Upload preprocess_data.py

Code to generate new columns from original data

preprocess_data.py

@@ -0,0 +1,128 @@
+### Code to generate dataset
+
+# %%
+from datasets import load_dataset
+
+dataset = load_dataset("conll2003")
+
+# %%
+dataset
+
+# %%
+dataset['train'][0]['tokens']
+
+# %%
+ner_tags= {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
+
+# %%
+# Swap keys and values using dictionary comprehension
+swapped_dict = {v: k for k, v in ner_tags.items()}
+
+# Print the swapped dictionary
+print(swapped_dict)
+
+# %%
+[swapped_dict[x] for x in dataset['train'][0]['ner_tags']]
+
+# %%
+dataset['train'][0]
+
+# %%
+def label_tokens(entry):
+    entry['ner_labels'] = [swapped_dict[x] for x in entry['ner_tags']]
+    return entry
+
+
+# %%
+dataset['train'] = dataset["train"].map(label_tokens)
+dataset['test'] = dataset["test"].map(label_tokens)
+dataset['validation'] = dataset["validation"].map(label_tokens)
+
+
+# %%
+def tokens_to_sentence(entry):
+    entry['sentence'] = ' '.join(entry['tokens'])
+    return entry
+
+dataset['train'] = dataset["train"].map(tokens_to_sentence)
+dataset['test'] = dataset["test"].map(tokens_to_sentence)
+dataset['validation'] = dataset["validation"].map(tokens_to_sentence)
+
+
+# %%
+def extract_entities(entry):
+    entities = {'PER': [], 'ORG': [], 'LOC': [], 'MISC': []}
+    current_entity = {"type": None, "words": []}
+    for word, label in zip(entry['sentence'].split(), entry['ner_labels']):
+        if label.startswith('B-'):
+            entity_type = label.split('-')[1]
+            if current_entity["type"] == entity_type:
+                entities[entity_type].append(' '.join(current_entity["words"]))
+                current_entity["words"] = [word]
+            else:
+                if current_entity["type"] is not None:
+                    entities[current_entity["type"]].append(' '.join(current_entity["words"]))
+                current_entity = {"type": entity_type, "words": [word]}
+        elif label.startswith('I-'):
+            if current_entity["type"] is not None:
+                current_entity["words"].append(word)
+        else:
+            if current_entity["type"] is not None:
+                entities[current_entity["type"]].append(' '.join(current_entity["words"]))
+            current_entity = {"type": None, "words": []}
+    if current_entity["type"] is not None:
+        entities[current_entity["type"]].append(' '.join(current_entity["words"]))
+
+    entry['entities'] = entities
+    return entry
+
+# Extract entities
+dataset['train'] = dataset["train"].map(extract_entities)
+dataset['test'] = dataset["test"].map(extract_entities)
+dataset['validation'] = dataset["validation"].map(extract_entities)
+
+
+
+# %%
+dataset['train'][10]['sentence'], dataset['train'][10]['entities']
+
+# %%
+dataset.push_to_hub("areias/conll2003-generative")
+
+# %%
+from collections import Counter
+
+def get_count(entries):
+    # Initialize counters for each entity type
+    per_counter = Counter()
+    org_counter = Counter()
+    loc_counter = Counter()
+    misc_counter = Counter()
+
+    # Count the occurrences of each type of entity
+    for item in entries:
+        per_counter.update(item['entities']['PER'])
+        org_counter.update(item['entities']['ORG'])
+        loc_counter.update(item['entities']['LOC'])
+        misc_counter.update(item['entities']['MISC'])
+
+    # Print the counts for each type of entity
+    print("Total PER entities:", sum(per_counter.values()))
+    print("Total ORG entities:", sum(org_counter.values()))
+    print("Total LOC entities:", sum(loc_counter.values()))
+    print("Total MISC entities:", sum(misc_counter.values()))
+
+
+# %%
+get_count(dataset['train'])
+
+# %%
+get_count(dataset['test'])
+
+# %%
+get_count(dataset['validation'])
+
+# %%
+
+
+