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---
license: mit
language:
- en,
tags:
- legal
- patents
pretty_name: PatClass2011
size_categories:
- 10B<n<100B
---
# 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 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

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
### Data Fields
The dataset is provided in CSV format and includes the aforementioned fields
- `ucid`: Unique identifier for the patent document.
- `patent_number`: Patent document number.
- `country`: Country code of the patent.
- `kind`: Kind code indicating the type of patent document.
- `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 inserted in the dataset.
- `status`: Status of the patent document.
- `main_code`: Primary classification code assigned to the patent.
- `further_codes`: Additional classification codes.
- `ipcr_codes`: International Patent Classification codes.
- `ecla_codes`: European Classification codes.
- `title`: Title of the patent document.
- `abstract`: Abstract summarizing the patent.
- `description`: Detailed description of the patent.
- `claims`: Claims defining the scope of the patent protection.
- `applicants`: Entities or individuals who applied for the patent.
- `inventors`: Inventors credited in the patent document.

## Usage

## Loading the Dataset

### 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, with all columns and once with selected columns.


```python
from datasets import load_dataset
import pandas as pd
from datetime import datetime
import gc
def load_temporal_optimized(start_date, end_date, columns_to_keep=None):
    """
    Load only the necessary CSV files from a Hugging Face dataset repository.
    :param start_date: str, the start date in 'YYYY-MM-DD' format (inclusive)
    :param end_date: str, the end date in 'YYYY-MM-DD' format (inclusive)
    :param columns_to_keep: list of str, optional. Specific columns to load (e.g., ["date", "lang"]).
    :return: pd.DataFrame, combined data from selected CSVs
    """
    huggingface_dataset_name = "amylonidis/PatClass2011"
    column_types = {
        "ucid": "string[pyarrow]",
        "country": "category",
        "kind": "category",
        "lang": "category",
        "date": "Int32",
        "application_date": "Int32",
        "date_produced": "Int32",
        "status": "category",
        "main_code": "string[pyarrow]",
        "further_codes": "string[pyarrow]",
        "ipcr_codes": "string[pyarrow]",
        "ecla_codes": "string[pyarrow]",
        "title": "string[pyarrow]",
        "abstract": "string[pyarrow]",
        "description": "string[pyarrow]",
        "claims": "string[pyarrow]",
        "applicants": "string[pyarrow]",
        "inventors": "string[pyarrow]",
        "patent_number": "Int64",
    }
    category_cols = {"country", "kind", "lang", "status"}
    dataset_years = [str(year) for year in range(1978, 2006)]
    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"))
    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 if f in given_years]
    if not matching_years:
        raise ValueError(f"No matching CSV files found for {start_date} to {end_date}")
    df_list = []
    for year in matching_years:
        filepath = f"data/years/{year}/clefip2011_en_classification_{year}_validated.csv"
        try:
            # 1. Load the dataset (This stays on disk as an Arrow memory-map, NOT in RAM)
            dataset = load_dataset(
                huggingface_dataset_name,
                data_files=filepath,
                split="train",
                sep=";",
                on_bad_lines="skip"
            )
            # Select Columns Before Chunking
            if columns_to_keep is not None:
                # Safety check: Only select columns that actually exist in this specific file
                valid_columns = [col for col in columns_to_keep if col in dataset.column_names]
                if valid_columns:
                    dataset = dataset.select_columns(valid_columns)
            # 2. Tell HuggingFace to output Pandas dataframes when sliced
            dataset = dataset.with_format("pandas")
            # 3. CHUNKING: Process exactly 40,000 rows at a time
            chunk_size = 10000
            for i in range(0, len(dataset), chunk_size):
                # Only these 40,000 rows are loaded into RAM
                df_chunk = dataset[i : i + chunk_size]
                df_chunk.columns = df_chunk.columns.str.strip()
                # Filter this specific chunk
                if "date" in df_chunk.columns:
                    temp_dates = pd.to_numeric(df_chunk["date"], errors="coerce")
                    mask = (temp_dates >= start_date_int) & (temp_dates <= end_date_int)
                    df_filtered = df_chunk[mask].copy()
                else:
                    df_filtered = df_chunk.copy()
                # Apply types and append
                if not df_filtered.empty:
                    # Step 1: Cast category columns via string[pyarrow] first to handle None/mixed types
                    for col in category_cols:
                        if col in df_filtered.columns:
                            df_filtered[col] = pd.Categorical(df_filtered[col])
                    # Step 2: Apply remaining column types, skipping category cols already handled
                    valid_column_types = {
                        col: dtype for col, dtype in column_types.items()
                        if col in df_filtered.columns and col not in category_cols
                    }
                    df_filtered = df_filtered.astype(valid_column_types)
                    df_list.append(df_filtered)
                # Clear chunk from memory immediately
                del df_chunk, df_filtered
                gc.collect()
            # Clear the dataset object from memory before the next year
            del dataset
            gc.collect()
        except Exception as e:
            print(f"Error processing {filepath}: {e}")
    if not df_list:
        return pd.DataFrame()
    # Combine chunks
    final_df = pd.concat(df_list, ignore_index=True)
    for col in category_cols:
        if col in final_df.columns:
            final_df[col] = pd.Categorical(final_df[col])
    # Destroy the list
    del df_list
    gc.collect()
    return final_df
```

```python
def load_full():
    dataset_full_no_optimization = load_dataset("amylonidis/PatClass2011",split="train",sep=";",on_bad_lines="skip")
    df_full_no_optimization = dataset_full_no_optimization.to_pandas()
    return df_full_no_optimization
```

Load All Columns

```python
df = load_full()
```

Load Selected Columns


```python
columns_to_keep = [
    "applicants",
    "lang"
]
start_date = "1985-03-01"
end_date = "1985-04-30"
df = load_temporal_optimized(start_date, end_date, columns_to_keep)
```

## Loading Script Benchmarks

The following Google Colab notebooks present performance metrics regarding the custom Loading Script performed on different environments

- [Google Colab Environment](https://colab.research.google.com/drive/1RbRJIek_aoEtTVvRYUqc8t5SrGpPR665?usp=sharing)
- [High Performance Server Environment](https://colab.research.google.com/drive/1xEYApVr3jnPTuNx8Xb0xwe0qEw4-gKFZ?usp=sharing)
- [Standard Workstation Environment](https://colab.research.google.com/drive/1sw0ldf1CL8r9rJEU4Sa51WghTJmW502H?usp=sharing)

## Google Colab Analytics


You can also use the following Google Colab notebooks to explore the Analytics that were performed to the dataset.

- [Date Analytics](https://colab.research.google.com/drive/17KXRabtBLPiPmgVIl2N30MoRqzOqlX-N?usp=sharing)
- [Applicant - Inventor Name Analytics](https://colab.research.google.com/drive/1arh4oQ5C9BquulFX-cqsb0lmHgjS5Vcx?usp=sharing)
- [Main Codes Analytics](https://colab.research.google.com/drive/1utNvvmCUGrr81SNyMam2vWqyFUp1fFEq?usp=sharing)
- [Section Title Analytics](https://colab.research.google.com/drive/1hDk25NZykpxenLg1Hck-0J_y6qHWHKOn?usp=sharing)
- [Section Abstract Analytics](https://colab.research.google.com/drive/1a16PS_1KyhhwXSoFJnkjRlVFBn4EQyR1?usp=sharing)
- [Section Claims Analytics](https://colab.research.google.com/drive/1MkrTpMY9u5CyhYIE5MD3MBWbZ0_2GO1w?usp=sharing)
- [Section Description Analytics](https://colab.research.google.com/drive/1D3MPzb1LVFLdnIjuJcD5OJTwuvYz_c5J?usp=sharing)


## Dataset Creation

### Source Data

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.

### 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

This dataset is distributed under the [MIT License](https://opensource.org/licenses/MIT). Users are free to use, modify, and distribute the dataset, provided that the original authors are credited.

## Citation

If you utilize the PatClass2011 dataset in your research or applications, please cite it appropriately.

---

	---
	license: mit
	language:
	- en,
	tags:
	- legal
	- patents
	pretty_name: PatClass2011
	size_categories:
	- 10B<n<100B
	---
	# 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 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

	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


	### Data Fields

	The dataset is provided in CSV format and includes the aforementioned fields

	- `ucid`: Unique identifier for the patent document.
	- `patent_number`: Patent document number.
	- `country`: Country code of the patent.
	- `kind`: Kind code indicating the type of patent document.
	- `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 inserted in the dataset.
	- `status`: Status of the patent document.
	- `main_code`: Primary classification code assigned to the patent.
	- `further_codes`: Additional classification codes.
	- `ipcr_codes`: International Patent Classification codes.
	- `ecla_codes`: European Classification codes.
	- `title`: Title of the patent document.
	- `abstract`: Abstract summarizing the patent.
	- `description`: Detailed description of the patent.
	- `claims`: Claims defining the scope of the patent protection.
	- `applicants`: Entities or individuals who applied for the patent.
	- `inventors`: Inventors credited in the patent document.

	## Usage

	## Loading the Dataset

	### 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, with all columns and once with selected columns.


	```python

	from datasets import load_dataset
	import pandas as pd
	from datetime import datetime
	import gc

	def load_temporal_optimized(start_date, end_date, columns_to_keep=None):
	"""
	Load only the necessary CSV files from a Hugging Face dataset repository.

	:param start_date: str, the start date in 'YYYY-MM-DD' format (inclusive)
	:param end_date: str, the end date in 'YYYY-MM-DD' format (inclusive)
	:param columns_to_keep: list of str, optional. Specific columns to load (e.g., ["date", "lang"]).
	:return: pd.DataFrame, combined data from selected CSVs
	"""

	huggingface_dataset_name = "amylonidis/PatClass2011"

	column_types = {
	"ucid": "string[pyarrow]",
	"country": "category",
	"kind": "category",
	"lang": "category",
	"date": "Int32",
	"application_date": "Int32",
	"date_produced": "Int32",
	"status": "category",
	"main_code": "string[pyarrow]",
	"further_codes": "string[pyarrow]",
	"ipcr_codes": "string[pyarrow]",
	"ecla_codes": "string[pyarrow]",
	"title": "string[pyarrow]",
	"abstract": "string[pyarrow]",
	"description": "string[pyarrow]",
	"claims": "string[pyarrow]",
	"applicants": "string[pyarrow]",
	"inventors": "string[pyarrow]",
	"patent_number": "Int64",
	}

	category_cols = {"country", "kind", "lang", "status"}

	dataset_years = [str(year) for year in range(1978, 2006)]

	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"))

	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 if f in given_years]

	if not matching_years:
	raise ValueError(f"No matching CSV files found for {start_date} to {end_date}")

	df_list = []

	for year in matching_years:
	filepath = f"data/years/{year}/clefip2011_en_classification_{year}_validated.csv"

	try:
	# 1. Load the dataset (This stays on disk as an Arrow memory-map, NOT in RAM)
	dataset = load_dataset(
	huggingface_dataset_name,
	data_files=filepath,
	split="train",
	sep=";",
	on_bad_lines="skip"
	)

	# Select Columns Before Chunking
	if columns_to_keep is not None:
	# Safety check: Only select columns that actually exist in this specific file
	valid_columns = [col for col in columns_to_keep if col in dataset.column_names]
	if valid_columns:
	dataset = dataset.select_columns(valid_columns)

	# 2. Tell HuggingFace to output Pandas dataframes when sliced
	dataset = dataset.with_format("pandas")

	# 3. CHUNKING: Process exactly 40,000 rows at a time
	chunk_size = 10000
	for i in range(0, len(dataset), chunk_size):
	# Only these 40,000 rows are loaded into RAM
	df_chunk = dataset[i : i + chunk_size]
	df_chunk.columns = df_chunk.columns.str.strip()

	# Filter this specific chunk
	if "date" in df_chunk.columns:
	temp_dates = pd.to_numeric(df_chunk["date"], errors="coerce")
	mask = (temp_dates >= start_date_int) & (temp_dates <= end_date_int)
	df_filtered = df_chunk[mask].copy()
	else:
	df_filtered = df_chunk.copy()

	# Apply types and append
	if not df_filtered.empty:
	# Step 1: Cast category columns via string[pyarrow] first to handle None/mixed types
	for col in category_cols:
	if col in df_filtered.columns:
	df_filtered[col] = pd.Categorical(df_filtered[col])

	# Step 2: Apply remaining column types, skipping category cols already handled
	valid_column_types = {
	col: dtype for col, dtype in column_types.items()
	if col in df_filtered.columns and col not in category_cols
	}
	df_filtered = df_filtered.astype(valid_column_types)
	df_list.append(df_filtered)

	# Clear chunk from memory immediately
	del df_chunk, df_filtered
	gc.collect()

	# Clear the dataset object from memory before the next year
	del dataset
	gc.collect()

	except Exception as e:
	print(f"Error processing {filepath}: {e}")

	if not df_list:
	return pd.DataFrame()

	# Combine chunks
	final_df = pd.concat(df_list, ignore_index=True)
	for col in category_cols:
	if col in final_df.columns:
	final_df[col] = pd.Categorical(final_df[col])
	# Destroy the list
	del df_list
	gc.collect()

	return final_df


	```

	```python
	def load_full():
	dataset_full_no_optimization = load_dataset("amylonidis/PatClass2011",split="train",sep=";",on_bad_lines="skip")
	df_full_no_optimization = dataset_full_no_optimization.to_pandas()

	return df_full_no_optimization
	```

	Load All Columns

	```python

	df = load_full()


	```

	Load Selected Columns


	```python

	columns_to_keep = [
	"applicants",
	"lang"
	]

	start_date = "1985-03-01"
	end_date = "1985-04-30"

	df = load_temporal_optimized(start_date, end_date, columns_to_keep)

	```

	## Loading Script Benchmarks

	The following Google Colab notebooks present performance metrics regarding the custom Loading Script performed on different environments

	- [Google Colab Environment](https://colab.research.google.com/drive/1RbRJIek_aoEtTVvRYUqc8t5SrGpPR665?usp=sharing)
	- [High Performance Server Environment](https://colab.research.google.com/drive/1xEYApVr3jnPTuNx8Xb0xwe0qEw4-gKFZ?usp=sharing)
	- [Standard Workstation Environment](https://colab.research.google.com/drive/1sw0ldf1CL8r9rJEU4Sa51WghTJmW502H?usp=sharing)

	## Google Colab Analytics


	You can also use the following Google Colab notebooks to explore the Analytics that were performed to the dataset.

	- [Date Analytics](https://colab.research.google.com/drive/17KXRabtBLPiPmgVIl2N30MoRqzOqlX-N?usp=sharing)
	- [Applicant - Inventor Name Analytics](https://colab.research.google.com/drive/1arh4oQ5C9BquulFX-cqsb0lmHgjS5Vcx?usp=sharing)
	- [Main Codes Analytics](https://colab.research.google.com/drive/1utNvvmCUGrr81SNyMam2vWqyFUp1fFEq?usp=sharing)
	- [Section Title Analytics](https://colab.research.google.com/drive/1hDk25NZykpxenLg1Hck-0J_y6qHWHKOn?usp=sharing)
	- [Section Abstract Analytics](https://colab.research.google.com/drive/1a16PS_1KyhhwXSoFJnkjRlVFBn4EQyR1?usp=sharing)
	- [Section Claims Analytics](https://colab.research.google.com/drive/1MkrTpMY9u5CyhYIE5MD3MBWbZ0_2GO1w?usp=sharing)
	- [Section Description Analytics](https://colab.research.google.com/drive/1D3MPzb1LVFLdnIjuJcD5OJTwuvYz_c5J?usp=sharing)


	## Dataset Creation

	### Source Data

	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.

	### 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

	This dataset is distributed under the [MIT License](https://opensource.org/licenses/MIT). Users are free to use, modify, and distribute the dataset, provided that the original authors are credited.

	## Citation

	If you utilize the PatClass2011 dataset in your research or applications, please cite it appropriately.

	---