Added poleval dataset

dvc.lock

@@ -5,11 +5,11 @@ stages:
       enron_spam --output_dir data/datasets/enron_spam
     deps:
     - path: experiments/scripts/download_dataset.py
-      md5: dfcc61ca00234b3dbe0e9c04697ae40a
-      size: 1686
+      md5: 9eb915fd5b9216965db519f686408a51
+      size: 887
     outs:
     - path: data/datasets/enron_spam/
-      md5: b2115d2a6901cd29727f9ed294196544.dir
+      md5: 66d44efedf37990b1989c81bbee085e0.dir
       size: 53096069
       nfiles: 3
   get_model@enron_spam:
@@ -17,7 +17,7 @@ stages:
       --output_dir data/models/enron_spam
     deps:
     - path: data/datasets/enron_spam
-      md5: b2115d2a6901cd29727f9ed294196544.dir
+      md5: 66d44efedf37990b1989c81bbee085e0.dir
       size: 53096069
       nfiles: 3
     - path: experiments/scripts/get_model.py
@@ -33,7 +33,7 @@ stages:
       --output_dir data/classification/enron_spam
     deps:
     - path: data/datasets/enron_spam/
-      md5: b2115d2a6901cd29727f9ed294196544.dir
+      md5: 66d44efedf37990b1989c81bbee085e0.dir
       size: 53096069
       nfiles: 3
     - path: data/models/enron_spam/
@@ -41,19 +41,19 @@ stages:
       size: 18505614
       nfiles: 6
     - path: experiments/scripts/classify.py
-      md5: 5bd1363bd8cb2742e5d8391a0287cddb
-      size: 1281
+      md5: 50f55b90eb47cbf448d83f8392dd37b6
+      size: 1102
     outs:
     - path: data/classification/enron_spam
-      md5: a83267cc1b9d8e210412b725f93902c0.dir
-      size: 326
-      nfiles: 1
+      md5: c7d42825b98b289f6a5ed3be1af14413.dir
+      size: 2763843
+      nfiles: 2
   explain@enron_spam:
     cmd: PYTHONPATH=. python experiments/scripts/explain.py --dataset_name enron_spam
       --output_dir data/explanations/enron_spam
     deps:
     - path: data/datasets/enron_spam
-      md5: b2115d2a6901cd29727f9ed294196544.dir
+      md5: 66d44efedf37990b1989c81bbee085e0.dir
       size: 53096069
       nfiles: 3
     - path: data/models/enron_spam
@@ -65,6 +65,6 @@ stages:
       size: 1445
     outs:
     - path: data/explanations/enron_spam/
-      md5: b05c04769355a99964e67e4b8a15f082.dir
-      size: 6269580
+      md5: 376bd1619c08b4989564788e74de8e06.dir
+      size: 7870394
       nfiles: 1

dvc.yaml

@@ -2,6 +2,7 @@ stages:
   download_dataset:
     foreach:
       - enron_spam
+      - poleval
     do:
       wdir: .
       cmd: >-

experiments/scripts/classify.py

@@ -3,10 +3,9 @@ from pathlib import Path
 
 import click
 import pandas as pd
-import torch
 from sklearn.metrics import classification_report
 
-from text_attacks.utils import get_model_and_tokenizer
+from text_attacks.utils import get_classify_function
 
 
 @click.command()
@@ -21,32 +20,25 @@ from text_attacks.utils import get_model_and_tokenizer
     type=click.Path(path_type=Path),
 )
 def main(
-        dataset_name: str,
-        output_dir: Path,
+    dataset_name: str,
+    output_dir: Path,
 ):
-    """Downloads the dataset to the output directory."""
+    """Classifies the test data and saves results to the output directory."""
     output_dir.mkdir(parents=True, exist_ok=True)
-
-    model, tokenizer = get_model_and_tokenizer(
+    classify = get_classify_function(
         dataset_name=dataset_name,
     )
     test = pd.read_json(f"data/datasets/{dataset_name}/test.jsonl", lines=True)
     test_x = test["text"].tolist()
     test_y = test["label"]
-    encoded_inputs = tokenizer(
-        test_x,
-        return_tensors="pt",
-        padding=True,
-        truncation=True,
-        max_length=512
-    )
-    logits = model(**encoded_inputs).logits
-    pred_y = torch.argmax(logits, dim=1).tolist()
-    pred_y = [model.config.id2label[p] for p in pred_y]
+    pred_y = classify(test_x)
 
     with open(output_dir / "metrics.txt", mode="wt") as fd:
         fd.write(classification_report(test_y, pred_y))
 
+    test["pred_label"] = pred_y
+    test.to_json(output_dir / "test.jsonl", orient="records", lines=True)
+
 
 if __name__ == "__main__":
     main()

experiments/scripts/download_dataset.py

@@ -2,29 +2,8 @@
 from pathlib import Path
 
 import click
-import pandas as pd
-from datasets import load_dataset
-from sklearn.model_selection import train_test_split
-
-
-def convert(dataset):
-    train = pd.DataFrame(dataset["train"].to_dict())
-    test = pd.DataFrame(dataset["test"].to_dict())
-
-    train["label"] = train["label_text"]
-    train = train.rename(columns={"message_id": "id"})
-    train = train.drop(columns=["label_text", "subject", "message", "date"])
-
-    test["label"] = test["label_text"]
-    test = test.rename(columns={"message_id": "id"})
-    test = test.drop(columns=["label_text", "subject", "message", "date"])
-    adversarial, test = train_test_split(
-        test,
-        test_size=0.9,
-        stratify=test["label"]
-    )
 
-    return train, test, adversarial
+from text_attacks.utils import download_dataset
 
 
 @click.command()
@@ -43,12 +22,9 @@ def main(
     output_dir: Path,
 ):
     """Downloads the dataset to the output directory."""
-    dataset_mappings = {
-        "enron_spam": "SetFit/enron_spam",
-    }
     output_dir.mkdir(parents=True, exist_ok=True)
-    dataset = load_dataset(dataset_mappings[dataset_name])
-    train, test, adversarial = convert(dataset)
+
+    train, test, adversarial = download_dataset(dataset_name)
     train.to_json(output_dir / "train.jsonl", orient="records", lines=True)
     test.to_json(output_dir / "test.jsonl", orient="records", lines=True)
     adversarial.to_json(

text_attacks/datasets/enron_spam.py

+"""Classification model for enron_spam"""
+import pandas as pd
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+
+
+def download_dataset():
+    dataset = load_dataset("SetFit/enron_spam")
+    train = pd.DataFrame(dataset["train"].to_dict())
+    test = pd.DataFrame(dataset["test"].to_dict())
+
+    train["label"] = train["label_text"]
+    train = train.rename(columns={"message_id": "id"})
+    train = train.drop(columns=["label_text", "subject", "message", "date"])
+
+    test["label"] = test["label_text"]
+    test = test.rename(columns={"message_id": "id"})
+    test = test.drop(columns=["label_text", "subject", "message", "date"])
+    adversarial, test = train_test_split(
+        test,
+        test_size=0.9,
+        stratify=test["label"]
+    )
+
+    return train, test, adversarial

text_attacks/datasets/poleval.py

+"""Download and preprecess poleval"""
+import pandas as pd
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+
+
+def download_dataset():
+    dataset = load_dataset("poleval2019_cyberbullying", "task01")
+    train = pd.DataFrame(dataset["train"].to_dict())
+    test = pd.DataFrame(dataset["test"].to_dict())
+
+    train["id"] = list(range(len(train)))
+    train["label"] = [
+        "hate" if lab == 1 else "normal" for lab in train["label"]
+    ]
+
+    test["id"] = list(range(len(test)))
+    test["label"] = [
+        "hate" if lab == 1 else "normal" for lab in test["label"]
+    ]
+    adversarial, test = train_test_split(
+        test,
+        test_size=0.9,
+        stratify=test["label"]
+    )
+
+    return train, test, adversarial

text_attacks/models/enron_spam.py

 """Classification model for enron_spam"""
+import os
+
+import torch
 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 
 def get_model_and_tokenizer():
-    tokenizer = AutoTokenizer.from_pretrained(
-        "mrm8488/bert-tiny-finetuned-enron-spam-detection"
-    )
-    model = AutoModelForSequenceClassification.from_pretrained(
-        "mrm8488/bert-tiny-finetuned-enron-spam-detection"
-    )
+    model_path = "data/models/endron_spam"
+    if not os.path.exists(model_path):
+        model_path = "mrm8488/bert-tiny-finetuned-enron-spam-detection"
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
     model.config.id2label = {0: "ham", 1: "spam"}
     return model, tokenizer
+
+
+def get_classify_function():
+    model, tokenizer = get_model_and_tokenizer()
+
+    def fun(texts):
+        encoded_inputs = tokenizer(
+            texts,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=512
+        )
+        logits = model(**encoded_inputs).logits
+        pred_y = torch.argmax(logits, dim=1).tolist()
+        pred_y = [model.config.id2label[p] for p in pred_y]
+        return pred_y
+
+    return fun

text_attacks/utils.py

@@ -10,4 +10,19 @@ def get_model_and_tokenizer(dataset_name):
     )
     return fun()
 
-    
+
+def get_classify_function(dataset_name):
+    """Return get_model_and_tokenizer for a specific dataset."""
+    fun = getattr(
+        importlib.import_module(f"text_attacks.models.{dataset_name}"),
+        "get_classify_function",
+    )
+    return fun()
+
+
+def download_dataset(dataset_name):
+    fun = getattr(
+        importlib.import_module(f"text_attacks.datasets.{dataset_name}"),
+        "download_dataset",
+    )
+    return fun()