Update README.md

README.md

@@ -28,4 +28,85 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
 ---
+# NAMEXTEND
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset extends [NAMEXACT](https://huggingface.co/datasets/aieng-lab/namexact) by including words that can be used as names, but may not exclusively be used as names in every context.
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+Unlike [NAMEXTEND](https://huggingface.co/datasets/aieng-lab/namexact), this datasets contains *words* that are mostly used as *names*, but may also be used in other contexts, such as
+
+- *Christian* (believer in Christianity)
+- *Drew* (simple past of the verb to draw)
+- *Florence* (an Italian city)
+- *Henry* (the SI unit of inductance)
+- *Mercedes* (a car brand)
+
+In addition, names with ambiguous gender are included - once for each `gender`. For instance, `Skyler` is included as female (`F`) name with a `probability` of 37.3%, and as male (`M`) name with a `probability` of 62.7%.
+
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Original Dataset:** [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name)
+
+
+## Dataset Structure
+
+- `name`: the name
+- `gender`: the gender of the name (`M` for male and `F` for female)
+- `count`: the count value of this name (raw value from the original dataset)
+- `probability`: the probability of this name (raw value from original dataset; not normalized to this dataset!)
+- `gender_agreement`: a value describing the certainty that this name has an unambiguous gender computed as the maximum probability of that name across both genders, e.g., $max(37.7%, 62.7%)=62.7%$ for *Skyler*. For names with a unique `gender` in this dataset, this value is 1.0
+- `primary_gender`: is equal to `gender` for names with a unique gender in this dataset, and equals otherwise the gender of that name with higher probability
+- `genders`: label `B` if *both* genders are contained for this name in this dataset, otherwise equal to `gender`
+- `prob_F`: the probability of that name being used as a female name (i.e., 0.0 or 1.0 if `genders` != `B`)
+- `prob_M`: the probability of that name being used as a male name
+
+## Dataset Creation
+
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+The data is created by filtering [Gender by Name](https://archive.ics.uci.edu/dataset/591/gender+by+name).
+
+
+#### 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 original data is filtered to contain only names with a `count` of at least 100 to remove very rare names. This threshold reduces the total number of names by $72%, from 133910 to 37425.
+
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+The original dataset provides counts of names (with their gender) for male and female babies from open-source government authorities in the US (1880-2019), UK (2011-2018), Canada (2011-2018), and Australia (1944-2019) in these periods.
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+## Dataset Card Authors
+
+[jdrechsel](https://huggingface.co/jdrechsel)
+