init

experiments/model_finetuning_ner.py

@@ -9,8 +9,9 @@ import json
 import logging
 import math
 import os
+import re
 from os.path import join as pj
-from shutil import copyfile
+from shutil import copyfile, rmtree
 from glob import glob
 
 import numpy as np
@@ -23,6 +24,28 @@ logging.basicConfig(format="%(asctime)s %(levelname)-8s %(message)s", level=logg
 EVAL_STEP = 500
 RANDOM_SEED = 42
 N_TRIALS = 10
+URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
+HANDLE_RE = re.compile(r"@\w+")
+
+
+def preprocess_bernice(text):
+    text = HANDLE_RE.sub("@USER", text)
+    text = URL_RE.sub("HTTPURL", text)
+    return text
+
+
+def preprocess_timelm(text):
+    text = HANDLE_RE.sub("@user", text)
+    text = URL_RE.sub("http", text)
+    return text
+
+
+def preprocess(model_name, text):
+    if model_name == "jhu-clsp/bernice":
+        return preprocess_bernice(text)
+    if "twitter-roberta-base" in model_name:
+        return preprocess_timelm(text)
+    return text
 
 
 def sigmoid(x):
@@ -66,8 +89,9 @@ def main(
         return new_labels
 
     def tokenize_and_align_labels(examples):
+        tokens = [[preprocess(model, w) for w in t] for t in examples["text_tokenized"]]
         tokenized_inputs = tokenizer(
-            examples["tokens"], truncation=True, is_split_into_words=True, padding="max_length", max_length=256
+            tokens, truncation=True, is_split_into_words=True, padding="max_length", max_length=256
         )
         all_labels = examples["ner_tags"]
         new_labels = []
@@ -174,6 +198,7 @@ def main(
             if not os.path.exists(f"{model_alias}/{os.path.basename(i)}"):
                 copyfile(i, f"{model_alias}/{os.path.basename(i)}")
         repo.push_to_hub()
+        rmtree(model_alias)
 
 
 if __name__ == "__main__":

experiments/model_finetuning_nerd.py

@@ -9,6 +9,7 @@ import json
 import logging
 import math
 import os
+import re
 from os.path import join as pj
 from shutil import copyfile, rmtree
 from glob import glob
@@ -23,6 +24,28 @@ logging.basicConfig(format="%(asctime)s %(levelname)-8s %(message)s", level=logg
 EVAL_STEP = 500
 RANDOM_SEED = 42
 N_TRIALS = 10
+URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
+HANDLE_RE = re.compile(r"@\w+")
+
+
+def preprocess_bernice(text):
+    text = HANDLE_RE.sub("@USER", text)
+    text = URL_RE.sub("HTTPURL", text)
+    return text
+
+
+def preprocess_timelm(text):
+    text = HANDLE_RE.sub("@user", text)
+    text = URL_RE.sub("http", text)
+    return text
+
+
+def preprocess(model_name, text):
+    if model_name == "jhu-clsp/bernice":
+        return preprocess_bernice(text)
+    if "twitter-roberta-base" in model_name:
+        return preprocess_timelm(text)
+    return text
 
 
 def sigmoid(x):
@@ -45,7 +68,9 @@ def main(
     dataset = load_dataset(dataset, dataset_type)
     tokenized_datasets = dataset.map(
         lambda x: tokenizer(
-            [f"[target] {a}, [definition] {b}, [text] {c}" for a, b, c in zip(x["target"], x["definition"], x["text"])],
+            [
+                preprocess(model, f"[target] {a}, [definition] {b}, [text] {c}")
+                for a, b, c in zip(x["target"], x["definition"], x["text"])],
             padding="max_length",
             truncation=True,
             max_length=256),
@@ -107,7 +132,7 @@ def main(
         test_split = ["test"]
         if dataset_type.endswith("temporal"):
             test_split += ["test_1", "test_2", "test_3", "test_4"]
-        summary_file = pj(output_dir, "summary.json")
+        summary_file = pj(best_model_path, "summary.json")
         if os.path.exists(summary_file):
             with open(summary_file) as f:
                 metric = json.load(f)

experiments/model_finetuning_topic.py

@@ -9,6 +9,7 @@ import json
 import logging
 import math
 import os
+import re
 from os.path import join as pj
 from shutil import copyfile, rmtree
 from glob import glob
@@ -46,6 +47,28 @@ ID2LABEL = {v: k for k, v in LABEL2ID.items()}
 EVAL_STEP = 500
 RANDOM_SEED = 42
 N_TRIALS = 10
+URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
+HANDLE_RE = re.compile(r"@\w+")
+
+
+def preprocess_bernice(text):
+    text = HANDLE_RE.sub("@USER", text)
+    text = URL_RE.sub("HTTPURL", text)
+    return text
+
+
+def preprocess_timelm(text):
+    text = HANDLE_RE.sub("@user", text)
+    text = URL_RE.sub("http", text)
+    return text
+
+
+def preprocess(model_name, text):
+    if model_name == "jhu-clsp/bernice":
+        return preprocess_bernice(text)
+    if "twitter-roberta-base" in model_name:
+        return preprocess_timelm(text)
+    return text
 
 
 def sigmoid(x):
@@ -67,7 +90,12 @@ def main(
     tokenizer = AutoTokenizer.from_pretrained(model)
     dataset = load_dataset(dataset, dataset_type)
     tokenized_datasets = dataset.map(
-        lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=256), batched=True
+        lambda x: tokenizer(
+            [preprocess(model, t) for t in x["text"]],
+            padding="max_length",
+            truncation=True,
+            max_length=256),
+        batched=True
     )
     tokenized_datasets = tokenized_datasets.rename_column("gold_label_list", "label")
     metric_accuracy = evaluate.load("accuracy", "multilabel")
@@ -79,6 +107,7 @@ def main(
         predictions = np.array([[int(sigmoid(j) > 0.5) for j in lo] for lo in logits])
         return metric_f1.compute(predictions=predictions, references=labels, average="micro")
 
+
     def compute_metric_all(eval_pred):
         logits, labels = eval_pred
         predictions = np.array([[int(sigmoid(j) > 0.5) for j in lo] for lo in logits])