
# Dataset Card for "tner/fin"

## Dataset Description

- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
- **Dataset:** FIN
- **Domain:** Financial News
- **Number of Entity:** 4

### Dataset Summary
FIN NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.


- Entity Types: `ORG`, `LOC`, `PER`, `MISC`

## Dataset Structure

### Data Instances
An example of `train` looks as follows.

```
{
    "tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}
```

### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json).
```python
{
  "O": 0,
  "B-PER": 1,
  "B-LOC": 2,
  "B-ORG": 3,
  "B-MISC": 4,
  "I-PER": 5,
  "I-LOC": 6,
  "I-ORG": 7,
  "I-MISC": 8
}
```

### Data Splits

|  name   |train|validation|test|
|---------|----:|---------:|---:|
|fin      |1014 |       303| 150|


### Citation Information

```
@inproceedings{salinas-alvarado-etal-2015-domain,
    title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
    author = "Salinas Alvarado, Julio Cesar  and
      Verspoor, Karin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://aclanthology.org/U15-1010",
    pages = "84--90",
}
```
task_categories:token-classification task_ids:named-entity-recognition multilinguality:monolingual size_categories:1K<n<10K language:en license:mit region:us
{"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "FIN"}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-mit #region-us 

Dataset Card for "tner/fin"
===========================


Dataset Description
-------------------


* Repository: T-NER
* Paper: URL
* Dataset: FIN
* Domain: Financial News
* Number of Entity: 4


### Dataset Summary


FIN NER dataset formatted in a part of TNER project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.


* Entity Types: 'ORG', 'LOC', 'PER', 'MISC'


Dataset Structure
-----------------


### Data Instances


An example of 'train' looks as follows.


### Label ID


The label2id dictionary can be found at here.


### Data Splits




### Dataset Summary


FIN NER dataset formatted in a part of TNER project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.


* Entity Types: 'ORG', 'LOC', 'PER', 'MISC'


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-mit #region-us 
 ### Dataset Summary


FIN NER dataset formatted in a part of TNER project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.


* Entity Types: 'ORG', 'LOC', 'PER', 'MISC'


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits