README added

README.md

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+# NER for Icelandic - MIM-GOLD-NER splits
+
+## MIM-GOLD-NER
+
+The original MIM-GOLD-NER data is found at http://hdl.handle.net/20.500.12537/42 
+
+This repository packages the data for use with the Datasets library from hugginface.
+
+## Splits
+
+Since the original data does not have train, dev and test splits there was a need to create them. See `create_splits.py` for how that was done.
+
+

create_splits.py

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+#
+# want data from all documents
+# want data from all classes
+# 
+
+file_names = [
+    "adjudications.txt",
+    "blog.txt",
+    "books.txt",
+    "emails.txt",
+    "fbl.txt",
+    "laws.txt",
+    "mbl.txt",
+    "radio_tv_news.txt",
+    "school_essays.txt",
+    "scienceweb.txt",
+    "webmedia.txt",
+    "websites.txt",
+    "written-to-be-spoken.txt"
+]
+
+def read_file(file_name):
+    data = []
+    sentence = []
+    with open(file_name) as fh:
+        for line in fh.readlines():
+            if not line.strip() and sentence:
+                data.append(sentence)
+                sentence = []
+                continue
+            w, t = line.strip().split()
+            sentence.append((w, t))
+    return data
+
+from collections import defaultdict
+def calc_stats(data):
+    stats = defaultdict(int)
+    for sent in data:
+        stats["n_sentences"] += 1
+        for token, label in sent:
+            stats[label] += 1
+    return stats
+
+
+import pprint
+def get_total_stats():
+    total_stats = defaultdict(int)
+    for file_name in file_names:
+        d = read_file("data/"+file_name)
+        stats = calc_stats(d)
+        #print(f"--- [{file_name}]---")
+        #pprint.pprint(stats)
+        for k, v in stats.items():
+            total_stats[k] += v
+        #print("---- TOTAL ---- ")
+        #pprint.pprint(total_stats)
+    return total_stats
+
+import random
+random.seed(1)
+
+def check_if_not_done(stats, total_stats, target):
+    for k, v in total_stats.items():
+        if v * target > stats[k]:
+            return True
+    return False
+
+def create_splits(train=0.8, test=0.1, dev=0.1):
+    train_data = []
+    test_data = []
+    dev_data = []
+
+    total_stats = get_total_stats()
+
+    for file_name in file_names:
+        train_stats = defaultdict(int)
+        test_stats = defaultdict(int)
+        dev_stats = defaultdict(int)
+ 
+        d = read_file("data/"+file_name)       
+        stats = calc_stats(d)
+        random.shuffle(d)
+
+        file_train = []
+        file_test = []
+        file_dev = []
+
+        for sent in d:
+            if check_if_not_done(test_stats, stats, test):
+                # TEST data
+                use = False
+                for token in sent:
+                    w, tag = token
+                    if tag == 'O':
+                        continue
+                    if test_stats[tag] < test * stats[tag] - 5:
+                        use = True
+                if test_stats['n_sentences'] < test * stats['n_sentences'] - 5:
+                    use = True
+                if use:
+                    file_test.append(sent)
+                    test_stats['n_sentences'] += 1
+                    for w, t in sent:
+                        test_stats[t] += 1                    
+                elif check_if_not_done(dev_stats, stats, dev):
+                    # DEV DATA
+                    use = False
+                    for token in sent:
+                        w, tag = token
+                        if tag == 'O':
+                            continue
+                        if dev_stats[tag] < dev * stats[tag] - 5:
+                            use = True
+                    if dev_stats['n_sentences'] < dev * stats['n_sentences'] - 5:
+                        use = True
+                    if use:
+                        file_dev.append(sent)
+                        dev_stats['n_sentences'] += 1
+                        for w, t in sent:
+                            dev_stats[t] += 1
+                    else:
+                        file_train.append(sent)
+                        train_stats['n_sentences'] += 1
+                        for w, t in sent:
+                            train_stats[t] += 1
+                else:
+                    file_train.append(sent)
+                    train_stats['n_sentences'] += 1
+                    for w, t in sent:
+                        train_stats[t] += 1
+        try:
+            assert len(d) == len(file_train) + len(file_dev) + len(file_test)
+        except:
+            import pdb; pdb.set_trace()
+        train_data += file_train
+        test_data += file_test
+        dev_data += file_dev
+
+    return train_data, test_data, dev_data
+
+train, test, dev = create_splits()
+
+total_stats = get_total_stats()
+print("---- total -----")
+pprint.pprint(total_stats)
+print("----- test ----")
+test_stats = calc_stats(test)
+pprint.pprint(test_stats)
+print("----- dev ----")
+dev_stats = calc_stats(dev)
+pprint.pprint(dev_stats)
+print("----- train ----")
+train_stats = calc_stats(train)
+pprint.pprint(train_stats)
+
+
+with open("train.txt", "w") as outf:
+    for sent in train:
+        for w, t in sent:
+            outf.writelines(f"{w} {t}\n")
+        outf.writelines("\n")
+
+
+with open("test.txt", "w") as outf:
+    for sent in test:
+        for w, t in sent:
+            outf.writelines(f"{w} {t}\n")
+        outf.writelines("\n")
+
+
+with open("dev.txt", "w") as outf:
+    for sent in dev:
+        for w, t in sent:
+            outf.writelines(f"{w} {t}\n")
+        outf.writelines("\n")