Upload README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- multiple-choice
+- question-answering
+tags:
+- telecommunications
+- 5G
+- network-analysis
+- root-cause-analysis
+pretty_name: TeleLogs (Processed MCQ Format)
+size_categories:
+- n<1K
+dataset_info:
+  features:
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: int64
+  - name: choices
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 5242880
+    num_examples: 864
+  download_size: 5242880
+  dataset_size: 5242880
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: telelogs_test.json
+---
+
+# TeleLogs Dataset (Processed MCQ Format)
+
+This dataset has been extracted from the original [netop/TeleLogs](https://huggingface.co/datasets/netop/TeleLogs) dataset and processed into multiple-choice question (MCQ) format for easier evaluation.
+
+## Dataset Description
+
+TeleLogs is a telecommunications log analysis benchmark where models must identify the root cause of network issues from 5G wireless network drive-test data and engineering parameters.
+
+### Processed Format
+
+This version has been restructured for MCQ evaluation with the following improvements:
+
+- **Clean separation** of question data, choices, and instructions
+- **0-based indexing** for answers (0-7 instead of 1-8)
+- **Extracted choices** as a proper array field
+- **Removed template text** from questions
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance contains:
+- `question`: The actual network data (drive-test logs, engineering parameters, tables)
+- `choices`: Array of 8 possible root causes
+- `answer`: Integer index (0-7) indicating the correct choice
+
+Example:
+```json
+{
+  "question": "Given:\n- The default electronic downtilt value is 255...\n\nUser plane drive test data as follows：\n\n<tables>...",
+  "choices": [
+    "The serving cell's downtilt angle is too large, causing weak coverage at the far end.",
+    "The serving cell's coverage distance exceeds 1km, resulting in over-shooting.",
+    ...
+  ],
+  "answer": 3
+}
+```
+
+### Data Fields
+
+- `question` (string): The network data and parameters to analyze. Typically starts with "Given:" and includes:
+  - Configuration parameters
+  - Drive test data tables
+  - Engineering parameters tables
+
+- `choices` (list of strings): Array of exactly 8 possible root causes
+
+- `answer` (integer): The correct answer index (0-7), where:
+  - 0 = First choice (originally C1)
+  - 1 = Second choice (originally C2)
+  - ...
+  - 7 = Eighth choice (originally C8)
+
+### Data Splits
+
+|     | test |
+|-----|------|
+| TeleLogs | 864 |
+
+## Transformations Applied
+
+This processed version applies three key transformations:
+
+### 1. Answer Column
+- **Original**: `C1`, `C2`, ..., `C8`
+- **Processed**: `0`, `1`, ..., `7`
+- Converted to 0-based indexing to match array positions
+
+### 2. Choices Column (New)
+- Extracted 8 choices cleanly from the original question text
+- Each choice stored as array element
+- For C8, stops at `\n\n` separator before actual question data
+
+### 3. Question Column
+- **Removed**: Template instructions (e.g., "Analyze the 5G wireless network...")
+- **Removed**: Choice listings (C1-C8 with their text)
+- **Kept**: Only the actual question data after `\n\n`
+- Typically starts with "Given:" and includes all data tables
+
+## Dataset Creation
+
+### Source Data
+
+Original dataset: [netop/TeleLogs](https://huggingface.co/datasets/netop/TeleLogs)
+
+### Processing Pipeline
+
+1. Load original TeleLogs dataset from HuggingFace
+2. Extract test split
+3. Parse and separate choices from question text using regex
+4. Convert answer format from C1-C8 to 0-7
+5. Remove instruction template and choice listings
+6. Export to multiple formats (CSV, JSON, Parquet)
+
+### Processing Scripts
+
+The processing scripts are available at: [Telecom-Bench](https://github.com/eaguaida/Telecom-Bench)
+
+## Usage
+
+### Loading with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("eaguaida/telelogs")
+```
+
+### Using with Inspect AI
+
+```python
+from inspect_ai import Task
+from inspect_ai.dataset import Sample
+from inspect_ai.scorer import choice
+from inspect_ai.solver import multiple_choice
+
+def telelogs_record_to_sample(record):
+    return Sample(
+        input=record["question"],
+        choices=record["choices"],
+        target=chr(65 + record["answer"]),  # Convert 0->A, 1->B, etc.
+    )
+
+# Load and evaluate
+dataset = load_dataset("eaguaida/telelogs", sample_fields=telelogs_record_to_sample)
+task = Task(dataset=dataset, solver=multiple_choice(), scorer=choice())
+```
+
+## File Formats
+
+The dataset is available in multiple formats:
+
+- **`telelogs_test.parquet`**: Parquet format (recommended, most efficient)
+- **`telelogs_test.json`**: JSON format (human-readable)
+- **`telelogs_test.csv`**: CSV format (note: choices stored as JSON string)
+
+## Licensing
+
+This dataset maintains the same license as the original TeleLogs dataset.
+
+## Citation
+
+If you use this dataset, please cite the original TeleLogs dataset:
+
+```bibtex
+@dataset{telelogs2024,
+  title={TeleLogs: Telecommunications Log Analysis Dataset},
+  author={Original Authors},
+  year={2024},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/netop/TeleLogs}
+}
+```
+
+## Contact
+
+For questions or issues with this processed version, please open an issue at [Telecom-Bench](https://github.com/eaguaida/Telecom-Bench).