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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<model_name: string, n_latents: int64, override_latents: null, dead_latent_threshold: int64, random_seed: int64, dataset_name: string, llm_context_size: int64, llm_batch_size: int64, llm_dtype: string, buffer: int64, no_overlap: bool, act_threshold_frac: double, total_tokens: int64, scoring: bool, max_tokens_in_explanation: int64, use_demos_in_explanation: bool, n_top_ex_for_generation: int64, n_iw_sampled_ex_for_generation: int64, n_top_ex_for_scoring: int64, n_random_ex_for_scoring: int64, n_iw_sampled_ex_for_scoring: int64>
to
{'model_name': Value(dtype='string', id=None), 'random_seed': Value(dtype='int64', id=None), 'f1_jump_threshold': Value(dtype='float64', id=None), 'max_k_value': Value(dtype='int64', id=None), 'prompt_template': Value(dtype='string', id=None), 'prompt_token_pos': Value(dtype='int64', id=None), 'llm_batch_size': Value(dtype='int64', id=None), 'llm_dtype': Value(dtype='string', id=None), 'k_sparse_probe_l1_decay': Value(dtype='float64', id=None), 'k_sparse_probe_batch_size': Value(dtype='int64', id=None), 'k_sparse_probe_num_epochs': Value(dtype='int64', id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<model_name: string, n_latents: int64, override_latents: null, dead_latent_threshold: int64, random_seed: int64, dataset_name: string, llm_context_size: int64, llm_batch_size: int64, llm_dtype: string, buffer: int64, no_overlap: bool, act_threshold_frac: double, total_tokens: int64, scoring: bool, max_tokens_in_explanation: int64, use_demos_in_explanation: bool, n_top_ex_for_generation: int64, n_iw_sampled_ex_for_generation: int64, n_top_ex_for_scoring: int64, n_random_ex_for_scoring: int64, n_iw_sampled_ex_for_scoring: int64>
to
{'model_name': Value(dtype='string', id=None), 'random_seed': Value(dtype='int64', id=None), 'f1_jump_threshold': Value(dtype='float64', id=None), 'max_k_value': Value(dtype='int64', id=None), 'prompt_template': Value(dtype='string', id=None), 'prompt_token_pos': Value(dtype='int64', id=None), 'llm_batch_size': Value(dtype='int64', id=None), 'llm_dtype': Value(dtype='string', id=None), 'k_sparse_probe_l1_decay': Value(dtype='float64', id=None), 'k_sparse_probe_batch_size': Value(dtype='int64', id=None), 'k_sparse_probe_num_epochs': Value(dtype='int64', id=None)}
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/parquet_and_info.py", line 1420, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1052, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
eval_type_id
string | eval_config
dict | eval_id
string | datetime_epoch_millis
int64 | eval_result_metrics
dict | eval_result_details
list | sae_bench_commit_hash
string | sae_lens_id
string | sae_lens_release_id
string | sae_lens_version
string | sae_cfg_dict
dict | eval_result_unstructured
null |
|---|---|---|---|---|---|---|---|---|---|---|---|
absorption_first_letter | {
"model_name": "gemma-2-2b",
"random_seed": 42,
"f1_jump_threshold": 0.03,
"max_k_value": 10,
"prompt_template": "{word} has the first letter:",
"prompt_token_pos": -6,
"llm_batch_size": 32,
"llm_dtype": "bfloat16",
"k_sparse_probe_l1_decay": 0.01,
"k_sparse_probe_batch_size": 4096,
"k_sparse_probe_num_epochs": 50
} | 22128bb3-6814-43a5-a915-c8e92b3dafdf | 1,737,712,515,634 | {
"mean": {
"mean_absorption_fraction_score": 0.5128883802810622,
"mean_full_absorption_score": 0.5078935374565119,
"mean_num_split_features": 3.8076923076923075,
"std_dev_absorption_fraction_score": 0.2445340748900784,
"std_dev_full_absorption_score": 0.22888758394564582,
"std_dev_num_split_features": 2.1912413060953515
}
} | [
{
"first_letter": "a",
"mean_absorption_fraction": 0.7546226489205629,
"full_absorption_rate": 0.6554716981132076,
"num_full_absorption": 1737,
"num_probe_true_positives": 2650,
"num_split_features": 6
},
{
"first_letter": "b",
"mean_absorption_fraction": 0.7598621131562924,
"full_absorption_rate": 0.6969887076537014,
"num_full_absorption": 1111,
"num_probe_true_positives": 1594,
"num_split_features": 5
},
{
"first_letter": "c",
"mean_absorption_fraction": 0.8490867081430603,
"full_absorption_rate": 0.8771865889212828,
"num_full_absorption": 2407,
"num_probe_true_positives": 2744,
"num_split_features": 2
},
{
"first_letter": "d",
"mean_absorption_fraction": 0.7041443920920853,
"full_absorption_rate": 0.6433691756272402,
"num_full_absorption": 1077,
"num_probe_true_positives": 1674,
"num_split_features": 6
},
{
"first_letter": "e",
"mean_absorption_fraction": 0.5977416479243565,
"full_absorption_rate": 0.5981072555205047,
"num_full_absorption": 948,
"num_probe_true_positives": 1585,
"num_split_features": 5
},
{
"first_letter": "f",
"mean_absorption_fraction": 0.6613986217256544,
"full_absorption_rate": 0.6190075693860387,
"num_full_absorption": 736,
"num_probe_true_positives": 1189,
"num_split_features": 8
},
{
"first_letter": "g",
"mean_absorption_fraction": 0.6711584191384148,
"full_absorption_rate": 0.6505808757819481,
"num_full_absorption": 728,
"num_probe_true_positives": 1119,
"num_split_features": 3
},
{
"first_letter": "h",
"mean_absorption_fraction": 0.6145851334346888,
"full_absorption_rate": 0.5537848605577689,
"num_full_absorption": 556,
"num_probe_true_positives": 1004,
"num_split_features": 5
},
{
"first_letter": "i",
"mean_absorption_fraction": 0.5647881273785028,
"full_absorption_rate": 0.5909090909090909,
"num_full_absorption": 1014,
"num_probe_true_positives": 1716,
"num_split_features": 4
},
{
"first_letter": "j",
"mean_absorption_fraction": 0.31622357185428007,
"full_absorption_rate": 0.2720763723150358,
"num_full_absorption": 114,
"num_probe_true_positives": 419,
"num_split_features": 1
},
{
"first_letter": "k",
"mean_absorption_fraction": 0.1035805530467567,
"full_absorption_rate": 0.10603829160530191,
"num_full_absorption": 72,
"num_probe_true_positives": 679,
"num_split_features": 1
},
{
"first_letter": "l",
"mean_absorption_fraction": 0.4197194046864777,
"full_absorption_rate": 0.45112781954887216,
"num_full_absorption": 540,
"num_probe_true_positives": 1197,
"num_split_features": 6
},
{
"first_letter": "m",
"mean_absorption_fraction": 0.76126008101637,
"full_absorption_rate": 0.7300109529025192,
"num_full_absorption": 1333,
"num_probe_true_positives": 1826,
"num_split_features": 5
},
{
"first_letter": "n",
"mean_absorption_fraction": 0.5089982018323069,
"full_absorption_rate": 0.48058252427184467,
"num_full_absorption": 396,
"num_probe_true_positives": 824,
"num_split_features": 4
},
{
"first_letter": "o",
"mean_absorption_fraction": 0.47746126889614565,
"full_absorption_rate": 0.5518913676042677,
"num_full_absorption": 569,
"num_probe_true_positives": 1031,
"num_split_features": 4
},
{
"first_letter": "p",
"mean_absorption_fraction": 0.7995710829433103,
"full_absorption_rate": 0.7032484635645303,
"num_full_absorption": 1602,
"num_probe_true_positives": 2278,
"num_split_features": 8
},
{
"first_letter": "q",
"mean_absorption_fraction": 0.20084371096522538,
"full_absorption_rate": 0.24719101123595505,
"num_full_absorption": 44,
"num_probe_true_positives": 178,
"num_split_features": 2
},
{
"first_letter": "r",
"mean_absorption_fraction": 0.6312593232602427,
"full_absorption_rate": 0.6120481927710844,
"num_full_absorption": 1016,
"num_probe_true_positives": 1660,
"num_split_features": 6
},
{
"first_letter": "s",
"mean_absorption_fraction": 0.7108393577453287,
"full_absorption_rate": 0.7511669658886894,
"num_full_absorption": 2092,
"num_probe_true_positives": 2785,
"num_split_features": 5
},
{
"first_letter": "t",
"mean_absorption_fraction": 0.6164434867253071,
"full_absorption_rate": 0.577922077922078,
"num_full_absorption": 979,
"num_probe_true_positives": 1694,
"num_split_features": 4
},
{
"first_letter": "u",
"mean_absorption_fraction": 0.4334403730728033,
"full_absorption_rate": 0.579746835443038,
"num_full_absorption": 458,
"num_probe_true_positives": 790,
"num_split_features": 2
},
{
"first_letter": "v",
"mean_absorption_fraction": 0.4208839828800243,
"full_absorption_rate": 0.45080946450809467,
"num_full_absorption": 362,
"num_probe_true_positives": 803,
"num_split_features": 3
},
{
"first_letter": "w",
"mean_absorption_fraction": 0.574039085495602,
"full_absorption_rate": 0.5911602209944752,
"num_full_absorption": 428,
"num_probe_true_positives": 724,
"num_split_features": 1
},
{
"first_letter": "x",
"mean_absorption_fraction": 0.03381676368343293,
"full_absorption_rate": 0.038834951456310676,
"num_full_absorption": 4,
"num_probe_true_positives": 103,
"num_split_features": 1
},
{
"first_letter": "y",
"mean_absorption_fraction": 0.125208320712347,
"full_absorption_rate": 0.1437125748502994,
"num_full_absorption": 24,
"num_probe_true_positives": 167,
"num_split_features": 1
},
{
"first_letter": "z",
"mean_absorption_fraction": 0.02412150657803866,
"full_absorption_rate": 0.03225806451612903,
"num_full_absorption": 8,
"num_probe_true_positives": 248,
"num_split_features": 1
}
] | f2d1d982515d2dee706eb23a1ca459b308988764 | custom_sae | matryoshka_0121_MatryoshkaBatchTopKTrainer_gemma_batch_topk_65k_google_gemma-2-2b_batch_top_k_resid_post_layer_12_trainer_0 | 5.3.2 | {
"model_name": "gemma-2-2b",
"d_in": 2304,
"d_sae": 65536,
"hook_layer": 12,
"hook_name": "blocks.12.hook_resid_post",
"context_size": null,
"hook_head_index": null,
"architecture": "batch_topk",
"apply_b_dec_to_input": null,
"finetuning_scaling_factor": null,
"activation_fn_str": "",
"prepend_bos": true,
"normalize_activations": "none",
"dtype": "bfloat16",
"device": "",
"dataset_path": "",
"dataset_trust_remote_code": true,
"seqpos_slice": [
null
],
"training_tokens": -100000,
"sae_lens_training_version": null,
"neuronpedia_id": null
} | null |
absorption_first_letter | {
"model_name": "gemma-2-2b",
"random_seed": 42,
"f1_jump_threshold": 0.03,
"max_k_value": 10,
"prompt_template": "{word} has the first letter:",
"prompt_token_pos": -6,
"llm_batch_size": 32,
"llm_dtype": "bfloat16",
"k_sparse_probe_l1_decay": 0.01,
"k_sparse_probe_batch_size": 4096,
"k_sparse_probe_num_epochs": 50
} | 78d7a831-e164-4602-9d20-87d7ed7d8ad9 | 1,737,714,254,997 | {
"mean": {
"mean_absorption_fraction_score": 0.4771375902155045,
"mean_full_absorption_score": 0.4496415649539802,
"mean_num_split_features": 2.3076923076923075,
"std_dev_absorption_fraction_score": 0.25096954637588803,
"std_dev_full_absorption_score": 0.23370508974066048,
"std_dev_num_split_features": 1.8279875441420441
}
} | [
{
"first_letter": "a",
"mean_absorption_fraction": 0.7209440526228379,
"full_absorption_rate": 0.570566037735849,
"num_full_absorption": 1512,
"num_probe_true_positives": 2650,
"num_split_features": 7
},
{
"first_letter": "b",
"mean_absorption_fraction": 0.6388987910901524,
"full_absorption_rate": 0.5370138017565872,
"num_full_absorption": 856,
"num_probe_true_positives": 1594,
"num_split_features": 3
},
{
"first_letter": "c",
"mean_absorption_fraction": 0.8098736964486499,
"full_absorption_rate": 0.8330903790087464,
"num_full_absorption": 2286,
"num_probe_true_positives": 2744,
"num_split_features": 1
},
{
"first_letter": "d",
"mean_absorption_fraction": 0.6718736558364921,
"full_absorption_rate": 0.5758661887694145,
"num_full_absorption": 964,
"num_probe_true_positives": 1674,
"num_split_features": 5
},
{
"first_letter": "e",
"mean_absorption_fraction": 0.5642140218956156,
"full_absorption_rate": 0.5955835962145111,
"num_full_absorption": 944,
"num_probe_true_positives": 1585,
"num_split_features": 3
},
{
"first_letter": "f",
"mean_absorption_fraction": 0.7224934526631536,
"full_absorption_rate": 0.6526492851135408,
"num_full_absorption": 776,
"num_probe_true_positives": 1189,
"num_split_features": 1
},
{
"first_letter": "g",
"mean_absorption_fraction": 0.5700730112409836,
"full_absorption_rate": 0.5326184092940125,
"num_full_absorption": 596,
"num_probe_true_positives": 1119,
"num_split_features": 1
},
{
"first_letter": "h",
"mean_absorption_fraction": 0.41762285150989736,
"full_absorption_rate": 0.3296812749003984,
"num_full_absorption": 331,
"num_probe_true_positives": 1004,
"num_split_features": 1
},
{
"first_letter": "i",
"mean_absorption_fraction": 0.5232450462526299,
"full_absorption_rate": 0.6148018648018648,
"num_full_absorption": 1055,
"num_probe_true_positives": 1716,
"num_split_features": 4
},
{
"first_letter": "j",
"mean_absorption_fraction": 0.11493574859587224,
"full_absorption_rate": 0.07875894988066826,
"num_full_absorption": 33,
"num_probe_true_positives": 419,
"num_split_features": 1
},
{
"first_letter": "k",
"mean_absorption_fraction": 0.032113461128301456,
"full_absorption_rate": 0.033873343151693665,
"num_full_absorption": 23,
"num_probe_true_positives": 679,
"num_split_features": 1
},
{
"first_letter": "l",
"mean_absorption_fraction": 0.49122204590864305,
"full_absorption_rate": 0.5146198830409356,
"num_full_absorption": 616,
"num_probe_true_positives": 1197,
"num_split_features": 3
},
{
"first_letter": "m",
"mean_absorption_fraction": 0.7045541679289932,
"full_absorption_rate": 0.6637458926615553,
"num_full_absorption": 1212,
"num_probe_true_positives": 1826,
"num_split_features": 3
},
{
"first_letter": "n",
"mean_absorption_fraction": 0.47456703255649185,
"full_absorption_rate": 0.3919902912621359,
"num_full_absorption": 323,
"num_probe_true_positives": 824,
"num_split_features": 1
},
{
"first_letter": "o",
"mean_absorption_fraction": 0.3265858884691408,
"full_absorption_rate": 0.3617846750727449,
"num_full_absorption": 373,
"num_probe_true_positives": 1031,
"num_split_features": 1
},
{
"first_letter": "p",
"mean_absorption_fraction": 0.7996323048325572,
"full_absorption_rate": 0.6940298507462687,
"num_full_absorption": 1581,
"num_probe_true_positives": 2278,
"num_split_features": 6
},
{
"first_letter": "q",
"mean_absorption_fraction": 0.3328662448367742,
"full_absorption_rate": 0.2640449438202247,
"num_full_absorption": 47,
"num_probe_true_positives": 178,
"num_split_features": 1
},
{
"first_letter": "r",
"mean_absorption_fraction": 0.6658450862499968,
"full_absorption_rate": 0.6144578313253012,
"num_full_absorption": 1020,
"num_probe_true_positives": 1660,
"num_split_features": 4
},
{
"first_letter": "s",
"mean_absorption_fraction": 0.7318918310046347,
"full_absorption_rate": 0.7378815080789947,
"num_full_absorption": 2055,
"num_probe_true_positives": 2785,
"num_split_features": 5
},
{
"first_letter": "t",
"mean_absorption_fraction": 0.6902991878323571,
"full_absorption_rate": 0.627508854781582,
"num_full_absorption": 1063,
"num_probe_true_positives": 1694,
"num_split_features": 2
},
{
"first_letter": "u",
"mean_absorption_fraction": 0.2673169114571065,
"full_absorption_rate": 0.3594936708860759,
"num_full_absorption": 284,
"num_probe_true_positives": 790,
"num_split_features": 1
},
{
"first_letter": "v",
"mean_absorption_fraction": 0.475902264449155,
"full_absorption_rate": 0.4657534246575342,
"num_full_absorption": 374,
"num_probe_true_positives": 803,
"num_split_features": 1
},
{
"first_letter": "w",
"mean_absorption_fraction": 0.4971857642669609,
"full_absorption_rate": 0.4516574585635359,
"num_full_absorption": 327,
"num_probe_true_positives": 724,
"num_split_features": 1
},
{
"first_letter": "x",
"mean_absorption_fraction": 0.04666909974998482,
"full_absorption_rate": 0.02912621359223301,
"num_full_absorption": 3,
"num_probe_true_positives": 103,
"num_split_features": 1
},
{
"first_letter": "y",
"mean_absorption_fraction": 0.08557497383561033,
"full_absorption_rate": 0.11976047904191617,
"num_full_absorption": 20,
"num_probe_true_positives": 167,
"num_split_features": 1
},
{
"first_letter": "z",
"mean_absorption_fraction": 0.029176752940125,
"full_absorption_rate": 0.04032258064516129,
"num_full_absorption": 10,
"num_probe_true_positives": 248,
"num_split_features": 1
}
] | f2d1d982515d2dee706eb23a1ca459b308988764 | custom_sae | matryoshka_0121_MatryoshkaBatchTopKTrainer_gemma_batch_topk_65k_google_gemma-2-2b_batch_top_k_resid_post_layer_12_trainer_1 | 5.3.2 | {
"model_name": "gemma-2-2b",
"d_in": 2304,
"d_sae": 65536,
"hook_layer": 12,
"hook_name": "blocks.12.hook_resid_post",
"context_size": null,
"hook_head_index": null,
"architecture": "batch_topk",
"apply_b_dec_to_input": null,
"finetuning_scaling_factor": null,
"activation_fn_str": "",
"prepend_bos": true,
"normalize_activations": "none",
"dtype": "bfloat16",
"device": "",
"dataset_path": "",
"dataset_trust_remote_code": true,
"seqpos_slice": [
null
],
"training_tokens": -100000,
"sae_lens_training_version": null,
"neuronpedia_id": null
} | null |
absorption_first_letter | {
"model_name": "gemma-2-2b",
"random_seed": 42,
"f1_jump_threshold": 0.03,
"max_k_value": 10,
"prompt_template": "{word} has the first letter:",
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] | fda4e306e03c1eb5996046a544947f9663d3ae39 | custom_sae | matroyshka_gemma-2-2b-16k-v2_BatchTopKTrainer_baseline_google_gemma-2-2b_ctx1024_0114_resid_post_layer_12_trainer_1 | 5.3.0 | {
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} | null |
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