The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/qa_evaluation/Agentic_v3_HybridProtected/examples/[]/judge/key_point_coverage) changed from number to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
pretty_name: AI Muon Collider RAG language:
- en tags:
- particle-physics
- muon-collider
- rag
- retrieval-augmented-generation
- high-energy-physics ---# QA result to paper outputs
Copy both scripts into:
/eos/home-r/rjiang/RAG_muon_collider/
Then run:
chmod +x run_qa20_and_make_outputs.sh
./run_qa20_and_make_outputs.sh data/eval/queries_eval_v4_template.json 20
Or manually:
python evaluate_agentic_v3.py \
--query_file data/eval/queries_eval_v4_template.json \
--dense_weight 0.9 \
--bm25_weight 0.1 \
--judge_answers \
--qa_systems hybrid,agentic_v3,oracle \
--max_queries 20 \
--output data/eval/eval_results_agentic_v3_qa20.json
python make_qa_paper_outputs.py \
--input data/eval/eval_results_agentic_v3_qa20.json \
--outdir paper_outputs_qa20
Outputs:
- paper_outputs_qa20/figures/*.png
- paper_outputs_qa20/tables_qa_results.tex
- paper_outputs_qa20/figures_include.tex
- paper_outputs_qa20/section_results_discussion.tex
Put the figures into Overleaf figures/, then paste the LaTeX tables and text into your paper.
AI Muon Collider RAG
A retrieval-augmented generation framework and evaluation dataset for literature related to muon collider physics.
Repository structure
main_pdf/
PDF documents used as the primary source corpus for the RAG system.
main_markdown/
Markdown/text representations extracted from the source PDFs. These files are used as machine-readable inputs for document chunking and retrieval.
data/index/
Pre-built retrieval resources used by the RAG system, including the vector/dense index, document chunks, and BM25-related files.
data/eval/
Evaluation datasets and outputs used to benchmark the retrieval and question-answering performance of the system.
RAG pipeline
The overall workflow is approximately:
PDF literature → text/Markdown extraction → document chunking → dense + sparse indexing → retrieval → LLM-based question answering → evaluation
Usage
The repository contains scripts for building the retrieval index, running the RAG chatbot, and evaluating its performance.
See the individual Python scripts for the current implementation.
Disclaimer
This repository is intended for research and development purposes. Source publications retain their respective copyrights and licenses.
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