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1901
+ "\n",
1902
+ " <div>\n",
1903
+ " \n",
1904
+ " <progress value='6' max='6' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1905
+ " [6/6 00:23, Epoch 3/3]\n",
1906
+ " </div>\n",
1907
+ " <table border=\"1\" class=\"dataframe\">\n",
1908
+ " <thead>\n",
1909
+ " <tr style=\"text-align: left;\">\n",
1910
+ " <th>Step</th>\n",
1911
+ " <th>Training Loss</th>\n",
1912
+ " <th>Validation Loss</th>\n",
1913
+ " </tr>\n",
1914
+ " </thead>\n",
1915
+ " <tbody>\n",
1916
+ " </tbody>\n",
1917
+ "</table><p>"
1918
+ ]
1919
+ },
1920
+ "metadata": {}
1921
+ },
1922
+ {
1923
+ "output_type": "execute_result",
1924
+ "data": {
1925
+ "text/plain": [
1926
+ "('./fine_tuned_model/tokenizer_config.json',\n",
1927
+ " './fine_tuned_model/special_tokens_map.json',\n",
1928
+ " './fine_tuned_model/vocab.txt',\n",
1929
+ " './fine_tuned_model/added_tokens.json')"
1930
+ ]
1931
+ },
1932
+ "metadata": {},
1933
+ "execution_count": 2
1934
+ }
1935
+ ],
1936
+ "source": [
1937
+ "import re\n",
1938
+ "import pandas as pd\n",
1939
+ "import torch\n",
1940
+ "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
1941
+ "import nltk\n",
1942
+ "nltk.download('punkt')\n",
1943
+ "\n",
1944
+ "# ===========================\n",
1945
+ "# A. Data Collection and Preprocessing\n",
1946
+ "# ===========================\n",
1947
+ "# Load your narrative texts dataset (CSV with a \"text\" column)\n",
1948
+ "df = pd.read_csv(\"narrative_texts.csv\")\n",
1949
+ "\n",
1950
+ "# Normalize the text (lowercase, remove non-alphabetical characters)\n",
1951
+ "def normalize_text(text):\n",
1952
+ " text = text.lower()\n",
1953
+ " text = re.sub(r'[^a-z\\s]', '', text) # remove non-alphabetical characters\n",
1954
+ " text = re.sub(r'\\s+', ' ', text).strip()\n",
1955
+ " return text\n",
1956
+ "\n",
1957
+ "df['text'] = df['text'].apply(normalize_text)\n",
1958
+ "\n",
1959
+ "# ===========================\n",
1960
+ "# B. Counterfactual Data Augmentation and Gender-Swapping\n",
1961
+ "# ===========================\n",
1962
+ "def gender_swap(text):\n",
1963
+ " # Simplified swapping; extend dictionary as needed.\n",
1964
+ " swaps = {\n",
1965
+ " \" he \": \" TEMP \", \" she \": \" he \", \" TEMP \": \" she \",\n",
1966
+ " \" his \": \" TEMP2 \", \" her \": \" his \", \" TEMP2 \": \" her \",\n",
1967
+ " \" him \": \" TEMP3 \", \" her \": \" him \", \" TEMP3 \": \" her \"\n",
1968
+ " }\n",
1969
+ " for key, value in swaps.items():\n",
1970
+ " text = text.replace(key, value)\n",
1971
+ " return text\n",
1972
+ "\n",
1973
+ "df['text_swapped'] = df['text'].apply(gender_swap)\n",
1974
+ "\n",
1975
+ "def remove_gender_terms(text):\n",
1976
+ " pattern = r'\\b(he|she|him|his|her)\\b'\n",
1977
+ " return re.sub(pattern, '', text)\n",
1978
+ "\n",
1979
+ "df['text_removed'] = df['text'].apply(remove_gender_terms)\n",
1980
+ "\n",
1981
+ "# ===========================\n",
1982
+ "# C. Create Dataset Conditions for Fine-Tuning\n",
1983
+ "# ===========================\n",
1984
+ "# Condition 1: Original dataset\n",
1985
+ "df_original = df[['text']].copy()\n",
1986
+ "\n",
1987
+ "# Condition 2: Removed gender terms\n",
1988
+ "df_removed = df[['text_removed']].rename(columns={'text_removed': 'text'}).copy()\n",
1989
+ "\n",
1990
+ "# Condition 3: Mixed dataset (original + gender-swapped)\n",
1991
+ "df_mixed = pd.concat([df[['text']], df[['text_swapped']].rename(columns={'text_swapped': 'text'})], ignore_index=True)\n",
1992
+ "labels_mixed = [0] * len(df) + [1] * len(df) # Label 0: original, Label 1: swapped\n",
1993
+ "\n",
1994
+ "# ===========================\n",
1995
+ "# D. Fine-Tuning\n",
1996
+ "# ===========================\n",
1997
+ "# Load pretrained tokenizer and model (using BERT-base-uncased for demonstration)\n",
1998
+ "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
1999
+ "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n",
2000
+ "\n",
2001
+ "# Define a Torch Dataset\n",
2002
+ "class TextDataset(torch.utils.data.Dataset):\n",
2003
+ " def __init__(self, texts, labels):\n",
2004
+ " self.encodings = tokenizer(texts.tolist(), truncation=True, padding=True, max_length=128)\n",
2005
+ " self.labels = labels\n",
2006
+ "\n",
2007
+ " def __getitem__(self, idx):\n",
2008
+ " item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
2009
+ " item[\"labels\"] = torch.tensor(self.labels[idx])\n",
2010
+ " return item\n",
2011
+ "\n",
2012
+ " def __len__(self):\n",
2013
+ " return len(self.labels)\n",
2014
+ "\n",
2015
+ "dataset_mixed = TextDataset(df_mixed[\"text\"], labels_mixed)\n",
2016
+ "\n",
2017
+ "# Set up training arguments (logging to wandb is disabled)\n",
2018
+ "training_args = TrainingArguments(\n",
2019
+ " output_dir=\"./results\",\n",
2020
+ " num_train_epochs=3,\n",
2021
+ " per_device_train_batch_size=16,\n",
2022
+ " per_device_eval_batch_size=16,\n",
2023
+ " learning_rate=2e-5,\n",
2024
+ " evaluation_strategy=\"steps\",\n",
2025
+ " eval_steps=500,\n",
2026
+ " logging_dir=\"./logs\",\n",
2027
+ " report_to=[], # Disable external logging\n",
2028
+ " run_name=\"bias_bin_training\"\n",
2029
+ ")\n",
2030
+ "\n",
2031
+ "# Initialize Trainer\n",
2032
+ "trainer = Trainer(\n",
2033
+ " model=model,\n",
2034
+ " args=training_args,\n",
2035
+ " train_dataset=dataset_mixed,\n",
2036
+ " eval_dataset=dataset_mixed # In practice, use a separate validation set\n",
2037
+ ")\n",
2038
+ "\n",
2039
+ "# Fine-tune the model\n",
2040
+ "trainer.train()\n",
2041
+ "\n",
2042
+ "# Save the fine-tuned model and tokenizer for inference deployment\n",
2043
+ "model.save_pretrained(\"./fine_tuned_model\")\n",
2044
+ "tokenizer.save_pretrained(\"./fine_tuned_model\")\n"
2045
+ ]
2046
+ },
2047
+ {
2048
+ "cell_type": "code",
2049
+ "source": [],
2050
+ "metadata": {
2051
+ "id": "v78LA2dA9u6s"
2052
+ },
2053
+ "execution_count": null,
2054
+ "outputs": []
2055
+ }
2056
+ ]
2057
+ }