
# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736)
- **Repository:** [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction)
- **Paper:** [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Brand-Product Relation Extraction Corpora in Polish

### Supported Tasks and Leaderboards

NER, Entity linking

### Languages

Polish

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

- id: int identifier of a text
- text: string text, for example a consumer comment on the social media
- ner: extracted entities and their relationship
    - source and target: a pair of entities identified in the text
        - from: int value representing starting character of the entity
        - text: string value with the entity text
        - to: int value representing end character of the entity
        - type: one of pre-identified entity types:
            - PRODUCT_NAME
            - PRODUCT_NAME_IMP
            - PRODUCT_NO_BRAND
            - BRAND_NAME
            - BRAND_NAME_IMP
            - VERSION
            - PRODUCT_ADJ
            - BRAND_ADJ
            - LOCATION
            - LOCATION_IMP


### Data Splits

No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:
- tele
- electro
- cosmetics
- banking

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information
```
@inproceedings{inproceedings,
author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
year = {2020},
month = {05},
pages = {},
title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
}
```

### Contributions

Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.
task_categories:text-retrieval task_ids:entity-linking-retrieval annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:pl license:unknown region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["pl"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["entity-linking-retrieval"], "pretty_name": "bprec", "dataset_info": [{"config_name": "default", "features": [{"name": "id", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "tele", "num_bytes": 2739015, "num_examples": 2391}, {"name": "electro", "num_bytes": 125999, "num_examples": 382}, {"name": "cosmetics", "num_bytes": 1565263, "num_examples": 2384}, {"name": "banking", "num_bytes": 446944, "num_examples": 561}], "download_size": 8006167, "dataset_size": 4877221}, {"config_name": "all", "features": [{"name": "id", "dtype": "int32"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "train", "num_bytes": 4937658, "num_examples": 5718}], "download_size": 8006167, "dataset_size": 4937658}, {"config_name": "tele", "features": [{"name": "id", "dtype": "int32"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "train", "num_bytes": 2758147, "num_examples": 2391}], "download_size": 4569708, "dataset_size": 2758147}, {"config_name": "electro", "features": [{"name": "id", "dtype": "int32"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "train", "num_bytes": 130205, "num_examples": 382}], "download_size": 269917, "dataset_size": 130205}, {"config_name": "cosmetics", "features": [{"name": "id", "dtype": "int32"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "train", "num_bytes": 1596259, "num_examples": 2384}], "download_size": 2417388, "dataset_size": 1596259}, {"config_name": "banking", "features": [{"name": "id", "dtype": "int32"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "ner", "sequence": [{"name": "source", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}, {"name": "target", "struct": [{"name": "from", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "to", "dtype": "int32"}, {"name": "type", "dtype": {"class_label": {"names": {"0": "PRODUCT_NAME", "1": "PRODUCT_NAME_IMP", "2": "PRODUCT_NO_BRAND", "3": "BRAND_NAME", "4": "BRAND_NAME_IMP", "5": "VERSION", "6": "PRODUCT_ADJ", "7": "BRAND_ADJ", "8": "LOCATION", "9": "LOCATION_IMP"}}}}]}]}], "splits": [{"name": "train", "num_bytes": 453119, "num_examples": 561}], "download_size": 749154, "dataset_size": 453119}]}
TAGS
#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-unknown #region-us 


# Dataset Card for [Dataset Name]

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: bprec homepage
- Repository: bprec repository
- Paper: bprec paper
- Leaderboard:
- Point of Contact:

### Dataset Summary

Brand-Product Relation Extraction Corpora in Polish

### Supported Tasks and Leaderboards

NER, Entity linking

### Languages

Polish

## Dataset Structure

### Data Instances



### Data Fields

- id: int identifier of a text
- text: string text, for example a consumer comment on the social media
- ner: extracted entities and their relationship
    - source and target: a pair of entities identified in the text
        - from: int value representing starting character of the entity
        - text: string value with the entity text
        - to: int value representing end character of the entity
        - type: one of pre-identified entity types:
            - PRODUCT_NAME
            - PRODUCT_NAME_IMP
            - PRODUCT_NO_BRAND
            - BRAND_NAME
            - BRAND_NAME_IMP
            - VERSION
            - PRODUCT_ADJ
            - BRAND_ADJ
            - LOCATION
            - LOCATION_IMP


### Data Splits

No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:
- tele
- electro
- cosmetics
- banking

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information






### Contributions

Thanks to @kldarek for adding this dataset.
# Dataset Card for [Dataset Name] ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: bprec homepage
- Repository: bprec repository
- Paper: bprec paper
- Leaderboard:
- Point of Contact: ### Dataset Summary

Brand-Product Relation Extraction Corpora in Polish ### Supported Tasks and Leaderboards

NER, Entity linking ### Languages

Polish ## Dataset Structure ### Data Instances ### Data Fields

- id: int identifier of a text
- text: string text, for example a consumer comment on the social media
- ner: extracted entities and their relationship
    - source and target: a pair of entities identified in the text
        - from: int value representing starting character of the entity
        - text: string value with the entity text
        - to: int value representing end character of the entity
        - type: one of pre-identified entity types:
            - PRODUCT_NAME
            - PRODUCT_NAME_IMP
            - PRODUCT_NO_BRAND
            - BRAND_NAME
            - BRAND_NAME_IMP
            - VERSION
            - PRODUCT_ADJ
            - BRAND_ADJ
            - LOCATION
            - LOCATION_IMP ### Data Splits

No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:
- tele
- electro
- cosmetics
- banking ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

Thanks to @kldarek for adding this dataset.
TAGS
#task_categories-text-retrieval #task_ids-entity-linking-retrieval #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-unknown #region-us 
 # Dataset Card for [Dataset Name] ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: bprec homepage
- Repository: bprec repository
- Paper: bprec paper
- Leaderboard:
- Point of Contact: ### Dataset Summary

Brand-Product Relation Extraction Corpora in Polish ### Supported Tasks and Leaderboards

NER, Entity linking ### Languages

Polish ## Dataset Structure ### Data Instances ### Data Fields

- id: int identifier of a text
- text: string text, for example a consumer comment on the social media
- ner: extracted entities and their relationship
    - source and target: a pair of entities identified in the text
        - from: int value representing starting character of the entity
        - text: string value with the entity text
        - to: int value representing end character of the entity
        - type: one of pre-identified entity types:
            - PRODUCT_NAME
            - PRODUCT_NAME_IMP
            - PRODUCT_NO_BRAND
            - BRAND_NAME
            - BRAND_NAME_IMP
            - VERSION
            - PRODUCT_ADJ
            - BRAND_ADJ
            - LOCATION
            - LOCATION_IMP ### Data Splits

No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:
- tele
- electro
- cosmetics
- banking ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

Thanks to @kldarek for adding this dataset.