refreshed repository, added models with lfs

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.gitignore

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+# Ignore system files
+.DS_Store
+Thumbs.db
+
+# Ignore virtual environments
+venv/
+env/
+my_venv/
+.venv/
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+# Ignore Python cache
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+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# Ignore large data files
+data/raw/
+data/processed/
+*.csv
+*.json
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+
+# Ignore model weights & checkpoints
+*.h5
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+
+# Ignore logs
+*.log
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+
+# Ignore IDE files
+.vscode/
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+
+# Ignore node_modules
+node_modules/

Article-Bias-Prediction/.gitignore

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+**/.DS_Store
+**/__pycache__/
+*.pyc
+

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Article-Bias-Prediction/README.md

@@ -0,0 +1,38 @@
+# Article-Bias-Prediction
+
+## Dataset
+The articles crawled from www.allsides.com are available in the ```./data``` folder, along with the different evaluation splits.
+
+The dataset consists of a total of 37,554 articles. Each article is stored as a ```JSON``` object in the ```./data/jsons``` directory, and contains the following fields:
+1. **ID**: an alphanumeric identifier.
+2. **topic**: the topic being discussed in the article.
+3. **source**: the name of the articles's source *(example: New York Times)*
+4. **source_url**: the URL to the source's homepage *(example: www.nytimes.com)*
+5. **url**: the link to the actual article.
+6. **date**: the publication date of the article.
+7. **authors**: a comma-separated list of the article's authors.
+8. **title**: the article's title.
+9. **content_original**: the original body of the article, as returned by the ```newspaper3k``` Python library.
+10. **content**: the processed and tokenized content, which is used as input to the different models.
+11. **bias_text**: the label of the political bias annotation of the article (left, center, or right).
+12. **bias**: the numeric encoding of the political bias of the article (0, 1, or 2).
+
+The ```./data/splits``` directory contains the two types of splits, as discussed in the paper: **random** and **media-based**. For each of these types, we provide the train, validation and test files that contains the articles' IDs belonging to each set, along with their numeric bias label.
+
+## Code
+Under maintenance. To be available soon.
+
+## Citation
+
+```
+@inproceedings{baly2020we,
+  author      = {Baly, Ramy and Da San Martino, Giovanni and Glass, James and Nakov, Preslav},
+  title       = {We Can Detect Your Bias: Predicting the Political Ideology of News Articles},
+  booktitle   = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  series      = {EMNLP~'20},
+  NOmonth     = {November},
+  year        = {2020}
+  pages       = {4982--4991},
+  NOpublisher = {Association for Computational Linguistics}
+}
+```

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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Mathew Wagerman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Qbias/README.md

@@ -0,0 +1,24 @@
+# Qbias
+𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions
+
+## allsides_balanced_news_headlines-texts.csv
+The dataset contains 21,747 news articles collected from [AllSides balanced news headline roundups](https://www.allsides.com/headline-roundups) in November 2022. 
+The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The goal of the AllSides balanced news is to offer multiple political perspectives on important news stories, for educating users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles.
+Overall, the dataset contains 10,273 articled tagged as left, 7,222 tagged as right and 4,252 articles tagged as center. To provide easier access to the most recent and complete version of the dataset for future research, we provide the scraping tool.
+
+Data was scraped on 2022-11-15 from [https://www.allsides.com/headline-roundups](https://www.allsides.com/headline-roundups).
+
+## suggestions.csv 
+The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. 
+We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total amount of topics. 
+On average, the dataset contains 469 search queries for each topic.
+In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.
+
+The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion"	and "rank" represent the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".
+
+## AllSides Scraper
+The [AllSides platform](https://www.allsides.com/headline-roundups) and the provides news is a frequently used source for high quality labeled biased news. We want to provide an easy means of retrieving the news and all corresponding information.For many tasks, especially in the news domain, it is relevant to have the most recent documents available.
+Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information.
+By providing the scraper we facilitate access to a recent version of the dataset for other researchers.
+
+To use the scraper, run the jupyter notebook "AllsidesDataCrawl.ipynb", it will retreive all available documents automatically. You will need to download a version of the Chromedriver that works with your chrome browser and move it to the "driver" subfolder. The data will be exported as "allsides_news_complete.csv". You will find more detailed documentation on how to run and configure the scraper in the jupyter notebook.

Qbias/Scraper/AllsidesDataCrawl.ipynb

@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7f8595ab-ad00-435e-9d18-f92b0993609e",
+   "metadata": {},
+   "source": [
+    "## Import of required Modules\n",
+    "Make sure, that the imported moduls are installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "911b1609-a4df-4311-92b5-67b1ac3d3957",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import re\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from IPython.display import clear_output \n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b078c40-20cc-46c4-9a09-2947812fcfe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from selenium import webdriver\n",
+    "from selenium.webdriver.support import expected_conditions as EC\n",
+    "from selenium.webdriver.common.by import By\n",
+    "from selenium.webdriver.support.ui import WebDriverWait\n",
+    "from selenium.webdriver.chrome.options import Options\n",
+    "from selenium.webdriver.chrome.service import Service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38fc4c60-25aa-4d46-9d40-47b32a7e6c4f",
+   "metadata": {},
+   "source": [
+    "### Settings\n",
+    "change the settings to assert the scraper runs on your system. We reccomend to change the wait_time to a higher value if you run the scraper on a slower system or internet connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a51084c-dea2-4290-adb5-0e07dfefda02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wait_time = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "788d1a88-6955-4e93-a558-54a8488e7697",
+   "metadata": {},
+   "source": [
+    "#### set up Selenium and Chrome Driver \n",
+    "We use selenium with Chrome and tested the scraper with the chromedriver. You need the latest version of the driver from https://chromedriver.chromium.org/. Alternatively, change to the driver to a driver of your preferance.\n",
+    "We set up the scraper to run in the background, if you wish to run it in regular window mode, remove the line  \"chrome_options.add_argument(\"--headless\")\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94b6234d-8171-4a69-8547-4247ae6d3453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# chromedriver setup\n",
+    "\n",
+    "serv = Service(r'driver/chromedriver') #path from 'which chromedriver'\n",
+    "\n",
+    "# test driver\n",
+    "# for headless chrome mode\n",
+    "chrome_options = Options()\n",
+    "\n",
+    "# remove this line if you do not wish to run in background \n",
+    "chrome_options.add_argument(\"--headless\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a153cf31-4c05-4b1c-8699-60ab5630dcd5",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Retrieve Article Links for Crawl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1389d55b-9b73-42a8-83f9-a10b26caa7e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# assemble list of links to all articles\n",
+    "links = []\n",
+    "\n",
+    "# interact with cookie terms\n",
+    "chrome_path = r'driver/chromedriver' #path from 'which chromedriver'\n",
+    "chrome_options = Options()\n",
+    "chrome_options.add_argument(\"--headless\") # open chrome in background\n",
+    "driver = webdriver.Chrome(service=serv, options=chrome_options)\n",
+    "driver.get('https://www.allsides.com/headline-roundups')\n",
+    "wait = WebDriverWait(driver, 10)\n",
+    "wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'css-47sehv')))\n",
+    "\n",
+    "ele = driver.find_element(By.CLASS_NAME, \"css-47sehv\")\n",
+    "ele.click()\n",
+    "\n",
+    "# retireve number of pages \n",
+    "last_page_button = driver.find_element(By.CLASS_NAME, \"pager-last\")\n",
+    "link_last_page = last_page_button.find_elements(By.TAG_NAME, \"a\")\n",
+    "t = link_last_page[0].get_attribute(\"href\")\n",
+    "last_page_index = int(t[-3:])\n",
+    "\n",
+    "# retrieve links from start page\n",
+    "main_table = driver.find_element(By.XPATH, \"//*[@id=\\\"block-views-de37fa32ea86f5545eb9b7722977a70d\\\"]/div/div[2]/table/tbody\") # table body\n",
+    "\n",
+    "rows = main_table.find_elements(By.TAG_NAME, \"tr\")\n",
+    "for i in rows:\n",
+    "    entry = i.find_elements(By.TAG_NAME, \"td\")\n",
+    "    link = entry[0].find_element(By.TAG_NAME, \"a\")\n",
+    "    links.append(link.get_attribute(\"href\"))\n",
+    "WebDriverWait(driver, 20)\n",
+    "\n",
+    "# retrieve links for other pages\n",
+    "for page in tqdm(range(2,last_page_index+1)):   # set to max number of pages\n",
+    "    driver.get(\"https://www.allsides.com/headline-roundups?page=\"+str(page))\n",
+    "    WebDriverWait(driver, 20)\n",
+    "    main_table = driver.find_element(By.XPATH, \"//*[@id=\\\"block-views-de37fa32ea86f5545eb9b7722977a70d\\\"]/div/div[2]/table/tbody\") # table body\n",
+    "\n",
+    "    rows = main_table.find_elements(By.TAG_NAME, \"tr\")\n",
+    "    for i in rows:\n",
+    "        entry = i.find_elements(By.TAG_NAME, \"td\")\n",
+    "        link = entry[0].find_element(By.TAG_NAME, \"a\")\n",
+    "        links.append(link.get_attribute(\"href\"))\n",
+    "\n",
+    "driver.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d21eb71b-45c5-40df-b103-7bca92a26096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exports links as pickle file\n",
+    "with open(\"linklist_allsides_news.pickle\", \"wb\") as f:\n",
+    "    pickle.dump(links, f, pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e39f557-5a25-4159-b7ef-7d1e5d06faa6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exports links as csv\n",
+    "with open(\"linklist_allsides_news.csv\", \"w\") as f:\n",
+    "    for line in links:\n",
+    "        print(line, file=f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fde818ff-8d37-4e5e-bd97-b567414ec9c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# functions that assert the existence of \n",
+    "def check_exists_by_xpath(xpath):\n",
+    "    try:\n",
+    "        driver.find_elements(By.XPATH, xpath)[0]\n",
+    "    except:\n",
+    "        return False\n",
+    "    return True\n",
+    "\n",
+    "def check_exists_by_class(inp):\n",
+    "    try:\n",
+    "        driver.find_elements(By.CLASS_NAME, inp)[0]\n",
+    "    except:\n",
+    "        return False\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411f5734-5415-45ac-ad47-edad75ceeb8f",
+   "metadata": {},
+   "source": [
+    "## Retrieve Articles\n",
+    "This section of the crawler retrieves all available news articles from AllSides along with the available information and bias tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "068d8b13-8ad6-47e9-acba-94020b6900a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load link list from pickle file\n",
+    "with open(\"linklist_allsides_news.pickle\", \"rb\") as f:\n",
+    "    links = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4a05fcc-8ba5-45fa-a79a-3da26b3a12c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# list for results \n",
+    "data = []\n",
+    "\n",
+    "# retrieve information from articles in list of links\n",
+    "for li in tqdm(links):\n",
+    "    time.sleep(wait_time)\n",
+    "    driver = webdriver.Chrome(service=serv, options=chrome_options)\n",
+    "    print(li)\n",
+    "    # open URL\n",
+    "    driver.get(li)\n",
+    "    \n",
+    "    # interact with pop-up window\n",
+    "    if check_exists_by_class(\"css-47sehv\"):\n",
+    "        ele = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((driver.find_element(By.CLASS_NAME, \"css-47sehv\"))))\n",
+    "        ele.click()\n",
+    "    else:\n",
+    "        print(\"no button\")\n",
+    "    \n",
+    "    # netral title heading \n",
+    "    try:\n",
+    "        heading = driver.find_element(By.TAG_NAME, \"h1\").text\n",
+    "    except:\n",
+    "        print(\"no heading found\")\n",
+    "    \n",
+    "    print(heading)\n",
+    "    \n",
+    "    # date\n",
+    "    try:\n",
+    "        date = driver.find_element(By.CLASS_NAME, \"date-display-single\").text\n",
+    "    except:\n",
+    "        date = \"\"\n",
+    "    \n",
+    "    # tags\n",
+    "    try:\n",
+    "        tags = [a.text for a in driver.find_element(By.CLASS_NAME, \"page-tags\").find_elements(By.TAG_NAME, \"a\")]\n",
+    "    except:\n",
+    "        tags = \"\"\n",
+    "        \n",
+    "    \n",
+    "    # define XPATH inforamtion for article divs\n",
+    "    divs = [\"/html/body/div[4]/div/div/div/div[4]/div/div/div/div[1]\", \"/html/body/div[4]/div/div/div/div[4]/div/div/div/div[2]\", \"/html/body/div[4]/div/div/div/div[4]/div/div/div/div[3]\"]\n",
+    "    \n",
+    "    # access information in article divs\n",
+    "    for d in divs:\n",
+    "        if check_exists_by_xpath(d):\n",
+    "            div = driver.find_elements(By.XPATH, d)[0]\n",
+    "\n",
+    "            # check heading element to find out left/center/right. The title contains the bias label that we can retrieve from the text here\n",
+    "            try:\n",
+    "                cat = div.find_element(By.TAG_NAME, \"h3\").text\n",
+    "            except: \n",
+    "                print(\"no headline found\")\n",
+    "                \n",
+    "            # retrieve link to original article\n",
+    "            try:\n",
+    "                link = div.find_element(By.TAG_NAME, \"a\").get_attribute(\"href\")\n",
+    "                print(link)\n",
+    "            except:\n",
+    "                print(\"no link found\")\n",
+    "                \n",
+    "            # left/center/right are shuffled for each article, some roundups have e.g. only left and right articles. \n",
+    "            # Thus, we have look to look each article seperately\n",
+    "            time.sleep(0.2)\n",
+    "            if \"Left\" in cat:\n",
+    "                print(\"left\")\n",
+    "                try:\n",
+    "                    left_heading = div.find_element(By.CLASS_NAME, \"news-title\").text # heading       \n",
+    "                except:\n",
+    "                    left_heading = \"\"\n",
+    "                    print(\"no headline found\")\n",
+    "                try:\n",
+    "                    left_source = div.find_element(By.CLASS_NAME, \"source-area\").find_element(By.TAG_NAME, \"span\").text #source\n",
+    "                except:\n",
+    "                    left_source = \"\"\n",
+    "                    print(\"no source found\")\n",
+    "                try:\n",
+    "                    left_text = div.find_element(By.CLASS_NAME, \"news-body\").find_element(By.CLASS_NAME, \"body-contents\").text # news text body-contents\n",
+    "                except:\n",
+    "                    left_text = \"\"\n",
+    "                    print(\"no text found\")\n",
+    "                    \n",
+    "                # add the article information\n",
+    "                data.append({\"url\":link, \"date\":date, \"title\": heading, \"tags\": tags, \"heading\":left_heading, \"source\": left_source, \"text\": left_text, \"bias_rating\": \"left\"})\n",
+    "\n",
+    "            elif \"Right\" in cat:\n",
+    "                print(\"right\")\n",
+    "                try:\n",
+    "                    right_heading = div.find_element(By.CLASS_NAME, \"news-title\").text # heading\n",
+    "                except:\n",
+    "                    right_heading = \"\"\n",
+    "                    print(\"no headline found\")\n",
+    "                try:\n",
+    "                    right_source = div.find_element(By.CLASS_NAME, \"source-area\").find_element(By.TAG_NAME, \"span\").text #source \n",
+    "                except:\n",
+    "                    right_source = \"\"\n",
+    "                    print(\"no source found\")\n",
+    "                try:\n",
+    "                    right_text = div.find_element(By.CLASS_NAME, \"news-body\").find_element(By.CLASS_NAME, \"body-contents\").text # news text\n",
+    "                except:\n",
+    "                    right_text = \"\"\n",
+    "                    print(\"no text found\")\n",
+    "                \n",
+    "                # add the article information\n",
+    "                data.append({\"url\":link, \"date\":date, \"title\": heading, \"tags\": tags, \"heading\":right_heading, \"source\": right_source, \"text\": right_text, \"bias_rating\": \"right\"}) \n",
+    "\n",
+    "            else:\n",
+    "                print(\"center\")\n",
+    "                try:\n",
+    "                    center_heading = div.find_element(By.CLASS_NAME, \"news-title\").text # heading\n",
+    "                except:\n",
+    "                    center_heading = \"\"\n",
+    "                    print(\"no headline found\")\n",
+    "                try:\n",
+    "                    center_source = div.find_element(By.CLASS_NAME, \"source-area\").find_element(By.TAG_NAME, \"span\").text #source \n",
+    "                except:\n",
+    "                    center_source = \"\"\n",
+    "                    print(\"no source found\")\n",
+    "                try:\n",
+    "                    center_text = div.find_element(By.CLASS_NAME, \"news-body\").find_element(By.CLASS_NAME, \"body-contents\").text # news text\n",
+    "                except:\n",
+    "                    center_text = \"\"\n",
+    "                    print(\"no text found\")\n",
+    "                    \n",
+    "                # add the article information\n",
+    "                data.append({\"url\":link, \"date\":date, \"title\": heading, \"tags\": tags, \"heading\":center_heading, \"source\": center_source, \"text\": center_text, \"bias_rating\": \"center\"})\n",
+    "        else:\n",
+    "            print(\"div not found\")\n",
+    "    \n",
+    "    driver.close()\n",
+    "    \n",
+    "    # clear output\n",
+    "    clear_output()\n",
+    "    # added a wait here to assert the scraper runs well\n",
+    "    time.sleep(wait_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2b06a89-ddec-4572-ba28-0856fbb4d44d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## convert data to dataframe\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# export scraped articles\n",
+    "df.to_csv(\"allsides_news_complete.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Qbias/Scraper/driver/readme.txt

@@ -0,0 +1 @@
+add your Chromedriver here

Qbias/datasetcleanup.py

@@ -0,0 +1,41 @@
+
+'''
+import pandas as pd
+from sklearn.utils import resample
+
+# Load the cleaned dataset
+df = pd.read_csv("cleaned_qbias_dataset.csv")
+
+# Separate majority and minority classes
+df_left = df[df.label == 0]
+df_center = df[df.label == 1]
+df_right = df[df.label == 2]
+
+# Determine target size (matching the majority class: 'left')
+target_size = len(df_left)
+
+# Upsample center class to match 'left'
+df_center_upsampled = resample(
+    df_center,
+    replace=True,
+    n_samples=target_size,
+    random_state=42
+)
+
+# Combine all classes into one balanced DataFrame
+df_balanced = pd.concat([df_left, df_center_upsampled, df_right])
+
+# Shuffle the final dataset
+df_balanced = df_balanced.sample(frac=1, random_state=42).reset_index(drop=True)
+
+# Save to new CSV
+df_balanced.to_csv("cleaned_qbias_balanced.csv", index=False)
+
+print("Balanced dataset saved as cleaned_qbias_balanced.csv")
+print(df_balanced['label'].value_counts())
+
+'''
+
+import pandas as pd
+df = pd.read_csv("cleaned_qbias_dataset.csv")
+print(df['label'].value_counts())

README.md

@@ -0,0 +1,51 @@
+---
+tags:
+  - bias-detection
+  - nlp
+  - peft
+  - lora
+  - fine-tuning
+license: mit
+datasets:
+  - ...
+model-index:
+  - name: Bias Detector
+    results:
+      - task:
+          type: text-classification
+        dataset:
+          name: ...
+          type: ...
+        metrics:
+          - type: accuracy
+            value: ...
+---
+
+# Bias Detector
+
+This model is fine-tuned using **PEFT LoRA** on existing **Hugging Face models** to classify and evaluate the bias in news sources. 
+
+## Model Details
+- **Architecture:** Transformer-based (e.g., BERT, RoBERTa)
+- **Fine-tuning Method:** Parameter Efficient Fine-Tuning (LoRA)
+- **Use Case:** Bias classification, text summarization, sentiment analysis
+- **Dataset:** [...](https://huggingface.co/datasets/your-dataset)
+- **Training Framework:** PyTorch + Transformers
+
+## Usage
+To use this model, install the necessary libraries:
+```bash
+pip install transformers torch
+```
+Then load the model with:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "mjwagerman/bias-detector"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "This is an example news headline."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```

bert-bias-detector/checkpoint-2447/model.safetensors

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berttrainedonallsides.py

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+from datasets import load_dataset
+from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+import torch
+
+model_name = "bert-base-uncased"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+print("Model loaded and moved to device:", device)
+
+# Load the dataset
+dataset = load_dataset(
+    "csv",
+    data_files="Qbias/allsides_balanced_news_headlines-texts.csv"
+)["train"]
+
+# Map string labels to integers
+def label_map(example):
+    mapping = {"left": 0, "center": 1, "right": 2}
+    example["label"] = mapping[example["bias_rating"].strip().lower()]
+    return example
+
+dataset = dataset.map(label_map)
+
+# Train/test split
+dataset = dataset.train_test_split(test_size=0.1)
+
+# Tokenization
+def tokenize_function(example):
+    return tokenizer(example["text"], padding="max_length", truncation=True, max_length=512)
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
+
+# Training setup
+training_args = TrainingArguments(
+    output_dir="./bert-allsides-bias-detector",
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=500,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["test"],
+    tokenizer=tokenizer,
+)
+
+# Train
+#trainer.train()

datasettesting.py

@@ -0,0 +1,6 @@
+from datasets import load_dataset
+
+dataset = load_dataset("newsmediabias/instruction-safe-llm")
+
+
+print(dataset)

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