---
configs:
  - config_name: holdout
    data_files:
      - split: train
        path: holdout/train.json
      - split: dev
        path: holdout/dev.json
      - split: test
        path: holdout/test.json
  - config_name: fold0
    data_files:
      - split: train
        path: 5folds/fold0/train.json
      - split: dev
        path: 5folds/fold0/dev.json
      - split: test
        path: 5folds/fold0/test.json
  - config_name: fold1
    data_files:
      - split: train
        path: 5folds/fold1/train.json
      - split: dev
        path: 5folds/fold1/dev.json
      - split: test
        path: 5folds/fold1/test.json
  - config_name: fold2
    data_files:
      - split: train
        path: 5folds/fold2/train.json
      - split: dev
        path: 5folds/fold2/dev.json
      - split: test
        path: 5folds/fold2/test.json
  - config_name: fold3
    data_files:
      - split: train
        path: 5folds/fold3/train.json
      - split: dev
        path: 5folds/fold3/dev.json
      - split: test
        path: 5folds/fold3/test.json
  - config_name: fold4
    data_files:
      - split: train
        path: 5folds/fold4/train.json
      - split: dev
        path: 5folds/fold4/dev.json
      - split: test
        path: 5folds/fold4/test.json
task_categories:
- token-classification
language:
- pt
tags:
- geological
- oil and gas
---

**Description**

This dataset is an updated version of the [GeoCorpus-3](https://github.com/Petroles/Petrovec/tree/master/GeoCorpus%20V3), originally described in [Portuguese word embeddings for the oil and gas industry: Development and evaluation
](https://www.sciencedirect.com/science/article/pii/S0166361520305819), where duplicates have been removed and annotation errors have been corrected. The updated corpus is thoroughly described in the paper "An Evaluation of Large Language Models for Geological Named Entity Recognition", which will be presented at the ICTAI 2024 conference. The preprint of the paper is available [here](https://www.researchgate.net/publication/383822506_An_Evaluation_of_Large_Language_Models_for_Geological_Named_Entity_Recognition).

**Dataset Structure**

The dataset is divided into two main subsets:

1. **Holdout:** A fixed train, dev, and test split, created using stratified sampling according to the methodology described [here](https://aclanthology.org/2024.propor-1.30/).
2. **Cross-Validation:** A 5-fold cross-validation setup, with each fold also created using stratified sampling, following the same methodology described [here](https://aclanthology.org/2024.propor-1.30/).

To access these subsets, you can use the following code:

```python
from datasets import load_dataset

# Load the holdout set
dataset_holdout = load_dataset("ronunes/GeoCorpus-3v2", name="holdout")

# Load a specific fold
dataset_fold0 = load_dataset("ronunes/GeoCorpus-3v2", name="fold0")
dataset_fold1 = load_dataset("ronunes/GeoCorpus-3v2", name="fold1")
# And so on for the other folds
```

**Additional Resources**

The code used for training and further experiments can be found in the [GitHub repository](https://github.com/RafaelOleques/geoner_ictai2024) associated with the paper. The repository also includes the corpus in CoNLL format for easy integration with various NLP tools.

**BibTeX**

Please cite the dataset using the following BibTeX entry:

```
@inproceedings{nunes2024geoner,
  author = {Nunes, Rafael and Spritzer, Andre and Balreira, Dennis and Freitas, Carla and Carbonera, Joel},
  year = {2024},
  pages = {},
  title = {An Evaluation of Large Language Models for Geological Named Entity Recognition}
  url={https://www.researchgate.net/publication/383822506_An_Evaluation_of_Large_Language_Models_for_Geological_Named_Entity_Recognition},
}
```