Update README.md

README.md

@@ -7,12 +7,10 @@ tags:
 size_categories:
 - n<1K
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: "data/dimmi_600.tsv"
-  - split: test
-    path: "data/dimmi_gs_07.json"
+- config_name: dimmi_600
+  data_files: "data/dimmi_600.tsv"
+- config_name: dimmi_gs_07
+  data_files: "data/dimmi_gs_07.json"
 ---
 # Dataset Card for Dataset Name
 
@@ -48,25 +46,75 @@ among which 1439 refer to generic drugs and 380 to class A drugs.
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
+The primary objective of this dataset is to facilitate the development of natural language processing models in the pharmaceutical domain. 
+
+Specifically, it is designed to support:
+
+1. **Information Extraction Tasks**: The dataset serves as a foundation for training models to extract critical information from pharmaceutical texts.
+
+2. **Question Answering Systems**: It enables the creation of models capable of accurately answering specific questions related to:
+   - Drug dosage
+   - Medication usage
+   - Side effects
+   - Drug-drug interactions
+
 
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
+
+**Model Evaluation**: Researchers and developers can use this dataset to benchmark the performance of their NLP models in pharmaceutical information extraction tasks.
+
+**AI in Healthcare**: The dataset contributes to the broader goal of enhancing AI applications in healthcare, particularly in pharmacology and patient care.
+
+By approaching this challenge as an information extraction task, researchers can develop models that not only understand pharmaceutical texts but also extract and structure relevant information, making it readily accessible for healthcare professionals and patients alike.
+
 
 [More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+While this dataset is designed to support research and development in pharmaceutical information extraction, it's important to note its limitations and potential misuses. 
+
+The following uses are considered out of scope and should be avoided:
+
+1. **Medical Diagnosis or Treatment**: This dataset is not intended to be used as a substitute for professional medical advice, diagnosis, or treatment. It should not be used to make clinical decisions without consultation with qualified healthcare professionals.
+
+2. **Drug Prescription**: The information extracted from this dataset should not be used to prescribe, modify, or discontinue any medication regimen without proper medical supervision.
+
+3. **Legal or Regulatory Compliance**: The dataset is not designed to ensure compliance with pharmaceutical regulations or legal requirements. It should not be used as a sole source for regulatory or legal decision-making in the pharmaceutical industry.
+
+4. **Patient-Specific Recommendations**: The dataset does not account for individual patient characteristics, medical history, or specific health conditions. It should not be used to generate personalized medical recommendations.
+
+5. **Commercial Product Development**: While the dataset can inform research, it is not intended for direct commercial application in drug development or marketing without additional validation and regulatory approval.
+
+6. **Comprehensive Drug Information**: The dataset does not cover all existing drugs or all possible drug interactions. It should not be considered an exhaustive source of pharmaceutical information.
+
+Users of this dataset should be aware of these limitations and ensure that their applications align with ethical guidelines and professional standards in healthcare and pharmaceutical research.
 
-[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. -->
+The DIMMI dataset consists of two main files:
+
+1. **dimmi_600.tsv**
+   This file contains the primary data entries, where each entry includes:
+   - ID: A unique identifier for each entry
+   - ID_LOC: Indicates the ID location in the original corpus
+   - Drug_Name: The name of the drug
+   - Text: The full text of the drug leaflet
+
+2. **dimmi_gs_07.json**
+   This file contains the gold standard annotations, structured as follows:
+   - ID_LOC: Corresponds to the ID_LOC in dimmi_600.tsv
+   - Annotations for the following categories, each represented as a list of strings:
+     - Molecule
+     - Usage
+     - Dosage
+     - Drug Interaction
+     - Side Effect
+
+The use of lists for the annotations in dimmi_gs_07.json allows for multiple entries per category, providing a comprehensive and flexible representation of the annotated information.
 
-[More Information Needed]
 
 ## Dataset Creation
 
@@ -90,11 +138,18 @@ among which 1439 refer to generic drugs and 380 to class A drugs.
 
 <!-- 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]
+### Annotations
+
+We developed a comprehensive gold standard (GS). This GS was created through manual annotation of the following key categories:
+
+1. Molecule
+2. Dosage
+3. Drug Interaction
+4. Usage
+5. Side Effect
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+The manual annotation process ensures a high-quality benchmark against which we can evaluate our system's accuracy and effectiveness in identifying and categorizing pharmaceutical information.
 
 #### Annotation process