README.md

metadata

dataset_info:
  features:
    - name: tweet_id
      dtype: string
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': SIN_PROFESION
            '1': CON_PROFESION
  splits:
    - name: train
      num_bytes: 711780
      num_examples: 2786
    - name: validation
      num_bytes: 238488
      num_examples: 999
    - name: test
      num_bytes: 242754
      num_examples: 1001
  download_size: 807660
  dataset_size: 1193022
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Binary Classification Dataset: Profession Detection in Tweets

This dataset is a derived version of the original PROFNER task, adapted for binary text classification. The goal is to determine whether a tweet mentions a profession or not.

🧠 Objective

Each example contains:

• A tweet_id (document identifier)
• A text field (full tweet content)
• A label, which has been normalized into two classes:
  • CON_PROFESION: The tweet contains a reference to a profession.
  • SIN_PROFESION: The tweet does not contain any profession-related term.

📦 Dataset Structure

The dataset is formatted as a DatasetDict with three splits:

Split	Description
train	Balanced dataset containing both classes
validation	Contains equal distribution of profession/no-profession
test	Also balanced for evaluating binary classification

Each example follows the structure:

{
  "tweet_id": "1242399976644325376",
  "text": "Nuestros colaboradores y conductores se quedan en casa!",
  "label": "CON_PROFESION"  # or "SIN_PROFESION"
}

The label column is implemented with Hugging Face ClassLabel, which makes it easy to convert between string and integer representation.

🔄 Label Mapping

The dataset uses the following class labels:

label_list = ["SIN_PROFESION", "CON_PROFESION"]
label2id = { "SIN_PROFESION": 0, "CON_PROFESION": 1 }
id2label = { 0: "SIN_PROFESION", 1: "CON_PROFESION" }

These are automatically applied via Hugging Face datasets.Features.

📥 How to Load

from datasets import load_dataset

ds = load_dataset("luisgasco/profner_classification_master")
print(ds["train"][0])
# Show features
print(ds["train"].features)
# Ver etiquetas as strings para un ejemplo:
example = ds["train"][5]
print(example["label"])  # IDs
print(ds["train"].features["label"].int2str(example["label"]))

✍️ Author

Processed and Luis Gasco for educational purposes, based on the PROFNER corpus.