code update

src/display/about.py

@@ -87,110 +87,6 @@ If everything is done, check you can launch your evaluation locally (you can add
 """
 
 
-CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
-CITATION_BUTTON_TEXT = r"""
-@article{KyrgyzLLM-Bench,
-  title={Bridging the Gap in Less-Resourced Languages: Building a Benchmark for Kyrgyz Language Models},
-  author={Timur Turatali, Aida Turdubaeva, Islam Zhenishbekov, Zhoomart Suranbaev, Anton Alekseev, Rustem Izmailov},
-  year={2025},
-  url={https://huggingface.co/datasets/TTimur/kyrgyzMMLU, 
-  https://huggingface.co/datasets/TTimur/kyrgyzRC, 
-  https://huggingface.co/datasets/TTimur/winogrande_kg, 
-  https://huggingface.co/datasets/TTimur/boolq_kg, 
-  https://huggingface.co/datasets/TTimur/truthfulqa_kg, 
-  https://huggingface.co/datasets/TTimur/gsm8k_kg, 
-  https://huggingface.co/datasets/TTimur/hellaswag_kg}
-}
-"""
-
-from dataclasses import dataclass
-from enum import Enum
-
-@dataclass
-class Task:
-    benchmark: str
-    metric: str
-    col_name: str
-
-
-# Init: to update with your specific keys
-class Tasks(Enum):
-    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
-    task0 = Task("KyrgyzMMLU", "metric_name", "KyrgyzMMLU")
-    task1 = Task("KyrgyzRC", "metric_name", "KyrgyzRC")
-    task2 = Task("WinoGrande", "metric_name", "WinoGrande")
-    task3 = Task("BoolQ", "metric_name", "BoolQ")
-    task4 = Task("HellaSwag", "metric_name", "HellaSwag")
-    task5 = Task("GSM8K", "metric_name", "GSM8K")
-    task6 = Task("TruthfulQA", "metric_name", "TruthfulQA")
-    
-
-# Your leaderboard name
-TITLE = """<h1 align="center" id="space-title"> OpenLLM Kyrgyz Leaderboard v0.1</h1>"""
-
-# What does your leaderboard evaluate?
-INTRODUCTION_TEXT = """
-Welcome to the Kyrgyz LLM Leaderboard — a dedicated platform for evaluating Large Language Models on Kyrgyz benchmarks. This space highlights models that perform well in Kyrgyz and helps advance research and tooling for low-resource languages.
-
-Benchmarks include native Kyrgyz tasks (KyrgyzMMLU, KyrgyzRC) and carefully translated sets (WinoGrande, BoolQ, HellaSwag, GSM8K, TruthfulQA). Scores are comparable across models and settings.
-
-🚀 Submit Your Model 🚀
-
-Have a Kyrgyz-capable model? Submit it for evaluation (currently manual) and help build a stronger Kyrgyz NLP ecosystem. See the About tab for details.
-"""
-
-# Which evaluations are you running? how can people reproduce what you have?
-LLM_BENCHMARKS_TEXT = f"""
-## How it works
-
-## Reproducibility
-
-This leaderboard aggregates results from Kyrgyz benchmarks. Datasets are hosted on the Hugging Face Hub under `TTimur`:
-- KyrgyzMMLU: `TTimur/kyrgyzMMLU`
-- KyrgyzRC: `TTimur/kyrgyzRC`
-- WinoGrande (KY): `TTimur/winogrande_kg`
-- BoolQ (KY): `TTimur/boolq_kg`
-- HellaSwag (KY): `TTimur/hellaswag_kg`
-- GSM8K (KY): `TTimur/gsm8k_kg`
-- TruthfulQA (KY): `TTimur/truthfulqa_kg`
-
-You can evaluate using your preferred evaluation harness (e.g., Lighteval or EleutherAI's lm-evaluation-harness) with Kyrgyz tasks enabled and then upload the resulting JSON to the results dataset for this Space.
-
-Notes:
-- Metrics reported are accuracy (or QEM for GSM8K), aligned with the dataset conventions.
-- Ensure you use consistent few-shot settings when comparing models.
-"""
-
-EVALUATION_QUEUE_TEXT = """
-## Some good practices before submitting a model
-
-### 1) Make sure you can load your model and tokenizer using AutoClasses:
-```python
-from transformers import AutoConfig, AutoModel, AutoTokenizer
-config = AutoConfig.from_pretrained("your model name", revision=revision)
-model = AutoModel.from_pretrained("your model name", revision=revision)
-tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
-```
-If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
-
-Note: make sure your model is public!
-Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
-
-### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
-It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
-
-### 3) Make sure your model has an open license!
-This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
-
-### 4) Fill up your model card
-When we add extra information about models to the leaderboard, it will be automatically taken from the model card
-
-## In case of model failure
-If your model is displayed in the `FAILED` category, its execution stopped.
-Make sure you have followed the above steps first.
-If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
-"""
-
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
 CITATION_BUTTON_TEXT = r"""
 @article{KyrgyzLLM-Bench,

src/display/utils.py

@@ -29,7 +29,7 @@ auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "ma
 #Scores
 auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
 for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
+    auto_eval_column_dict.append([task.value.col_name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Model information
 auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
 auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])