ToxDect-roberta Evaluation.ipynb

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64"],"metadata":{"id":"_L9402N1S8dG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ignore_functionalities = [\"F11: Non-hateful use of profanity\",\n","                          \"F22: Abuse targeted at objects\",\n","                          \"F23: Abuse targeted at individuals (not as member of a prot. group)\",\n","                          \"F24: Abuse targeted at nonprotected groups (e.g. professions)\",\n","                          \"F25-29: Spelling variation\"]"],"metadata":{"id":"sE-yc00Fh46v"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 0. Download Model from HuggingFace"],"metadata":{"id":"T_WmbWSHdeYj"}},{"cell_type":"code","source":["from huggingface_hub import snapshot_download\n","snapshot_download(repo_id=\"Xuhui/ToxDect-roberta-large\", cache_dir=ROOT_DIR/\"models/ToxDect-roberta-large\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":352,"referenced_widgets":["df62a373abb04029b48f2b3fac8ae745","940c3862ab384e31acf317b8e21c2645","28f458a771a64704bffda6cc1f634f75","a6a42bd64e14483a9a6e2ea050c616fa","715d06033b8f44fda56500cbb80306cc","07ea8fafb0114a5f8f06e9561f7ce055","089c2f3889bf4372aa38e81a5f9329b1","c15be92e99f440bb8196ce0eb4c4af12","95ea6c1534f84bbf9be084c643f4a318","bdfc88f85057444781cd739b1a3c1259","efe7303c4f7c4a75a99fb0575309c25c","58c9caee75ca424c938140f024b11d38","141d492c3d5043f88809808be487f2d7","ee5c5801d6c5441da7dd45c7daa2442b","2a34d4ee43cd45b681fb453d15ac6336","ac2b35c2c50f4d219658b0ff201fdfd1","451638b35d9745c89853e60ebb12c0c3","43ca368eb31f476e98d3a1bbf71c6951","5088a8ccb80f4e5ba1d3324f4e1425fa","266d275802ce42eeb11c4cf47274b563","5cefd8f3ca644f59ab51d45a00afe487","856d2e30352b4bf888a0269d50ad8720","870f00e055984ed784d31ea901bab4e9","f55cb9df07b74e8ba2b1856d19b16895","ed9776672e1340ad94808dc1a8396302","b7b49e6f100b47358dd88c588782352f","2f4397267852439baa61975f0f7c3e4a","9af0ef573ed14964aae4c640b38f6ff5","b54ebe8dfb904bf096358a398289a9c0","dd702b1d0a944410aee3b09ecf8b6819","01ac0fd6154246289e41b969f2528ca2","fa1bfd43d2f14f719adbfb71fde5d91d","edfdc463dcec4fe38fc19c073a9ce79c","f45bb9f12751402da9af09fc7dc4a895","07ab70afc6bd49fba809071055a4dc74","388666a1bbaf4dc8b2e49789f1bc0ac6","f30c19894db84280a4838c63a954da08","f42debb1c500430ca73d9e0b79d597eb","da4aabfc53d9493697e93b6ee3304893","dfb5dda90f1e45b5b708eb19bdc6fc68","1feb81f86fa1459397521517fa7b7e37","6a0ac9d622d94d4b8240f25d9d441c45","b9d9eb48de5f44dd98df0687d5598008","331115766cbe427abbef0a51801f010e","865878398889448e9d36c55ac7edb51d","8f4ac11d4f8047ddbe98159a5751e18e","20fac26cad5c465f936789f9753f3491","6db1c87589ed4617b59d8632d1fba49c","639fac0d5c264beb99cea628d2775e72","0aacd15c209e428d868393d433508f17","f06cfd547e7d4ecd9d16d30dd4f8d90a","b405ebf05c244694a4dc46b416249b30","8735d190739f4403a060b4cec418b22a","0b817faf8cca4035b91479149b0a1274","66edf466c2404e9db12957663f5ca01f","373d4a650e2b4062b691b2acf87dc1d3","95f38864f5604c599c80c63c77405e82","83b5fe7224534b3c88b2d6b75a8c7f1d","51fe4345ecba4a96a86c1b2b6bdacf3f","569453a8a4524aaa9da1bf0f32fed3fa","9224e58d373d411d9a0ef402b88fe23d","c8520de3a0544ab8a52e397620e8156f","1559b3b91f24443db517c9a53939f8b6","0ba0d697b95b4a30b673eaf998a35450","1c716005382e4be0a4c6d6df3a6e5374","7248362489164787afd81dd52f36fdf9","1518e7e484184b53b1e0c1515d8cfbe4","49635dfbda5c436eaa82332edba6227f","423d9a931d884f71b9b8edb378d1b36f","f5ec5b67d6e04ec7a480124cf4ed6300","e02eb586a75f4c36bbf5f5be22431215","a635d5defa214fa68069d8dbdd7ce0bc","0b33b45272ad4e76bd7f7bbce55d6db3","90f84c27883e4130a64d183901b513d7","075fbaa074a34e2ab390ceaec7af49f0","3bebcdedf2d8471a8f694522386fb5db","735bfa1ad51f4f638fa0fe57e7a1907b","e52d29b2b15d4e769a888a655b62b503","a107e481dd0d4369b526c016f78c162d","cf4dd92b75cf4411a82507556b84f678","2bdb608d86d14ab18b578d57d2805ab2","9e7b67d8627b4c5cb8ecea9444f8ebc6","cc44d5e0f7ae4a8ebd8366413708bba1","fd6bf0f07d3841819dbe21e518732ad8","f1150ff7c5774806898378f269fbe0a8","719d8f4168dd44f9bb8ad47568eb0b18","1f65c070345c4bd88ad77e529fe55060","4c1eb58b71e947ee858fe6d601c2aaa2","7d9d878b51ec4b33880ab69dc5c5de7a","6b277bce8fd04d0d973e7809f86e3fa2","ccf3947b13bc4f4295afbbf7778c066d","5f0b240add5a4a1e9512dba02c6d0ca6","e2c456754fd74ff1a43b147844a11015","63fcda70022345d5bdbe04505789fb49","b461e41bd7134f1e940958cfcf403a23","4f417b55f2bd4e2e91d73d9081fdc547","1ba5384d09b949dd9f8e32ca1fb6d1ac","bccf8d9f789e408db27801d797d757fb","2920d968384d4e78a1a9cc84f8038a21"]},"id":"_i-D6CYoddMT","executionInfo":{"status":"ok","timestamp":1719825427637,"user_tz":-120,"elapsed":26850,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"7eb9ba1b-924d-409a-db72-f3d0ffb9d4b6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Fetching 8 files:   0%|          | 0/8 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"df62a373abb04029b48f2b3fac8ae745"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/1.56k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58c9caee75ca424c938140f024b11d38"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[".gitattributes:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"870f00e055984ed784d31ea901bab4e9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/788 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f45bb9f12751402da9af09fc7dc4a895"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"865878398889448e9d36c55ac7edb51d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"373d4a650e2b4062b691b2acf87dc1d3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.22k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1518e7e484184b53b1e0c1515d8cfbe4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e52d29b2b15d4e769a888a655b62b503"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d9d878b51ec4b33880ab69dc5c5de7a"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/Colab Notebooks/biases-hatecheck/models/ToxDect-roberta-large/models--Xuhui--ToxDect-roberta-large/snapshots/7b97c89938cb241d3ae9235257bbe4916d4f0c75'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["## Load model and HelloWorld example"],"metadata":{"id":"uewwiwxfeStB"}},{"cell_type":"code","source":["model_path = ROOT_DIR/\"models/ToxDect-roberta-large/models--Xuhui--ToxDect-roberta-large/snapshots/7b97c89938cb241d3ae9235257bbe4916d4f0c75\"\n","clf_model = AutoModelForSequenceClassification.from_pretrained(model_path)\n","tokenizer = AutoTokenizer.from_pretrained(model_path)"],"metadata":{"id":"HKHcNog8TC77"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def predict(text, max_len=128, padding=True, truncation=True, return_prob=True):\n","    tokenized_input = tokenizer(text, return_tensors='pt', max_length=max_len, padding=padding, truncation=truncation)\n","    with torch.no_grad():\n","        pred = clf_model(**tokenized_input).logits\n","        if not return_prob:\n","          _, indices = torch.max(pred, dim=1)\n","          return indices\n","        else:\n","          probs = torch.softmax(pred, dim=1)\n","          return probs[:, 1]  # return the prob of hateful label"],"metadata":{"id":"OHL_RGJ4Tpj0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predict([\"Trans are fucking stupid.\", \"Trans are beautiful.\"], return_prob=True).tolist()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Me-gqw_BT23f","executionInfo":{"status":"ok","timestamp":1719825504411,"user_tz":-120,"elapsed":431,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"5a363678-755c-4b99-f7b3-53977c883467"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.9971505999565125, 0.0015412544598802924]"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["## 1. Load GPT-HateCheckDataset and Predict\n","\n","The prediction files are written to /datasets/GPT-HateCheck/hate_bert/"],"metadata":{"id":"GrDDa2KzI8hC"}},{"cell_type":"code","source":["dataset_path = ROOT_DIR/\"datasets/GPT-HateCheck/nli_hypothesis_test/output/\""],"metadata":{"id":"iB06Q6KRXCZ8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for p in dataset_path.glob('**/*.csv'):\n","\n","  if (ROOT_DIR/\"datasets/GPT-HateCheck/toxdect-roberta/\"/p.name).is_file():\n","    print(f\"[WARNING] File perspective-{p.name} already exists. Skipped.\")\n","    continue\n","  df = pd.read_csv(p)\n","  df = df[df['nli_pass_test']==1]\n","  messages = df['message'].tolist()\n","  functionalities = df['functionality'].tolist()\n","  hate_labels = df['hate_label'].tolist()\n","  n_messages = len(messages)\n","  toxicity_scores = []\n","  print(f\"Predicting for file: {p.name} with {n_messages} messages.\")\n","  for i in trange(0, n_messages, bs):\n","    batch_toxicity_scores = predict(messages[i:i+bs]).tolist()\n","    toxicity_scores.extend(batch_toxicity_scores)\n","  df_out = pd.DataFrame({\"message\": messages,\n","                         \"functionality\":  functionalities,\n","                         \"hate_label\": hate_labels,\n","                         \"toxicity_score\": toxicity_scores})\n","  df_out.to_csv(ROOT_DIR/\"datasets/GPT-HateCheck/toxdect-roberta/\"/p.name, index=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":397,"referenced_widgets":["342927ffe581446cb75c88ce021ee40b","de945afacfe14c14b0f69a8d8bb0aa9c","82e27b6cfad24c93ab67185fc8b8d322","1fdea990ca1644dcb27a60f666cc26a5","811fcea8028d4fd9b714b4b99e0266db","ae8bb4e56fb449b4aae706f6626678dd","d8db6307e5ef4f35bde3af1075caf166","9c0d1e34ba07446ba5adec7ed5dae56b","7e6b74ce69114a88a33a580dd16b7081","6ded6af853ee4fb4a54f6d4e08a8943e","60bdab45756e43f3a876168c755f534d","eaca539395a24ac09055575ca07cb5c9","026c686270804e0fb674d904d3cb9b32","0736a7f6106c4805a4aaf415839530a1","c105070ef9d64feda3ce115a0ca68202","5cc1df85f4f44c738f828d4d3be114ad","de4011949438427ea9df0bf5f4396e36","35a8513263134e29a70206346ff1aa8e","8ea8c84d4bd94417916b4459d77fa3f4","d9de0e7c0bb94d59b2b453c0a4411592","02ce9799bdd84e4797fae95dd85e763e","d8e390edbb484926987d9d9d8cb36d44","cc6b3196688c496396ac0f495604663c","fd88c04d6cc94a4c804d7e23bd53da9b","13864371b63149ce974187e62b34dfd2","82efbf26afb3420c86bff9e8ab9ad205","93c725c1022d4557838341829317bd77","775ca6113d2d451f85cf9267b3a24c5b","58a79248e13a4095bf2dc2101ea27b3a","8415eca751864f7992a0276114242219","c91d9d7f707644eab04b2cb670bb1d76","83105f59af6c4f88a711132b32b3fbe1","33aae292900d4a929e275ed1be51532c","98f1f4ece03c423cb98f8297a39641e9","9dac9635a5184c47ba50cc5412f38c3d","167b1afea76b43d5ae08ed10456fdcb4","224f8c258d5c4b0fa17d0fc98db07691","d4b73a6ac12448c28544be45e740c7a3","490ac7ee05e6473aab54d65b0e04ee62","347eabd03abc469aa5726362f2b09cdd","2fbf49425f8e41b6a7c01bc45619ae38","73dfe6fe55964146bb04f681c89394ae","62614001ddac4c48abd7eb7792258df2","9822a3dc9b4e456297f97f6faada92c8","02ae2536a5434db78bf840cc107b6c76","0185c23710a44b6d9518dca44159f351","59aaf7e7e1804aadaf68366cc554fd76","0566b64567a44a63a60dfc65f9368d7a","e934e1bc1a114963b7e6eb7015dd3b93","b50766de9c5c429ca36078a541be20cf","7b50da7f14da4010af64e05e18f6c833","49574f7db24b499a91d9c5b0fefe184e","7e3de0a7449441ccb771e40e456e19c7","4db29fc4f0fb490e96eb2d75223906b3","7ac169e817084087abad07953d60a34e","b1515a5fd17d4c09af951fdbfc8f036d","d5959c2968744d7ca79922a35a4a5f50","3686fcea60fa45839f6625b9ff5ad97d","00d3855ae7b048c796b83f5692df609e","5316963c1e2c4f77b44a4132ac5f675a","650fb9f8dc2640d5aa0d05a929dc2a76","74ad3b2849c14585a1d077b8f0a9a45c","e51cae82a8644e00a370b037383ac586","2a5fb48a91344f5c88d1b39414ce617a","367e8a4713c647658a7049e4a2d382b5","af205ff404c2425183bedc0d2c1f4d28","e7406457e5304feb9eb0497d67f55452","30da7b94600c4756b3a15e07a635ed0e","01a66f60c3c44bab9cb80b4aa4cd9b2c","2fa4e57e8ae549c8845c1459632633e4","5a03d10adc44421cbddf4978ea23cb5a","e27eee7896504fe88bf542e40f4935f7","2f7ee679f147497689d49453f2a8ab5c","0f7f2bcb95774c0285ba754ffb5eb3fb","452a118a4e2549d3ae3b16301dec7967","4f2be2c936c54dc48d819531606db2d6","dc25a7537e0847998868d3d8a2860146"]},"id":"t2ux2bLbJL5h","executionInfo":{"status":"ok","timestamp":1719827134567,"user_tz":-120,"elapsed":1379859,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"e14b2fc6-f324-4675-b663-916b68c1620b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_black people.csv with 874 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/14 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"342927ffe581446cb75c88ce021ee40b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_Muslims.csv with 663 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eaca539395a24ac09055575ca07cb5c9"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_disabled people.csv with 644 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cc6b3196688c496396ac0f495604663c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_trans.csv with 611 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/10 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98f1f4ece03c423cb98f8297a39641e9"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_women.csv with 606 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/10 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"02ae2536a5434db78bf840cc107b6c76"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_gays.csv with 646 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1515a5fd17d4c09af951fdbfc8f036d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: dataset_immigrants.csv with 684 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e7406457e5304feb9eb0497d67f55452"}},"metadata":{}}]},{"cell_type":"markdown","source":["### 1.1 Evaluation and analysis"],"metadata":{"id":"EXu8PE39Lo5b"}},{"cell_type":"code","source":["prediction_path = ROOT_DIR/\"datasets/GPT-HateCheck/toxdect-roberta/\""],"metadata":{"id":"aShYyrMaLU1i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Overall accuracy\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[df['functionality'] != 'F25-29: Spelling variation']\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  toxicity_acc = accuracy_score(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist())\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"toxicity_acc: {toxicity_acc:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Hf0CB2s4MPqn","executionInfo":{"status":"ok","timestamp":1719827455949,"user_tz":-120,"elapsed":405,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"b153627d-da0e-40d5-b452-9ca770895471"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","toxicity_acc: 0.6544\n","Target identity: Muslims\n","toxicity_acc: 0.8535\n","Target identity: disabled people\n","toxicity_acc: 0.4734\n","Target identity: trans\n","toxicity_acc: 0.5482\n","Target identity: women\n","toxicity_acc: 0.4161\n","Target identity: gays\n","toxicity_acc: 0.7516\n","Target identity: immigrants\n","toxicity_acc: 0.5186\n"]}]},{"cell_type":"code","source":["# Averaged functionality-level accuracy\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  df['is_correct'] = df.apply(lambda x: 1 if x['toxicity_pred']==x['hate_label'] else 0, axis=1)\n","  df_func_acc = df[['functionality', 'hate_label', 'is_correct']].groupby('functionality').mean().reset_index()\n","  overall_acc = df_func_acc['is_correct'].mean()\n","  df_category_acc = df_func_acc[['hate_label', 'is_correct']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n","  cat_acc = df_category_acc['is_correct'].tolist()\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"Overall acc: {overall_acc:.3f}; Hateful subset acc: {cat_acc[0]:.3f}; Non-hateful subset acc: {cat_acc[1]:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"skFK65LoMkiJ","executionInfo":{"status":"ok","timestamp":1718024191370,"user_tz":-120,"elapsed":544,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"8449dfe2-59c8-405d-b8bc-5dc8d993d9f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","Overall acc: 0.686; Hateful subset acc: 0.858; Non-hateful subset acc: 0.482\n","Target identity: Muslims\n","Overall acc: 0.821; Hateful subset acc: 0.866; Non-hateful subset acc: 0.738\n","Target identity: disabled people\n","Overall acc: 0.669; Hateful subset acc: 0.611; Non-hateful subset acc: 0.777\n","Target identity: trans\n","Overall acc: 0.776; Hateful subset acc: 0.763; Non-hateful subset acc: 0.800\n","Target identity: women\n","Overall acc: 0.625; Hateful subset acc: 0.618; Non-hateful subset acc: 0.642\n","Target identity: gays\n","Overall acc: 0.808; Hateful subset acc: 0.828; Non-hateful subset acc: 0.772\n","Target identity: immigrants\n","Overall acc: 0.749; Hateful subset acc: 0.758; Non-hateful subset acc: 0.733\n"]}]},{"cell_type":"code","source":["# Averaged functionality-level prediction\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n","  overall_pred = df_func_acc['toxicity_score'].mean()\n","  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n","  cat_pred = df_category_acc['toxicity_score'].tolist()\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tmALjfZCNBS0","executionInfo":{"status":"ok","timestamp":1718024221052,"user_tz":-120,"elapsed":321,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"3ae06a1b-4135-4ecb-df54-b86713935dfb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","Overall avg pred: 0.619; Hateful subset avg pred: 0.712; Non-hateful subset avg pred: 0.510\n","Target identity: Muslims\n","Overall avg pred: 0.577; Hateful subset avg pred: 0.736; Non-hateful subset avg pred: 0.281\n","Target identity: disabled people\n","Overall avg pred: 0.491; Hateful subset avg pred: 0.588; Non-hateful subset avg pred: 0.310\n","Target identity: trans\n","Overall avg pred: 0.538; Hateful subset avg pred: 0.676; Non-hateful subset avg pred: 0.284\n","Target identity: women\n","Overall avg pred: 0.537; Hateful subset avg pred: 0.608; Non-hateful subset avg pred: 0.383\n","Target identity: gays\n","Overall avg pred: 0.570; Hateful subset avg pred: 0.705; Non-hateful subset avg pred: 0.319\n","Target identity: immigrants\n","Overall avg pred: 0.543; Hateful subset avg pred: 0.665; Non-hateful subset avg pred: 0.317\n"]}]},{"cell_type":"code","source":["for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  identity_attack_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n","  print(f\"Target identity: {target_identity}\")\n","  print(identity_attack_report)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2TL1Mw9ZNSBr","executionInfo":{"status":"ok","timestamp":1718024246819,"user_tz":-120,"elapsed":374,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"9a207f43-6ef1-47e3-d758-9f81d6e6c71a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","              precision    recall  f1-score   support\n","\n","           0      0.735     0.481     0.582       374\n","           1      0.675     0.861     0.757       468\n","\n","    accuracy                          0.692       842\n","   macro avg      0.705     0.671     0.669       842\n","weighted avg      0.702     0.692     0.679       842\n","\n","Target identity: Muslims\n","              precision    recall  f1-score   support\n","\n","           0      0.652     0.632     0.642       190\n","           1      0.842     0.854     0.848       438\n","\n","    accuracy                          0.787       628\n","   macro avg      0.747     0.743     0.745       628\n","weighted avg      0.785     0.787     0.786       628\n","\n","Target identity: disabled people\n","              precision    recall  f1-score   support\n","\n","           0      0.523     0.759     0.619       224\n","           1      0.818     0.610     0.698       397\n","\n","    accuracy                          0.663       621\n","   macro avg      0.670     0.684     0.659       621\n","weighted avg      0.711     0.663     0.670       621\n","\n","Target identity: trans\n","              precision    recall  f1-score   support\n","\n","           0      0.664     0.783     0.719       217\n","           1      0.864     0.777     0.818       385\n","\n","    accuracy                          0.779       602\n","   macro avg      0.764     0.780     0.768       602\n","weighted avg      0.792     0.779     0.782       602\n","\n","Target identity: women\n","              precision    recall  f1-score   support\n","\n","           0      0.403     0.601     0.482       183\n","           1      0.765     0.594     0.669       401\n","\n","    accuracy                          0.596       584\n","   macro avg      0.584     0.597     0.575       584\n","weighted avg      0.652     0.596     0.610       584\n","\n","Target identity: gays\n","              precision    recall  f1-score   support\n","\n","           0      0.696     0.703     0.699       182\n","           1      0.873     0.869     0.871       426\n","\n","    accuracy                          0.819       608\n","   macro avg      0.784     0.786     0.785       608\n","weighted avg      0.820     0.819     0.819       608\n","\n","Target identity: immigrants\n","              precision    recall  f1-score   support\n","\n","           0      0.513     0.663     0.578       178\n","           1      0.856     0.761     0.805       468\n","\n","    accuracy                          0.734       646\n","   macro avg      0.684     0.712     0.692       646\n","weighted avg      0.761     0.734     0.743       646\n","\n"]}]},{"cell_type":"markdown","source":["## 2. Load HateCheck Dataset and Predict"],"metadata":{"id":"PFOfzJZ9WcER"}},{"cell_type":"code","source":["dataset_path = ROOT_DIR/\"datasets/HateCheck/\""],"metadata":{"id":"nXZcvMN6OXV7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for p in dataset_path.glob('identities/*.csv'):\n","\n","  if (dataset_path/\"hate_bert\"/p.name).is_file():\n","    print(f\"[WARNING] File {p.name} already exists. Skipped.\")\n","    continue\n","  df = pd.read_csv(p)\n","  # Remove test cases related to spelling variation\n","  df = df.loc[~df['functionality'].str.startswith('spell_', na=False)]\n","  messages = df['test_case'].tolist()\n","  functionalities = df['functionality'].tolist()\n","  hate_labels = df['label_gold'].tolist()\n","  n_messages = len(messages)\n","  toxicity_scores, identity_attack_scores = [], []\n","  print(f\"Predicting for file: {p.name} with {n_messages} messages.\")\n","\n","  for i in trange(0, n_messages, bs):\n","    batch_toxicity_scores = predict(messages[i:i+bs]).tolist()\n","    toxicity_scores.extend(batch_toxicity_scores)\n","\n","  df_out = pd.DataFrame({\"message\": messages,\n","                         \"functionality\":  functionalities,\n","                         \"hate_label\": hate_labels,\n","                         \"toxicity_score\": toxicity_scores})\n","  df_out.to_csv(dataset_path/\"hate_bert\"/p.name, index=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":397,"referenced_widgets":["dfc69553930f4e0e9bd81762cd218373","e8b2dbf9c3d94a3f99801ef7f4e19f36","024b3a911c50444e8b8ab00ed1d80ec3","e900454e02434432bdf33a06dc0a358c","1b6379637731420bb5d3b76f9b34b5d5","e079197ed55f482484d803017294e851","3678f9fedd0349dc98b0852f9fa18de7","80d93f5e7b2748f882fe9ccfb43cbb3f","49380e8cd7454195a811fca6cd15e211","019b0bc66c684354897d0efd98020541","9fcf779b94e74828a4ad3b3a4449803b","b1598617244f4b9c85e64fcc0fd58afe","59216d77678846ba854f2969156ecc96","2967136d747542619c1919791b7d35d2","3354e97a7b504b8a8746197d038b8e37","f9e0a3bb64134e8992289fed94b80e82","2d806531adf14651b177e9542e0f396f","28c0e7297bb3482f981fa9bf2fa4f25c","401ed537677740409b7499a5c726c375","96dc2ba2515e42ec8961ad916ed3f68f","5cf5255101a74803b55e05a890787d5c","989997026faa48578f86bdbcfe04918f","2a88e9495c3e4201b83e509fac5218a8","50a955a048d1444cbb4ef688d45fc791","da7e6e5a9c15410c850d329c8ad1e6ce","3d4a1ce6e80842b19705a8918466e6e1","70c9f16308844c849bf0cc9c1fef869d","2a0274ad86a34dbe99a5839016292173","76ff8e6c088b4aa5a1697ad49ec99a09","e175ce9fdfcd49d0a198aef0683dc8db","49371223b00c48819c6c847cf7493fcb","35f49fe4e0134473a060e58dbeb33593","d4252ff6e3e44529aaa84c51749432ba","4e37dc705f2c4900b540ec0555ea2dd2","ed10e6350840466a83d4f9da84eae98f","c440b4df434042ef84a3b51461ec8f62","5c82447ee40a47a69865e3697dad2083","4ad6a219dcd94cee955f2fc94886e86b","3b9e241ab32f435f8e5da4546a02c731","cb4fed6eb16143bbac56fc8d6558f30c","e3a7f7ffc84849df8d378a8c510cf074","bac20f09617e4fcc94644b90e6aedca9","299c2d79165c48109a5a334881448fab","5cd9a17eb05243bbb7b2eab8228abd01","986db4fad427442bb355990c76e1c7f1","39a79b059b5e42b79f81e7fe6a872927","38d2756835054141826f795a13710f75","9b94ad2043014dcf82a5035b105ed5bf","3e64a93e18fb463fa1e16abdb401fcd3","b88909bec06a4fe4834bb79e8792c42a","7b5e6e3cad29403a89bfb22afc6b31cc","cd1f01ffa37347baa335cc15edb76ad0","3bd6986e608f41b89e0aec516a718237","5a3e4c554fa64d3190158580c46bbc31","9b56e1f19bfb4d6c8b2f07d48a34e053","bb74f35c8eed407c94983b09018f5702","34a7ffc8d22b4601a86413c74a38249f","6a381565b9234d8d891a5f485fb07480","9df890a905224ff29bf9440c7cb5984d","09acdf015cc64f39a1ca95962acb1931","725ef2bf981243e6abfb747a8d6aa0a4","af9adc615ced4ffe91fdb233dce6f528","8c9679b13f4640cdba3e95f505ee62d3","d304adad13bf4e548c9d6e6ac8dfdb2d","d42a7a96d23444958175a880bdd23076","e1e9d3c1664c48c4ba4b560f4028ac26","ce6f46c3d5224fa6820159d9ebd72e0b","c2a2b381726945b997c580730575f0b8","42c4e708c5064265af3f4ab466cc4f0a","b5de289855704a769713b3f832956e35","4988a31d05a143729aafa61ef90afba7","2803ff2df30e4af1a3aea0bbbb941660","f3559207673e4555abae5b5039c0eabf","4d6d2ff6a945420cbe451c1c992b4480","715624175a4c42d892cf8c0534d44e36","50945952d61e46d6b4f92438185a274f","9f3a0fb115244d7b87fe79737bd45032"]},"id":"3SjKf6q-WjMu","executionInfo":{"status":"ok","timestamp":1718027153126,"user_tz":-120,"elapsed":251206,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"2a29243e-12f8-421a-c639-fc6d0fe5823b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Predicting for file: immigrants_test_suite_cases.csv with 359 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dfc69553930f4e0e9bd81762cd218373"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: gay people_test_suite_cases.csv with 439 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1598617244f4b9c85e64fcc0fd58afe"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: black people_test_suite_cases.csv with 378 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2a88e9495c3e4201b83e509fac5218a8"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: Muslims_test_suite_cases.csv with 372 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e37dc705f2c4900b540ec0555ea2dd2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: trans people_test_suite_cases.csv with 359 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"986db4fad427442bb355990c76e1c7f1"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: women_test_suite_cases.csv with 397 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bb74f35c8eed407c94983b09018f5702"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Predicting for file: disabled people_test_suite_cases.csv with 372 messages.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce6f46c3d5224fa6820159d9ebd72e0b"}},"metadata":{}}]},{"cell_type":"markdown","source":["### 2.1 Evaluation and analysis"],"metadata":{"id":"JX2a4DhKXoq-"}},{"cell_type":"code","source":["prediction_path = ROOT_DIR/\"datasets/HateCheck/hate_bert\""],"metadata":{"id":"nQxtXZdSXi2K"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Overall accuracy\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[df['functionality'] != 'F25-29: Spelling variation']\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n","  toxicity_acc = accuracy_score(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist())\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"toxicity_acc: {toxicity_acc:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zC7Yr7baYLLO","executionInfo":{"status":"ok","timestamp":1718027347177,"user_tz":-120,"elapsed":591,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"ac8880ef-4f58-47e3-de98-a47e2cdad510"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: immigrants\n","toxicity_acc: 0.7744\n","Target identity: gay people\n","toxicity_acc: 0.7244\n","Target identity: black people\n","toxicity_acc: 0.7619\n","Target identity: Muslims\n","toxicity_acc: 0.7984\n","Target identity: trans people\n","toxicity_acc: 0.7772\n","Target identity: women\n","toxicity_acc: 0.7380\n","Target identity: disabled people\n","toxicity_acc: 0.7312\n"]}]},{"cell_type":"code","source":["# Averaged functionality-level accuracy\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[df['functionality'] != 'F25-29: Spelling variation']\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n","  df['is_correct'] = df.apply(lambda x: 1 if x['toxicity_pred']==x['hate_label'] else 0, axis=1)\n","  df_func_acc = df[['functionality', 'hate_label', 'is_correct']].groupby('functionality').mean().reset_index()\n","  overall_acc = df_func_acc['is_correct'].mean()\n","  df_category_acc = df_func_acc[['hate_label', 'is_correct']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n","  cat_acc = df_category_acc['is_correct'].tolist()\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"Overall acc: {overall_acc:.3f}; Hateful subset acc: {cat_acc[0]:.3f}; Non-hateful subset acc: {cat_acc[1]:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0V90t_-wY2rx","executionInfo":{"status":"ok","timestamp":1718027406644,"user_tz":-120,"elapsed":259,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"820114c5-3dfe-4495-96d3-1d7a81f91b7f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: immigrants\n","Overall acc: 0.778; Hateful subset acc: 0.917; Non-hateful subset acc: 0.416\n","Target identity: gay people\n","Overall acc: 0.755; Hateful subset acc: 0.917; Non-hateful subset acc: 0.455\n","Target identity: black people\n","Overall acc: 0.764; Hateful subset acc: 0.977; Non-hateful subset acc: 0.304\n","Target identity: Muslims\n","Overall acc: 0.810; Hateful subset acc: 0.961; Non-hateful subset acc: 0.419\n","Target identity: trans people\n","Overall acc: 0.781; Hateful subset acc: 0.871; Non-hateful subset acc: 0.547\n","Target identity: women\n","Overall acc: 0.730; Hateful subset acc: 0.903; Non-hateful subset acc: 0.409\n","Target identity: disabled people\n","Overall acc: 0.738; Hateful subset acc: 0.837; Non-hateful subset acc: 0.481\n"]}]},{"cell_type":"code","source":["# Averaged functionality-level prediction\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[df['functionality'] != 'F25-29: Spelling variation']\n","  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n","  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n","  overall_pred = df_func_acc['toxicity_score'].mean()\n","  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n","  cat_pred = df_category_acc['toxicity_score'].tolist()\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WG2NHoRJY9Fm","executionInfo":{"status":"ok","timestamp":1718027483970,"user_tz":-120,"elapsed":382,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"51c0f45b-72ec-4aa2-b830-efd2453777dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: immigrants\n","Overall avg pred: 0.748; Hateful subset avg pred: 0.819; Non-hateful subset avg pred: 0.565\n","Target identity: gay people\n","Overall avg pred: 0.712; Hateful subset avg pred: 0.810; Non-hateful subset avg pred: 0.531\n","Target identity: black people\n","Overall avg pred: 0.772; Hateful subset avg pred: 0.841; Non-hateful subset avg pred: 0.622\n","Target identity: Muslims\n","Overall avg pred: 0.767; Hateful subset avg pred: 0.837; Non-hateful subset avg pred: 0.585\n","Target identity: trans people\n","Overall avg pred: 0.682; Hateful subset avg pred: 0.764; Non-hateful subset avg pred: 0.468\n","Target identity: women\n","Overall avg pred: 0.696; Hateful subset avg pred: 0.771; Non-hateful subset avg pred: 0.555\n","Target identity: disabled people\n","Overall avg pred: 0.653; Hateful subset avg pred: 0.718; Non-hateful subset avg pred: 0.483\n"]}]},{"cell_type":"code","source":["for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[df['functionality'] != 'F25-29: Spelling variation']\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n","  identity_attack_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n","  print(f\"Target identity: {target_identity}\")\n","  print(identity_attack_report)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hhBaSYp_Y-Ll","executionInfo":{"status":"ok","timestamp":1718027540806,"user_tz":-120,"elapsed":789,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"3cf3eda5-c54d-41ac-9be6-6823414b63fb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: immigrants\n","              precision    recall  f1-score   support\n","\n","           0      0.687     0.434     0.532       106\n","           1      0.795     0.917     0.851       253\n","\n","    accuracy                          0.774       359\n","   macro avg      0.741     0.675     0.692       359\n","weighted avg      0.763     0.774     0.757       359\n","\n","Target identity: gay people\n","              precision    recall  f1-score   support\n","\n","           0      0.782     0.444     0.566       178\n","           1      0.707     0.916     0.798       261\n","\n","    accuracy                          0.724       439\n","   macro avg      0.745     0.680     0.682       439\n","weighted avg      0.738     0.724     0.704       439\n","\n","Target identity: black people\n","              precision    recall  f1-score   support\n","\n","           0      0.872     0.328     0.477       125\n","           1      0.746     0.976     0.846       253\n","\n","    accuracy                          0.762       378\n","   macro avg      0.809     0.652     0.661       378\n","weighted avg      0.788     0.762     0.724       378\n","\n","Target identity: Muslims\n","              precision    recall  f1-score   support\n","\n","           0      0.810     0.423     0.556       111\n","           1      0.796     0.958     0.870       261\n","\n","    accuracy                          0.798       372\n","   macro avg      0.803     0.691     0.713       372\n","weighted avg      0.800     0.798     0.776       372\n","\n","Target identity: trans people\n","              precision    recall  f1-score   support\n","\n","           0      0.641     0.557     0.596       106\n","           1      0.824     0.870     0.846       253\n","\n","    accuracy                          0.777       359\n","   macro avg      0.733     0.713     0.721       359\n","weighted avg      0.770     0.777     0.772       359\n","\n","Target identity: women\n","              precision    recall  f1-score   support\n","\n","           0      0.695     0.419     0.523       136\n","           1      0.749     0.904     0.819       261\n","\n","    accuracy                          0.738       397\n","   macro avg      0.722     0.662     0.671       397\n","weighted avg      0.731     0.738     0.718       397\n","\n","Target identity: disabled people\n","              precision    recall  f1-score   support\n","\n","           0      0.558     0.477     0.515       111\n","           1      0.791     0.839     0.814       261\n","\n","    accuracy                          0.731       372\n","   macro avg      0.674     0.658     0.664       372\n","weighted avg      0.721     0.731     0.725       372\n","\n"]}]},{"cell_type":"markdown","source":["### 2.2 Minimum set analysis\n","\n","The examples in HateCheck contains minimum sets where the only difference among examples are the identity terms. Therefore, we can calculate the diviation of predictions among mimimum sets.\n","\n","Thereafter, a naive way to \"debiase\" is to substract the diviation from the prediction."],"metadata":{"id":"cuMET4IWifnZ"}},{"cell_type":"code","source":["dataset_path = ROOT_DIR/\"datasets/HateCheck/\""],"metadata":{"id":"y3kO5JJAa6R5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(dataset_path/\"test_suite_cases.csv\")\n","df = df.loc[~df['functionality'].str.startswith('spell_', na=False)]\n","df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Umi3_-XNilLG","executionInfo":{"status":"ok","timestamp":1719827480380,"user_tz":-120,"elapsed":1309,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"65473273-71f4-4e88-c51d-7f1bc9460340"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2968, 13)"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# Get the list of unique templates that covers all 7 target groups (excluding spelling variation ones)\n","df_temp = df[['case_templ', 'case_id']].groupby(\"case_templ\").count().sort_values(\"case_id\").reset_index()\n","df_temp = df_temp.loc[df_temp['case_id']==7]\n","unique_templates = set(df_temp['case_templ'].tolist())\n","print(f\"{len(unique_templates)} unique templates.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aa7UZ8WYipxE","executionInfo":{"status":"ok","timestamp":1719827491718,"user_tz":-120,"elapsed":364,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"74a52b6a-f3f3-4474-af7d-9eb6595b357e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["333 unique templates.\n"]}]},{"cell_type":"code","source":["# Filter the dataset by the templates and sort the result\n","df = df.loc[df['case_templ'].isin(unique_templates)]\n","df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aTkZHsJWirUn","executionInfo":{"status":"ok","timestamp":1719827493735,"user_tz":-120,"elapsed":394,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"95eb3d77-deb4-43f0-fcc4-73ab52404d7f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2331, 13)"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["messages = df['test_case'].tolist()\n","n_messages = len(messages)\n","toxicity_scores = []\n","for i in trange(0, n_messages, bs):\n","  batch_toxicity_scores = predict(messages[i:i+bs]).tolist()\n","  toxicity_scores.extend(batch_toxicity_scores)\n","df['toxicity_score'] = toxicity_scores\n","df.to_csv(dataset_path/\"toxdect-roberta/minimum_sets.csv\", index=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["0665248896554b5ea46b01c1932e8db0","a52f6a429e5c451bbeb35737574e9738","b248fa24d9fe4156bf73f7e9fa37678f","425b949ec9c444eeb8e070cd70ec2110","2891478469124021bf148c0caea899c7","ab896febaf76414aad78858051bd8bc7","b2ab8601a42041a1b2c39352e6a4451e","7e9efc36451c43b8b26ff60248e2bee3","38637f91e16045b58dcd6f7e6d96ff48","84148811778944cba707ba9ca9edd644","bd879fc9f83849748622b533c23092a4"]},"id":"eyFdemaOiutE","executionInfo":{"status":"ok","timestamp":1719828404221,"user_tz":-120,"elapsed":488738,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"e95ad289-b035-49c5-cba9-8710f405ff28"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/37 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0665248896554b5ea46b01c1932e8db0"}},"metadata":{}}]},{"cell_type":"code","source":["df_median = df[['case_templ', 'toxicity_score']].groupby(\"case_templ\").median().reset_index()"],"metadata":{"id":"LQu3xQ1Nj0mb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["template_avg_score_dict = dict(zip(df_median['case_templ'].tolist(), df_median['toxicity_score'].tolist()))"],"metadata":{"id":"gFwHcVi8jkIS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['normalized_toxicity_score'] = df.apply(lambda x: x['toxicity_score']-template_avg_score_dict[x['case_templ']], axis=1)"],"metadata":{"id":"aZkpT_-OkWTM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_bias = df[['target_ident', 'normalized_toxicity_score']]"],"metadata":{"id":"6FvEHgg-kZcj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.figure(figsize=(6, 3), dpi=300)\n","sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n","plt.xticks(rotation=30)\n","plt.xlabel(None)\n","plt.ylabel(\"Normalized Prediction\")\n","plt.savefig(dataset_path/'toxdect-roberta_target_identity_bias.png', dpi=300, bbox_inches='tight', pad_inches=0)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":886},"id":"Ue4xelbqklO0","executionInfo":{"status":"ok","timestamp":1719828498625,"user_tz":-120,"elapsed":1196,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"3349130b-cd63-43a8-8d6b-19180a743218"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-31-0e5b2844f43d>:2: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n","  sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n","<ipython-input-31-0e5b2844f43d>:2: UserWarning: The palette list has more values (8) than needed (7), which may not be intended.\n","  sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1800x900 with 1 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Debias predictions on GPT-HateCheck using the identity bias from §2.2"],"metadata":{"id":"QYlFkKsssfPA"}},{"cell_type":"code","source":["prediction_path = ROOT_DIR/\"datasets/GPT-HateCheck/toxdect-roberta/\"\n","bias_df_path = ROOT_DIR/\"datasets/HateCheck/toxdect-roberta_identity_bias.csv\""],"metadata":{"id":"oyUAcPbfqb7o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_bias = pd.read_csv(bias_df_path)\n","bias_dict = dict(zip(df_bias['target_ident'].tolist(), df_bias['normalized_toxicity_score'].tolist()))\n","bias_dict['trans'] = bias_dict['trans people']\n","bias_dict['gays'] = bias_dict['gay people']"],"metadata":{"id":"WgpPhaCKsnbM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Before debiasing\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  toxicity_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n","  print(f\"Target identity: {target_identity}\")\n","  print(toxicity_report)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FD6AM172sqBD","executionInfo":{"status":"ok","timestamp":1719828544544,"user_tz":-120,"elapsed":402,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"fc0fd68e-0019-4b99-f5e3-0f48e657f972"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","              precision    recall  f1-score   support\n","\n","           0      0.835     0.324     0.467       281\n","           1      0.703     0.962     0.812       468\n","\n","    accuracy                          0.722       749\n","   macro avg      0.769     0.643     0.639       749\n","weighted avg      0.753     0.722     0.683       749\n","\n","Target identity: Muslims\n","              precision    recall  f1-score   support\n","\n","           0      0.895     0.584     0.707       190\n","           1      0.843     0.970     0.902       438\n","\n","    accuracy                          0.854       628\n","   macro avg      0.869     0.777     0.805       628\n","weighted avg      0.859     0.854     0.843       628\n","\n","Target identity: disabled people\n","              precision    recall  f1-score   support\n","\n","           0      0.399     0.911     0.555       224\n","           1      0.818     0.227     0.355       397\n","\n","    accuracy                          0.473       621\n","   macro avg      0.609     0.569     0.455       621\n","weighted avg      0.667     0.473     0.427       621\n","\n","Target identity: trans\n","              precision    recall  f1-score   support\n","\n","           0      0.439     0.908     0.592       217\n","           1      0.869     0.345     0.494       385\n","\n","    accuracy                          0.548       602\n","   macro avg      0.654     0.627     0.543       602\n","weighted avg      0.714     0.548     0.529       602\n","\n","Target identity: women\n","              precision    recall  f1-score   support\n","\n","           0      0.320     0.770     0.453       183\n","           1      0.708     0.254     0.374       401\n","\n","    accuracy                          0.416       584\n","   macro avg      0.514     0.512     0.413       584\n","weighted avg      0.587     0.416     0.399       584\n","\n","Target identity: gays\n","              precision    recall  f1-score   support\n","\n","           0      0.581     0.610     0.595       182\n","           1      0.830     0.812     0.821       426\n","\n","    accuracy                          0.752       608\n","   macro avg      0.705     0.711     0.708       608\n","weighted avg      0.755     0.752     0.753       608\n","\n","Target identity: immigrants\n","              precision    recall  f1-score   support\n","\n","           0      0.359     0.949     0.521       178\n","           1      0.949     0.355     0.516       468\n","\n","    accuracy                          0.519       646\n","   macro avg      0.654     0.652     0.519       646\n","weighted avg      0.786     0.519     0.518       646\n","\n"]}]},{"cell_type":"code","source":["# Averaged functionality-level prediction after debiasing\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df['toxicity_score'] = df.apply(lambda x: x['toxicity_score']-bias_dict[target_identity], axis=1)\n","  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n","  overall_pred = df_func_acc['toxicity_score'].mean()\n","  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n","  cat_pred = df_category_acc['toxicity_score'].tolist()\n","  print(f\"Target identity: {target_identity}\")\n","  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nQ6ing-o6X0p","executionInfo":{"status":"ok","timestamp":1719828563641,"user_tz":-120,"elapsed":494,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"d6ed896d-bf33-4267-d5aa-2a877d128a37"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","Overall avg pred: 0.492; Hateful subset avg pred: 0.605; Non-hateful subset avg pred: 0.309\n","Target identity: Muslims\n","Overall avg pred: 0.444; Hateful subset avg pred: 0.652; Non-hateful subset avg pred: 0.056\n","Target identity: disabled people\n","Overall avg pred: 0.346; Hateful subset avg pred: 0.400; Non-hateful subset avg pred: 0.245\n","Target identity: trans\n","Overall avg pred: 0.298; Hateful subset avg pred: 0.388; Non-hateful subset avg pred: 0.132\n","Target identity: women\n","Overall avg pred: 0.348; Hateful subset avg pred: 0.376; Non-hateful subset avg pred: 0.288\n","Target identity: gays\n","Overall avg pred: 0.354; Hateful subset avg pred: 0.524; Non-hateful subset avg pred: 0.038\n","Target identity: immigrants\n","Overall avg pred: 0.274; Hateful subset avg pred: 0.384; Non-hateful subset avg pred: 0.071\n"]}]},{"cell_type":"code","source":["# After debiasing\n","for p in prediction_path.glob('**/*.csv'):\n","  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n","  df = pd.read_csv(p)\n","  df = df[~df['functionality'].isin(ignore_functionalities)]\n","  df['toxicity_score'] = df.apply(lambda x: x['toxicity_score']-bias_dict[target_identity], axis=1)\n","  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n","  toxicity_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n","  print(f\"Target identity: {target_identity}\")\n","  print(toxicity_report)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"flIlZBd9tSmK","executionInfo":{"status":"ok","timestamp":1719828569487,"user_tz":-120,"elapsed":418,"user":{"displayName":"YIPING JIN","userId":"17124138946787548999"}},"outputId":"69c2e852-583c-494d-f168-b1405d72eb47"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Target identity: black people\n","              precision    recall  f1-score   support\n","\n","           0      0.837     0.367     0.510       281\n","           1      0.716     0.957     0.819       468\n","\n","    accuracy                          0.736       749\n","   macro avg      0.777     0.662     0.664       749\n","weighted avg      0.761     0.736     0.703       749\n","\n","Target identity: Muslims\n","              precision    recall  f1-score   support\n","\n","           0      0.888     0.584     0.705       190\n","           1      0.843     0.968     0.901       438\n","\n","    accuracy                          0.852       628\n","   macro avg      0.865     0.776     0.803       628\n","weighted avg      0.857     0.852     0.842       628\n","\n","Target identity: disabled people\n","              precision    recall  f1-score   support\n","\n","           0      0.401     0.911     0.557       224\n","           1      0.821     0.232     0.361       397\n","\n","    accuracy                          0.477       621\n","   macro avg      0.611     0.571     0.459       621\n","weighted avg      0.670     0.477     0.432       621\n","\n","Target identity: trans\n","              precision    recall  f1-score   support\n","\n","           0      0.439     0.908     0.592       217\n","           1      0.869     0.345     0.494       385\n","\n","    accuracy                          0.548       602\n","   macro avg      0.654     0.627     0.543       602\n","weighted avg      0.714     0.548     0.529       602\n","\n","Target identity: women\n","              precision    recall  f1-score   support\n","\n","           0      0.323     0.770     0.455       183\n","           1      0.714     0.262     0.383       401\n","\n","    accuracy                          0.421       584\n","   macro avg      0.518     0.516     0.419       584\n","weighted avg      0.592     0.421     0.406       584\n","\n","Target identity: gays\n","              precision    recall  f1-score   support\n","\n","           0      0.571     0.615     0.593       182\n","           1      0.830     0.803     0.816       426\n","\n","    accuracy                          0.747       608\n","   macro avg      0.701     0.709     0.704       608\n","weighted avg      0.753     0.747     0.749       608\n","\n","Target identity: immigrants\n","              precision    recall  f1-score   support\n","\n","           0      0.359     0.949     0.521       178\n","           1      0.949     0.355     0.516       468\n","\n","    accuracy                          0.519       646\n","   macro avg      0.654     0.652     0.519       646\n","weighted avg      0.786     0.519     0.518       646\n","\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"vQrFFE7wthkW"},"execution_count":null,"outputs":[]}]}