chore: write README

README.md

@@ -18,41 +18,70 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **Curated by:** [Emergent Methods](https://www.emergentmethods.ai/)
+- **Funded by:** [Emergent Methods](https://www.emergentmethods.ai/)
+- **Shared by:** [Emergent Methods](https://www.emergentmethods.ai/)
+- **Language(s) (NLP):** English (en), and translated French, Spanish, German, Swedish, Italian, Arabic, Chinese, Norwegian, Danish, Portuguese, Dutch, Russian, Ukranian
+- **License:** Apache 2.0
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** [AskNews API](https://docs.asknews.app)
+- **Paper:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
 <!-- Address questions around how the dataset is intended to be used. -->
 
-### Direct Use
+This dataset is intended to be used to fine-tune entity extractors for improved generalization, as well as higher accuracy on the latest news events. For example, we used this dataset to fine-tune `GLiNER-news`, which is a fine-tuned version of `GLiNER`, geared toward improved entity extraction on news articles. The fine-tune improved performance for nearly all benchmarks (even beyond news).
 
-<!-- This section describes suitable use cases for the dataset. -->
 
-[More Information Needed]
-
-### 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]
+The dataset is structured as follows:
+
+```
+5049-formatted-summaries_llama3-dataset_splits.json
+  - train
+  - test
+  - validation
+```
+
+Where each split is a list of structured JSON, where each sample is structured as follows:
+
+```json
+        {
+            "metadata": {
+                "source_country": <country str>,
+                "article_language": <language str>,
+                "article_pubDate": <pub_date datetime>,
+                "topic-classification": [
+                    <topic classification str>
+                ],
+                "articleId": <AskNews article uuid>
+            },
+            "tokenized_text": [
+                <word string>,
+                <word string>,
+                ...
+            ],
+            "ner": [
+                [
+                    <Start word int>,
+                    <Stop word int>,
+                    <Entity str>
+                ],
+                ...
+            ]
+        },
+        ...
+```
 
 ## Dataset Creation
 
@@ -60,57 +89,82 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 <!-- Motivation for the creation of this dataset. -->
 
-[More Information Needed]
+This dataset was created in an effort to improve the representation of underrepresented topics and entities in entity extractors, thereby improving entity extraction accuracy and generalization. The pre-processing pipeline for this dataset follows a strict set of steps:
+
+[AskNews API](https://docs.asknews.app):
+1. Enforce diversity on the collection of news articles from diverse countries/languages/sources. 
+2. Translate and summarize the articles with Llama2.
+3. Embed summaries to vectors
+
+Present dataset curation:
+4. Cluster embeddings according to topic, for each 4 hour bucket of articles throughout the duration of February 20-March 30 2024.
+5. Pull samples from clusters, distributing evenly across country of origin.
+6. Extract entities from each summary using Llama3.
+
+![countries distribution](figures/countries_distribution.png)
+
+The data was used to train `GLiNER-news`, which is a fine-tuned version of `GLiNER`, geared towared improved entity extraction on news articles. The fine-tune improved performance for nearly all benchmarks (even beyond news):
+
+![topic distribution](figures/topics_fig_connected.png)
+
+The entity types in the dataset are limited to the following:
+
+![entity-types](figures/entity-types_limited.png)
 
 ### Source Data
 
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 
+The synthetic data is pulled from [AskNews API](https://docs.asknews.app), which generates news translations and summaries using Llama2/3 from open-web news content.
+
 #### 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]
+The [AskNews API](https://docs.asknews.app) uses open-web news articles to generate synthetic data (news article summaries) with Llama2/3. This dataset was pulled from the API by querying 4 hour buckets of articles between February 20 and March 31, 2024. These buckets were then processed with the following steps:
+
+4. Cluster embeddings according to topic, for each 4 hour bucket of articles throughout the duration of February 20-March 30 2024.
+5. Pull samples from clusters, distributing evenly across country of origin.
+6. Extract entities from each summary using Llama3.
 
 #### 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]
+The source data producer is the [AskNews API](https://docs.asknews.app), which uses open-web news articles to generate translations and summaries.
 
-<!-- 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]
+The news translations and summaries are passed to Llama3 for entity extraction to extract entities.
 
 #### Who are the annotators?
 
 <!-- This section describes the people or systems who created the annotations. -->
 
-[More Information Needed]
+[Emergent Methods](https://www.emergentmethods.ai/) built and oversaw the systems used to annotate the dataset.
 
 #### 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]
+This dataset does not contain any information that is not publicly available on the open-web.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+Although the goal of the dataset is to reduce bias, and improve diversity, it is still biased to western languages and countries. This limitation originates from the abilities of Llama2 for the translation and summary generations. Further, any bias originating in Llama2 training data will also be present in this dataset, since Llama2 was used to summarize the open-web articles. Further, any biases present in Llama3 will be present in the present dataaset since Llama3 was used to extract entities from the summaries.
+
+![countries distribution](figures/topics_fig_connected.png)
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+Carefully consider the dataset topic, country, and language distributions when implementing or training on this data.
 
 ## Citation [optional]
 
@@ -124,20 +178,9 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 
 [More Information Needed]
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+## Dataset Card Authors
 
-## Dataset Card Contact
+Elin Törnquist, Emergent Methods elin at emergentmethods.ai
+Robert Caulk, Emergent Methods rob at emergentmethods.ai
 
-[More Information Needed]