Datasets:

mksethi
/

eli5_sae_features

@@ -1,6 +1,6 @@
 # Dataset Card for gpt2_eli5_sae_features
-This dataset aims to create a corpus of data to help push reserach into monosemantic features using SAE's. It has been generated using [this raw template](https://github.com/manik-sethi/hallucination-circuits)
 ## Dataset Details
@@ -30,17 +30,12 @@ This dataset takes the eli5 subset from facebook/kilt_tasks and processes it to
 This dataset is meant to train a multi-layer-perceptron to predict what the SAE feature activations would be in the answer for a prompt. Note that this dataset doesn't actually have the answers to the tokenized questions, but rather a ~25k long vector of activations for different features in a trained SAE.
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## Dataset Structure
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
 ## Dataset Creation
@@ -52,41 +47,15 @@ This dataset is meant to train a multi-layer-perceptron to predict what the SAE
 ### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 #### Data Collection and Processing
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
@@ -100,32 +69,7 @@ This dataset is meant to train a multi-layer-perceptron to predict what the SAE
 Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
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-## Dataset Card Authors [optional]
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 ## Dataset Card Contact
-[More Information Needed]

 # Dataset Card for gpt2_eli5_sae_features
+This dataset aims to create a corpus of data to help guide research into monosemantic features using SAE's. It has been generated using [this raw template](https://github.com/manik-sethi/hallucination-circuits)
 ## Dataset Details
 This dataset is meant to train a multi-layer-perceptron to predict what the SAE feature activations would be in the answer for a prompt. Note that this dataset doesn't actually have the answers to the tokenized questions, but rather a ~25k long vector of activations for different features in a trained SAE.
 ## Dataset Structure
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+Currently, we only have the train split from the ELI5 dataset. Therefore, this dataset is very similarly structured.
 ## Dataset Creation
 ### Source Data
+The source of this data is from the subreddit "Explain Like I'm 5", or ELI5 for short.
 #### Data Collection and Processing
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+The inputs are tokenized prompts. The labels are tokenized, embedded, and then encoded with the given SAE layer.
 ## Bias, Risks, and Limitations
 Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 ## Dataset Card Contact
+mksethi@ucdavis.edu