Update README.md

README.md

@@ -1,27 +1,46 @@
 ---
 license: mit
 language:
-- en
+- en,
 tags:
 - legal
 - patents
-pretty_name: CLEFIP-2011
+pretty_name: PatClass2011
 size_categories:
 - 10B<n<100B
 ---
-# CLEFIP-2011 Dataset
+# PatClass2011 Dataset
 
 ![CLEFIP-2011](https://huggingface.co/datasets/amylonidis/PatClass2011/resolve/main/CLEFIP2011.png)
 
 ## Dataset Summary
 
-The **PatClass2011** dataset is a comprehensive collection of approximately 719,000 patent documents, focusing on patent classification tasks. Each entry encompasses detailed metadata and textual content, including titles, abstracts, descriptions, and claims. The dataset is structured to facilitate research in patent classification, information retrieval, and natural language processing.
+The **PatClass2011** dataset is a comprehensive collection of approximately 719,000 patent documents from the CLEF-IP 2011 Test Collection,
+focusing on patent classification tasks. Each entry encompasses detailed metadata and textual content, including titles, abstracts, descriptions, and claims.
+The dataset is structured to facilitate research in patent classification, information retrieval, and natural language processing.
+
+## Languages
+
+The dataset contains English, French and German text.
+
+## Domain
+
+Patents (intellectual property).
+
+## Dataset Curators
+
+The dataset was created by Eleni Kamateri and Tasos Mylonidis
 
 ## Dataset Structure
 
-### Data Instances
+The dataset consists of 28 folders which correspond to a specific year, ranging from 1978 to 2005. Within each yearly subdirectory, you'll find a CSV file named in the format
+clefip2011_en_classification_<year>.csv. These files contain patent data that were all published that year.
+This structure facilitates year-wise analysis, allowing researchers to study trends and patterns in patent classifications over time. In total, there are 19 data fields for each CSV
 
-Each record in the dataset represents a patent document with the following fields:
+
+### Data Fields
+
+The dataset is provided in CSV format and includes the aforementioned fields
 
 - `ucid`: Unique identifier for the patent document.
 - `doc_number`: Patent document number.
@@ -30,7 +49,7 @@ Each record in the dataset represents a patent document with the following field
 - `lang`: Language of the patent document.
 - `date`: Publication date of the patent.
 - `application_date`: Date when the patent application was filed.
-- `date_produced`: Date when the data was produced or processed.
+- `date_produced`: Date when the data was inserted in the dataset.
 - `status`: Status of the patent document.
 - `main_code`: Primary classification code assigned to the patent.
 - `further_codes`: Additional classification codes.
@@ -43,13 +62,104 @@ Each record in the dataset represents a patent document with the following field
 - `applicants`: Entities or individuals who applied for the patent.
 - `inventors`: Inventors credited in the patent document.
 
-### Data Fields
+## Usage
 
-The dataset is provided in CSV format and includes the aforementioned fields. The textual fields (`title`, `abstract`, `description`, `claims`) are particularly useful for natural language processing tasks.
+## Loading the Dataset
 
-## Usage
+### Sample ( 1985 March to April )
+
+The following script can be used to load a sample version of the dataset, which contains all the patent applications
+that were published from March until April in 1985.
+
+
+'''
+
+def load_csvs_from_huggingface( start_date, end_date):
+    """
+    Load only the necessary CSV files from a Hugging Face dataset repository.
+
+    :param start_year: int, the start year (inclusive)
+    :param end_year: int, the end year (inclusive)
+
+    :return: pd.DataFrame, combined data from selected CSVs
+    """
+
+
+    column_types = {
+      "ucid": "string",
+      "country": "category",
+      "doc_number": "int64",
+      "kind": "category",
+      "lang": "category",,
+      "date": "int32",
+      "application_date": "int32",
+      "date_produced": "int32",
+      "status": "category",
+      "main_code": "string",
+      "further_codes": "string",
+      "ipcr_codes": "string",
+      "ecla_codes": "string",
+      "title": "string",
+      "abstract": "string",
+      "description": "string",
+      "claims": "string",
+      "applicants": "string",
+      "inventors": "string",
+  }
+
+
+    dataset_years = ['1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
+                     '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
+                     '1996','1997', '1998', '1999', '2000', '2001', '2002','2003', '2004', '2005']
+
+    start_date_int = int(datetime.strptime(start_date, "%Y-%m-%d").strftime("%Y%m%d"))
+    end_date_int = int(datetime.strptime(end_date, "%Y-%m-%d").strftime("%Y%m%d"))
 
-To load the dataset using the Hugging Face `datasets` library:
+    start_year, end_year = str(start_date_int)[:4], str(end_date_int)[:4]
+
+    given_years = [str(year) for year in range(int(start_year), int(end_year) + 1)]
+
+    matching_years = [f  for f in dataset_years for year in given_years if f==year]
+
+    if not matching_years:
+        raise ValueError(f"No matching CSV files found in dataset for the given dates")
+
+    df_list = []
+    for year in matching_years:
+        filepath = f"data/years/{year}/clefip2011_en_classification_{year}_validated.csv"
+        print(filepath)
+
+        try:
+            dataset = load_dataset("amylonidis/PatClass2011", data_files=filepath)
+            df = dataset["train"].to_pandas().astype(column_types)
+            df_list.append(df)
+        except Exception as e:
+            print(f"Error loading {filepath}: {e}")
+
+
+    if df_list:
+        df = pd.concat(df_list, ignore_index=True)
+        df["date"] = df["date"].astype(float).astype(int)
+        df_filtered = df[(df["date"] >= start_date_int) & (df["date"] <= end_date_int)]
+        return df_filtered
+    else:
+        return pd.DataFrame()
+
+'''
+
+'''
+
+start_date = "1985-03-01"
+end_date = "1985-04-30"
+
+df = load_csvs_from_huggingface(start_date, end_date)
+
+
+'''
+
+### Full
+
+To load the complete dataset using the Hugging Face `datasets` library:
 
 ```python
 from datasets import load_dataset
@@ -57,26 +167,24 @@ from datasets import load_dataset
 dataset = load_dataset("amylonidis/PatClass2011")
 ```
 
-This will load the dataset into a `DatasetDict` object, allowing for easy access and manipulation.
+This will load the dataset into a `DatasetDict` object, please make sure you have enough disk space.
 
-## Dataset Creation
+## Google Colab Analytics
 
-### Source Data
 
-The PatClass2011 dataset aggregates patent documents from various patent offices, ensuring a diverse and comprehensive collection. The data includes both metadata and full-text fields, facilitating a wide range of research applications.
+You can also use the following Google Colab notebooks to explore the Analytics that were performed to the dataset.
 
-### Annotations
+- [Date Analytics](https://colab.research.google.com/drive/1N2w5F1koWmZOyQaf0ZTB3gighPTXtUzD?usp=sharing)
 
-The dataset includes classification codes (`main_code`, `further_codes`, `ipcr_codes`, `ecla_codes`) assigned by patent offices.
+## Dataset Creation
 
-## Applications
+### Source Data
 
-The PatClass2011 dataset is suitable for various research and development tasks, including:
+The PatClass2011 dataset aggregates the patent documents from the CLEF-IP 2011 Test Collection using a parsing script. The data includes both metadata and full-text fields, facilitating a wide range of research applications.
 
-- Patent classification and categorization.
-- Information retrieval and search systems.
-- Natural language processing applications such as summarization and keyword extraction.
-- Machine learning models for legal and technical document analysis.
+### Annotations
+
+The dataset does not contain any human-written or computer-generated annotations beyond those produced by patent documents of the Source Data.
 
 ## Licensing Information
 
@@ -86,4 +194,4 @@ This dataset is distributed under the [MIT License](https://opensource.org/licen
 
 If you utilize the PatClass2011 dataset in your research or applications, please cite it appropriately.
 
----
+---