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Datasets:
OLAResearchX
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MemoryATHENA

Languages:
English
Tags:
experiment-metadata
question-answering
text-classification
memory-augmented-language-model
License:
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xet
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
task_family: string
task: string
condition: string
split: string
metric_schema: string
metrics_percent: struct<EM: double, F1: double, MC1: double, MC2: double, MC3: double, mean: double, accuracy: double (... 1 chars omitted)
  child 0, EM: double
  child 1, F1: double
  child 2, MC1: double
  child 3, MC2: double
  child 4, MC3: double
  child 5, mean: double
  child 6, accuracy: double
model_repo: string
notes: string
collection: string
model_repositories: list<item: string>
  child 0, item: string
test_data: struct<qa: struct<NQ: string, WebQA: string, TriviaQA: string, TruthfulQA: string, HotpotQA: string> (... 292 chars omitted)
  child 0, qa: struct<NQ: string, WebQA: string, TriviaQA: string, TruthfulQA: string, HotpotQA: string>
      child 0, NQ: string
      child 1, WebQA: string
      child 2, TriviaQA: string
      child 3, TruthfulQA: string
      child 4, HotpotQA: string
  child 1, general_nlp: struct<SST2: struct<split: string, examples: int64>, MR: struct<split: string, examples: int64>, CR: (... 176 chars omitted)
      child 0, SST2: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
      child 1, MR: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
      child 2, CR: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
      child 3, RT: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
      child 4, AGN: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
      child 5, Yahoo: struct<split: string, examples: int64>
          child 0, split: string
          child 1, examples: int64
disclosures: list<item: string>
  child 0, item: string
description: string
excluded: list<item: string>
  child 0, item: string
training_data: struct<qa: struct<reader_and_router: string, source_memory_dependency: string, token_budget: int64,  (... 160 chars omitted)
  child 0, qa: struct<reader_and_router: string, source_memory_dependency: string, token_budget: int64, seed: int64 (... 1 chars omitted)
      child 0, reader_and_router: string
      child 1, source_memory_dependency: string
      child 2, token_budget: int64
      child 3, seed: int64
  child 1, general_nlp: struct<mixture: list<item: string>, mixing: string, tokens_per_phase: int64, sequence_length: int64, (... 32 chars omitted)
      child 0, mixture: list<item: string>
          child 0, item: string
      child 1, mixing: string
      child 2, tokens_per_phase: int64
      child 3, sequence_length: int64
      child 4, batch_size: int64
      child 5, seed: int64
base_model: string
release: string
seed: int64
to
{'release': Value('string'), 'description': Value('string'), 'base_model': Value('string'), 'seed': Value('int64'), 'collection': Value('string'), 'model_repositories': List(Value('string')), 'training_data': {'qa': {'reader_and_router': Value('string'), 'source_memory_dependency': Value('string'), 'token_budget': Value('int64'), 'seed': Value('int64')}, 'general_nlp': {'mixture': List(Value('string')), 'mixing': Value('string'), 'tokens_per_phase': Value('int64'), 'sequence_length': Value('int64'), 'batch_size': Value('int64'), 'seed': Value('int64')}}, 'test_data': {'qa': {'NQ': Value('string'), 'WebQA': Value('string'), 'TriviaQA': Value('string'), 'TruthfulQA': Value('string'), 'HotpotQA': Value('string')}, 'general_nlp': {'SST2': {'split': Value('string'), 'examples': Value('int64')}, 'MR': {'split': Value('string'), 'examples': Value('int64')}, 'CR': {'split': Value('string'), 'examples': Value('int64')}, 'RT': {'split': Value('string'), 'examples': Value('int64')}, 'AGN': {'split': Value('string'), 'examples': Value('int64')}, 'Yahoo': {'split': Value('string'), 'examples': Value('int64')}}}, 'disclosures': List(Value('string')), 'excluded': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              task_family: string
              task: string
              condition: string
              split: string
              metric_schema: string
              metrics_percent: struct<EM: double, F1: double, MC1: double, MC2: double, MC3: double, mean: double, accuracy: double (... 1 chars omitted)
                child 0, EM: double
                child 1, F1: double
                child 2, MC1: double
                child 3, MC2: double
                child 4, MC3: double
                child 5, mean: double
                child 6, accuracy: double
              model_repo: string
              notes: string
              collection: string
              model_repositories: list<item: string>
                child 0, item: string
              test_data: struct<qa: struct<NQ: string, WebQA: string, TriviaQA: string, TruthfulQA: string, HotpotQA: string> (... 292 chars omitted)
                child 0, qa: struct<NQ: string, WebQA: string, TriviaQA: string, TruthfulQA: string, HotpotQA: string>
                    child 0, NQ: string
                    child 1, WebQA: string
                    child 2, TriviaQA: string
                    child 3, TruthfulQA: string
                    child 4, HotpotQA: string
                child 1, general_nlp: struct<SST2: struct<split: string, examples: int64>, MR: struct<split: string, examples: int64>, CR: (... 176 chars omitted)
                    child 0, SST2: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
                    child 1, MR: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
                    child 2, CR: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
                    child 3, RT: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
                    child 4, AGN: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
                    child 5, Yahoo: struct<split: string, examples: int64>
                        child 0, split: string
                        child 1, examples: int64
              disclosures: list<item: string>
                child 0, item: string
              description: string
              excluded: list<item: string>
                child 0, item: string
              training_data: struct<qa: struct<reader_and_router: string, source_memory_dependency: string, token_budget: int64,  (... 160 chars omitted)
                child 0, qa: struct<reader_and_router: string, source_memory_dependency: string, token_budget: int64, seed: int64 (... 1 chars omitted)
                    child 0, reader_and_router: string
                    child 1, source_memory_dependency: string
                    child 2, token_budget: int64
                    child 3, seed: int64
                child 1, general_nlp: struct<mixture: list<item: string>, mixing: string, tokens_per_phase: int64, sequence_length: int64, (... 32 chars omitted)
                    child 0, mixture: list<item: string>
                        child 0, item: string
                    child 1, mixing: string
                    child 2, tokens_per_phase: int64
                    child 3, sequence_length: int64
                    child 4, batch_size: int64
                    child 5, seed: int64
              base_model: string
              release: string
              seed: int64
              to
              {'release': Value('string'), 'description': Value('string'), 'base_model': Value('string'), 'seed': Value('int64'), 'collection': Value('string'), 'model_repositories': List(Value('string')), 'training_data': {'qa': {'reader_and_router': Value('string'), 'source_memory_dependency': Value('string'), 'token_budget': Value('int64'), 'seed': Value('int64')}, 'general_nlp': {'mixture': List(Value('string')), 'mixing': Value('string'), 'tokens_per_phase': Value('int64'), 'sequence_length': Value('int64'), 'batch_size': Value('int64'), 'seed': Value('int64')}}, 'test_data': {'qa': {'NQ': Value('string'), 'WebQA': Value('string'), 'TriviaQA': Value('string'), 'TruthfulQA': Value('string'), 'HotpotQA': Value('string')}, 'general_nlp': {'SST2': {'split': Value('string'), 'examples': Value('int64')}, 'MR': {'split': Value('string'), 'examples': Value('int64')}, 'CR': {'split': Value('string'), 'examples': Value('int64')}, 'RT': {'split': Value('string'), 'examples': Value('int64')}, 'AGN': {'split': Value('string'), 'examples': Value('int64')}, 'Yahoo': {'split': Value('string'), 'examples': Value('int64')}}}, 'disclosures': List(Value('string')), 'excluded': List(Value('string'))}
              because column names don't match

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MemoryATHENA

This dataset repository is a compact release ledger of MemoryATHENA experiment metadata and aggregate results. It follows the release style of OLAResearchX/XMemTransfer-Results. It is not a training corpus, benchmark mirror, or redistribution of task examples, labels, or prediction logs.

What is released

  • results.jsonl: 54 compact aggregate rows (30 QA condition/task rows and 24 general-NLP condition/task rows).
  • metadata.json: release provenance, dataset references, model links, and exclusions.
  • schema.json: row schema.

The repository intentionally excludes ARTIFACTS.md, internal filesystem paths, raw datasets, labels, predictions, optimizer states, and credentials.

Training data

The model stages use causal text and a token budget. The reported training and validation counts below are processed token positions, not the number of raw documents. Downstream QA/NLP labels are never training targets.

Component Training source and split Held-out validation used for checkpoint selection Budget / selection Source
QA reader/router English Wikipedia 2021 causal-text stream Held-out causal-text validation from the same Wikipedia-2021 training stream; this is not NQ/WebQuestions/TriviaQA validation 20M processed positions per stage; 2M validation positions; QA router checkpoint selected at 8.192M processed positions Wikimedia English Wikipedia dumps
General-NLP reader/router Equal-token mixture of WikiText-103, Amazon Polarity, CC-News, and IMDB causal text Held-out causal-text validation for the same general-text protocol; benchmark labels are not used for model selection 19,998,720 processed positions per stage (20M budget); 2M validation positions; sequence length 2,048; general-NLP router checkpoint selected at 16.384M positions WikiText-103, Amazon Polarity, CC-News, IMDB
Imported source memory Released Llama-2 source-memory artifact from XMemTransfer Not re-trained in this release 20M-source-memory artifact; used as an input dependency for the QA line XMemTransfer result release

Validation versus downstream evaluation

There are two different uses of the word “validation” in this release:

  1. Training validation is held-out causal text used to select memory/router checkpoints. It uses no downstream benchmark labels.
  2. Downstream benchmark splits are listed below. Their labels are used only for final metrics after inference, not to train memory, readers, or the router.

This distinction matters: a QA validation split is an evaluation benchmark split, whereas the 2M-position causal-text validation stream is the checkpoint-selection set.

Downstream evaluation datasets

All released QA and general-NLP results are inference-only evaluations with frozen model components. The exact split and the number of examples actually scored are listed here.

Five-task QA evaluation

Open-QA tasks report exact match (EM) and token F1. TruthfulQA reports MC1, MC2, MC3, and their arithmetic mean. NQ has 3,610 rows in the public validation split; the evaluator excludes one malformed answer-only row, so the released result scores 3,609 examples.

Task Dataset / configuration Split actually evaluated Examples scored Metric Dataset link
NQ google-research-datasets/nq_open validation 3,609 EM / F1 Natural Questions Open
WebQA Stanford/web_questions test 2,032 EM / F1 WebQuestions
TriviaQA mandarjoshi/trivia_qa, rc.nocontext validation 17,944 EM / F1 TriviaQA
TruthfulQA truthfulqa/truthful_qa, multiple_choice validation 817 MC1 / MC2 / MC3 / mean TruthfulQA
HotpotQA hotpotqa/hotpot_qa, distractor validation 7,405 EM / F1 HotpotQA

For the case-study and RAG diagnostics, HotpotQA uses distractor context. Supporting-fact annotations are not provided to the router or used to configure routing.

Six-task general-NLP evaluation

The primary six-task table follows the exact public kNN-Prompt task files used by the evaluator. This is important for reproducibility: MR, CR, and RT are local protocol files rather than a claim that the HF SetFit/CR or another replacement dataset is identical.

Task Exact release used by the primary evaluator Split / file used Examples scored Metric Dataset/source link
SST2 kNN-Prompt task data; GLUE SST-2 development set dev.tsv / GLUE validation 872 Accuracy GLUE on HF, exact task files
MR kNN-Prompt task data test.csv 2,000 Accuracy Exact task files
CR kNN-Prompt task data test.csv 2,000 Accuracy Exact task files
RT kNN-Prompt task data / Rotten Tomatoes test.jsonl / test 1,066 Accuracy Rotten Tomatoes on HF, exact task files
AGN AG News test 7,600 Accuracy AG News
Yahoo Yahoo Answers Topics test 60,000 Accuracy Yahoo Answers Topics

The six-task score is an unweighted mean of the six accuracies. General-NLP scoring uses domain-conditional PMI with next-token log-probability sums over label synonyms. Labels are consumed only for final accuracy.

Secondary HaluEval stress test

HaluEval is kept separate from the six-task macro-average because it uses a different binary factuality-classification protocol. The evaluator uses pminervini/HaluEval, configurations dialogue_samples, qa_samples, and summarization_samples, split data, with 10,000 examples per configuration.

Yahoo threshold note

The default router evaluation uses the configured threshold tau=0. A separate Yahoo tau=0.9/1.0 sweep is a post-hoc test-set diagnostic and must not be interpreted as an independently validation-selected threshold. It does not retrain the model and is reported separately from the default evaluation.

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