luisgasco/profner_ner_master

NER-PROFESION Dataset (Based on PROFNER)

This dataset is built upon the original PROFNER corpus, which focuses on Named Entity Recognition (NER) of professions in Spanish.

To ensure consistency and simplicity, the original fine-grained entity tags have been normalized into the following three labels:

• B-PROFESION: beginning of a profession entity
• I-PROFESION: continuation of a profession entity
• O: outside any named entity

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📦 Dataset Structure

The dataset is provided as a Hugging Face DatasetDict with the following splits:

Split	Description
train	Balanced: approximately 50% of the documents contain at least one B-PROFESION tag, while the other 50% contain no profession entities.
validation	Balanced with respect to the number of documents containing profession entities, matching the test split.
test	Also balanced: half of the documents contain at least one B-PROFESION, half contain none.

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⚙️ Dataset Generation

Two utility functions were used to prepare the dataset:

procesar_training_set_balanceado

This function loads the original training data and performs the following steps:

1. Groups the data by document ID.
2. Splits documents into two groups: those containing at least one B- label (indicating a profession) and those without.
3. Selects an equal number of documents from both groups to ensure a 50/50 balance between positive and negative samples.
4. Converts the documents into CoNLL-style format: one token-label pair per line, separated by empty lines between documents.

procesar_dev_test_balanceado

This function splits the original development set into two subsets:

1. Documents are separated into two groups:
   • With at least one B- tag
   • Without any B- tag
2. Each group is split equally into two halves:
   • Half goes into the validation set
   • Half goes into the test set
3. This guarantees that both validation and test are balanced with respect to the presence of annotated profession entities.

The selected document IDs are stored separately (dev_ids.txt and test_ids.txt) for reproducibility.

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🧾 Format

Each instance is a dictionary with:

• tokens: list of tokenized words
• ner_tags: list of integer-encoded entity labels

The ner_tags feature uses Hugging Face's ClassLabel, which maps integers to string labels:

label_list = ["B-PROFESION", "I-PROFESION", "O"]

Example:

{
  "tokens": ["Nuestros", "colaboradores", "y", "conductores"],
  "ner_tags": [0, 1, 2, 0]  # Using ClassLabel -> ["B-PROFESION", "I-PROFESION", "O", "B-PROFESION"]
}

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🔁 Label Mappings

The dataset includes a label_mappings.json file with:

{
  "label_list": ["B-PROFESION", "I-PROFESION", "O"],
  "label2id": {
    "B-PROFESION": 0,
    "I-PROFESION": 1,
    "O": 2
  },
  "id2label": {
    "0": "B-PROFESION",
    "1": "I-PROFESION",
    "2": "O"
  }
}

These can be loaded to configure a model or to interpret predictions.

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📥 Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("luisgasco/profner_ner_master")
tokens = dataset["train"][0]["tokens"]
tags = dataset["train"].features["ner_tags"].feature.int2str(dataset["train"][0]["ner_tags"])

print(list(zip(tokens, tags)))

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🔗 Original Data Source

This dataset is based on the PROFNER corpus:

• Zenodo: LINK
• Original task website

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✍️ Author

This version has been processed and curated by Luis Gasco, based on the PROFNER dataset, for educational purposes.

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