Environment:OpenBMB UltraFeedback HuggingFace Hub Environment
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, Data_Management |
| Last Updated | 2026-02-08 06:00 GMT |
Overview
Python environment with HuggingFace `datasets` library for loading and saving datasets from the HuggingFace Hub.
Description
This environment provides access to the HuggingFace Hub for dataset operations. The score correction pipeline in fix_overall_score_issue.py loads the published UltraFeedback dataset from the Hub via `load_dataset("openbmb/UltraFeedback")` and saves corrected results back to disk via `dataset.save_to_disk()`. The completion generation and annotation pipelines use local JSON files but still require the `datasets` library for `Dataset.from_pandas()` operations. The `pandas` library is also required throughout for DataFrame manipulation.
Usage
Use this environment for any workflow that needs to load published datasets from HuggingFace Hub or convert between pandas DataFrames and HuggingFace Datasets. It is a prerequisite for Annotation_Data_Loading, Instruction_Data_Loading, and Score_Correction_Pipeline implementations.
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| OS | Any (Linux, macOS, Windows) | No OS-specific requirements |
| Hardware | No GPU required | Data loading is CPU-only |
| Network | Internet access to huggingface.co | Required for `load_dataset()` from Hub; local JSON loading works offline |
| Disk | 10GB+ | UltraFeedback dataset is several GB when downloaded |
Dependencies
Python Packages
- `datasets` (HuggingFace datasets library)
- `pandas`
- `json` (stdlib)
Credentials
- `HF_TOKEN`: HuggingFace API token. Required only if accessing gated or private datasets. The public `openbmb/UltraFeedback` dataset does not require authentication.
Quick Install
pip install datasets pandas
Code Evidence
HuggingFace Hub dataset loading from `fix_overall_score_issue.py:110`:
dataset = load_dataset("openbmb/UltraFeedback")["train"]
Dataset save to disk from `fix_overall_score_issue.py:115`:
dataset.save_to_disk("UltraFeedback")
Local JSON to Dataset conversion from `main.py:247-248`:
dataset = json.load(open(load_path))
dataset = datasets.Dataset.from_pandas(pd.DataFrame(dataset)).select(range(id*2000, min((id+1)*2000, len(dataset))))
Pandas JSON loading from `sampling.py:20-21`:
dataset = pd.read_json(f"./completion_data/{subset}.json", lines=True)
dataset = Dataset.from_pandas(pd.DataFrame(dataset))
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `ConnectionError: Couldn't reach huggingface.co` | No internet access | Ensure network connectivity or use cached datasets |
| `FileNotFoundError: completion_data/...json` | Missing local data files | Ensure completion_data directory exists with subset JSON files |
| `ValueError: Mismatch between DataFrame columns` | Inconsistent JSON structure | Verify all JSON entries have the same schema |
Compatibility Notes
- Local vs Hub loading: Completion generation uses local JSON files (`./completion_data/{subset}.json`); the score correction pipeline loads from HuggingFace Hub.
- Dataset.from_pandas(): Used throughout for converting JSON-loaded data into HuggingFace Dataset objects for `.map()` operations.
- Data formats: sampling.py uses `pd.read_json(lines=True)` (JSONL format) while main.py uses `json.load()` (standard JSON array).