model Card Contact
+---
 
-[More Information Needed]
+### **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])
+```