# Dataset Card for "counterfact-filtered-gptj6b"

This dataset is a subset of azhx/counterfact-easy, however it was filtered based on a *heuristic* that was used to determine whether the knowledge in each row is actually known by the GPT-J-6B model


## The heuristic is as follows:

For each prompt in the original counterfact dataset used by ROME, we use GPT-J-6B to generate n=5 completions to a max generated token length of 30. 
If the completion contains the answer that is specified in the dataset for the majority of the completions (>=3), then we conclude that the model does indeed know this fact. 

In practice, we find that many of the prompts in the original dataset cannot be answered accurately a lot of the time using GPT-J-6B. The number of case_ids were filtered from ~21k to about ~3k. 
region:us
{"dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "proposition", "dtype": "string"}, {"name": "subject+predicate", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}, {"name": "case_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 915160.9417906551, "num_examples": 6896}, {"name": "test", "num_bytes": 101655.05820934482, "num_examples": 766}], "download_size": 421630, "dataset_size": 1016816.0}}
TAGS
#region-us 

# Dataset Card for "counterfact-filtered-gptj6b"

This dataset is a subset of azhx/counterfact-easy, however it was filtered based on a *heuristic* that was used to determine whether the knowledge in each row is actually known by the GPT-J-6B model


## The heuristic is as follows:

For each prompt in the original counterfact dataset used by ROME, we use GPT-J-6B to generate n=5 completions to a max generated token length of 30. 
If the completion contains the answer that is specified in the dataset for the majority of the completions (>=3), then we conclude that the model does indeed know this fact. 

In practice, we find that many of the prompts in the original dataset cannot be answered accurately a lot of the time using GPT-J-6B. The number of case_ids were filtered from ~21k to about ~3k. 
# Dataset Card for "counterfact-filtered-gptj6b"

This dataset is a subset of azhx/counterfact-easy, however it was filtered based on a *heuristic* that was used to determine whether the knowledge in each row is actually known by the GPT-J-6B model ## The heuristic is as follows:

For each prompt in the original counterfact dataset used by ROME, we use GPT-J-6B to generate n=5 completions to a max generated token length of 30. 
If the completion contains the answer that is specified in the dataset for the majority of the completions (>=3), then we conclude that the model does indeed know this fact. 

In practice, we find that many of the prompts in the original dataset cannot be answered accurately a lot of the time using GPT-J-6B. The number of case_ids were filtered from ~21k to about ~3k.
TAGS
#region-us 
 # Dataset Card for "counterfact-filtered-gptj6b"

This dataset is a subset of azhx/counterfact-easy, however it was filtered based on a *heuristic* that was used to determine whether the knowledge in each row is actually known by the GPT-J-6B model ## The heuristic is as follows:

For each prompt in the original counterfact dataset used by ROME, we use GPT-J-6B to generate n=5 completions to a max generated token length of 30. 
If the completion contains the answer that is specified in the dataset for the majority of the completions (>=3), then we conclude that the model does indeed know this fact. 

In practice, we find that many of the prompts in the original dataset cannot be answered accurately a lot of the time using GPT-J-6B. The number of case_ids were filtered from ~21k to about ~3k.
passage: TAGS
#region-us 
# Dataset Card for "counterfact-filtered-gptj6b"

This dataset is a subset of azhx/counterfact-easy, however it was filtered based on a *heuristic* that was used to determine whether the knowledge in each row is actually known by the GPT-J-6B model## The heuristic is as follows:

For each prompt in the original counterfact dataset used by ROME, we use GPT-J-6B to generate n=5 completions to a max generated token length of 30. 
If the completion contains the answer that is specified in the dataset for the majority of the completions (>=3), then we conclude that the model does indeed know this fact. 

In practice, we find that many of the prompts in the original dataset cannot be answered accurately a lot of the time using GPT-J-6B. The number of case_ids were filtered from ~21k to about ~3k.