Create generation_script.py

generation_script.py

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+# Con el output del generation script de https://huggingface.co/datasets/luisgasco/profner_ner_master
+# Y añadiendo los archivos valid.tsv y train.tsv de la task 1 del dataset de Profner
+
+import pandas as pd
+from collections import defaultdict
+from datasets import Dataset, DatasetDict
+
+# 1. Cargar TSVs de etiquetas
+df_train_labels = pd.read_csv("/content/train.tsv", sep="\t")
+df_valid_labels = pd.read_csv("/content/valid.tsv", sep="\t")
+
+# Unificar etiquetas en un solo dict
+labels_dict = dict(zip(df_train_labels["tweet_id"], df_train_labels["label"]))
+labels_dict.update(dict(zip(df_valid_labels["tweet_id"], df_valid_labels["label"])))
+
+# 2. Cargar IDs de cada split
+def load_ids(path):
+    with open(path, encoding="utf-8") as f:
+        return set(line.strip() for line in f if line.strip())
+
+train_ids = load_ids("/content/train_ids.txt")
+dev_ids = load_ids("/content/dev_ids.txt")
+test_ids = load_ids("/content/test_ids.txt")
+
+labels_dict = {str(k): v for k, v in labels_dict.items()}
+train_ids = set(str(id_) for id_ in train_ids)
+dev_ids = set(str(id_) for id_ in dev_ids)
+test_ids = set(str(id_) for id_ in test_ids)
+# 3. Leer los archivos .spacy estilo CoNLL (train + valid juntos)
+def cargar_textos_conll(path):
+    textos = defaultdict(list)
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            if line.strip():
+                parts = line.strip().split()
+                if len(parts) == 5:
+                    token, doc_id, *_ = parts
+                    textos[doc_id].append(token)
+    return textos
+
+textos_train = cargar_textos_conll("/content/train_spacy.txt")
+textos_valid = cargar_textos_conll("/content/valid_spacy.txt")
+textos = {**textos_train, **textos_valid}
+
+# 4. Construir datasets por split
+def construir_split(ids):
+    data = []
+    for doc_id in ids:
+        if doc_id in textos and doc_id in labels_dict:
+            text = " ".join(textos[doc_id])
+            label = int(labels_dict[doc_id])
+            data.append({"tweet_id": doc_id, "text": text, "label": label})
+    return Dataset.from_list(data)
+
+# 5. Crear DatasetDict
+dataset = DatasetDict({
+    "train": construir_split(train_ids),
+    "validation": construir_split(dev_ids),
+    "test": construir_split(test_ids),
+})
+
+from datasets import ClassLabel, Features, Value
+
+# Definir las etiquetas de texto
+label_names = ["SIN_PROFESION", "CON_PROFESION"]
+
+# Crear esquema con ClassLabel
+features = Features({
+    "tweet_id": Value("string"),
+    "text": Value("string"),
+    "label": ClassLabel(names=label_names)
+})
+
+# Aplicar el esquema a cada división
+for split in dataset:
+    dataset[split] = dataset[split].cast(features)