diff --git a/examples/community_lm/community_lm.ipynb b/examples/community_lm/community_lm.ipynb index 149214d..02c47cd 100644 --- a/examples/community_lm/community_lm.ipynb +++ b/examples/community_lm/community_lm.ipynb @@ -20,20 +20,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "29563e5d-41b0-4f89-8d8b-a54b40f8dfb7", "metadata": {}, "outputs": [], "source": [ "from llments.lm.base.hugging_face import HuggingFaceLM, HuggingFaceLMFitter\n", - "# from llments.lm.base.empirical import load_from_text_file\n", + "from llments.lm.base.empirical import load_from_text_file\n", "from llments.eval.sentiment import HuggingFaceSentimentEvaluator\n", "import pandas as pd\n", "import numpy as np\n", - "from examples.community_lm.community_lm_constants import politician_feelings, groups_feelings, anes_df\n", - "from examples.community_lm.community_lm_utils import generate_community_opinion, compute_group_stance\n", + "from community_lm_constants import politician_feelings, groups_feelings, anes_df\n", + "from community_lm_utils import generate_community_opinion, compute_group_stance\n", "\n", - "device = 'cuda' # change to 'mps' if you have a mac, or 'cuda:0' if you have an NVIDIA GPU " + "device = 'mps' # change to 'mps' if you have a mac, or 'cuda:0' if you have an NVIDIA GPU " ] }, { @@ -101,172 +101,7 @@ "execution_count": null, "id": "d2049390", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n", - "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_1 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 33%|███▎ | 10/30 [00:37<01:22, 4.14s/it]--- Logging error ---\n", - "Traceback (most recent call last):\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/logging/__init__.py\", line 1110, in emit\n", - " msg = self.format(record)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/logging/__init__.py\", line 953, in format\n", - " return fmt.format(record)\n", - " ^^^^^^^^^^^^^^^^^^\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/logging/__init__.py\", line 687, in format\n", - " record.message = record.getMessage()\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/logging/__init__.py\", line 377, in getMessage\n", - " msg = msg % self.args\n", - " ~~~~^~~~~~~~~~~\n", - "TypeError: not all arguments converted during string formatting\n", - "Call stack:\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/base_events.py\", line 608, in run_forever\n", - " self._run_once()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/base_events.py\", line 1936, in _run_once\n", - " handle._run()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 359, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 446, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/tmp/ipykernel_1778592/4055455867.py\", line 6, in \n", - " compute_group_stance(\n", - " File \"/home/mihirban/llments/examples/community_lm/community_lm_utils.py\", line 155, in compute_group_stance\n", - " sentiment_vals = evaluator.evaluate_batch(\n", - " File \"/home/mihirban/llments/llments/eval/sentiment.py\", line 105, in evaluate_batch\n", - " for x in self.sentiment_pipeline(minibatch)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/pipelines/text_classification.py\", line 156, in __call__\n", - " result = super().__call__(*inputs, **kwargs)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py\", line 1167, in __call__\n", - " logger.warning_once(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/utils/logging.py\", line 329, in warning_once\n", - " self.warning(*args, **kwargs)\n", - "Message: 'You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset'\n", - "Arguments: (,)\n", - "Processing questions: 100%|██████████| 30/30 [02:01<00:00, 4.05s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.18s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.18s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_2 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 100%|██████████| 30/30 [02:04<00:00, 4.16s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.18s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.18s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_3 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:04<00:00, 4.17s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_4 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.18s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_5 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 100%|██████████| 30/30 [02:05<00:00, 4.17s/it]\n", - "Processing questions: 13%|█▎ | 4/30 [00:16<01:48, 4.18s/it]" - ] - } - ], + "outputs": [], "source": [ "evaluator = HuggingFaceSentimentEvaluator(\n", " \"cardiffnlp/twitter-roberta-base-sentiment-latest\",\n", @@ -349,7 +184,7 @@ "df_politician_results['Prompt4'] = anes_df['Prompt4'].to_list()\n", "\n", "df_politician_results['pid'] = df_politician_results.index\n", - "df_politician_results.to_csv(\"output/anes2020_pilot_prompt_probing_ft.csv\", index=False)\n", + "df_politician_results.to_csv(\"output/anes2020_pilot_prompt_probing.csv\", index=False)\n", "# df_politician_results" ] }, @@ -420,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv(\"output/anes2020_pilot_prompt_probing_ft.csv\")\n", + "df = pd.read_csv(\"output/anes2020_pilot_prompt_probing.csv\")\n", "df_scores = compute_scores(df, df_dem, df_repub)\n", "df_scores" ] @@ -570,14 +405,6 @@ "plt.tight_layout()\n", "plt.savefig('rankings/gold_repub_rank.png', bbox_inches = \"tight\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5082d3c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -585,6 +412,18 @@ "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": "3.11.7" } }, "nbformat": 4, diff --git a/examples/community_lm/community_lm_rag.ipynb b/examples/community_lm/community_lm_rag.ipynb index 0a88963..b485f2b 100644 --- a/examples/community_lm/community_lm_rag.ipynb +++ b/examples/community_lm/community_lm_rag.ipynb @@ -107,1810 +107,7 @@ "execution_count": 4, "id": "b5685fcb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading the index...\n", - "Index loaded successfully!\n", - "Loading the document file...\n", - "Documents loaded successfully!\n", - "generating democrat opinion for Prompt1 run 1...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generating opinions: 0%| | 0/30 [00:00\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/base_events.py\", line 608, in run_forever\n", - " self._run_once()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/base_events.py\", line 1936, in _run_once\n", - " handle._run()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/asyncio/events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 359, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 446, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/tmp/ipykernel_1776716/2258336994.py\", line 6, in \n", - " compute_group_stance(\n", - " File \"/home/mihirban/llments/examples/community_lm/community_lm_utils.py\", line 155, in compute_group_stance\n", - " sentiment_vals = evaluator.evaluate_batch(\n", - " File \"/home/mihirban/llments/llments/eval/sentiment.py\", line 105, in evaluate_batch\n", - " for x in self.sentiment_pipeline(minibatch)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/pipelines/text_classification.py\", line 156, in __call__\n", - " result = super().__call__(*inputs, **kwargs)\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py\", line 1167, in __call__\n", - " logger.warning_once(\n", - " File \"/home/mihirban/miniconda3/lib/python3.11/site-packages/transformers/utils/logging.py\", line 329, in warning_once\n", - " self.warning(*args, **kwargs)\n", - "Message: 'You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset'\n", - "Arguments: (,)\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.36it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.34it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.33it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_2 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.36it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.34it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.32it/s]\n", - "Processing questions: 100%|██████████| 30/30 [00:12<00:00, 2.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing run_3 ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - 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pid1rDemocratRepublicanis_repub_leading
fttrump117.66437877.833163True
ftobama181.29270429.992821False
ftbiden166.49742324.401235False
ftwarren159.84347820.457680False
ftsanders163.76982820.503597False
ftbuttigieg152.78660721.663441False
ftharris152.12088918.633008False
ftklobuchar150.04343922.171367False
ftpence124.09184571.121524True
ftyang149.27913329.191176False
ftpelosi161.75817616.098664False
ftrubio131.52205243.008547True
ftocasioc150.59660116.489752False
fthaley129.86053147.069714True
ftthomas129.95000048.631868True
ftfauci166.66987758.275899False
ftblack76.21963866.510309False
ftwhite71.25000077.156540True
fthisp71.26936363.595876False
ftasian68.94554963.443756False
ftillegal56.16982826.246646False
ftfeminists61.96793833.920372False
ftmetoo63.73977432.730851False
fttransppl63.21761735.061983False
ftsocialists54.00086924.106029False
ftcapitalists46.67565260.533054True
ftbigbusiness43.14211457.847826True
ftlaborunions60.67247444.870466False
ftrepublicanparty25.01643674.465641True
ftdemocraticparty71.23768424.344262False
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pid1rDemocratRepublicanis_repub_leadingPrompt1Prompt2Prompt3Prompt4pid
fttrump117.66437877.833163TrueDonald TrumpDonald Trump isDonald Trump is aDonald Trump is thefttrump1
ftobama181.29270429.992821FalseBarack ObamaBarack Obama isBarack Obama is aBarack Obama is theftobama1
ftbiden166.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden1
ftwarren159.84347820.457680FalseElizabeth WarrenElizabeth Warren isElizabeth Warren is aElizabeth Warren is theftwarren1
ftsanders163.76982820.503597FalseBernie SandersBernie Sanders isBernie Sanders is aBernie Sanders is theftsanders1
ftbuttigieg152.78660721.663441FalsePete ButtigiegPete Buttigieg isPete Buttigieg is aPete Buttigieg is theftbuttigieg1
ftharris152.12088918.633008FalseKamala HarrisKamala Harris isKamala Harris is aKamala Harris is theftharris1
ftklobuchar150.04343922.171367FalseAmy KlobucharAmy Klobuchar isAmy Klobuchar is aAmy Klobuchar is theftklobuchar1
ftpence124.09184571.121524TrueMike PenceMike Pence isMike Pence is aMike Pence is theftpence1
ftyang149.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang1
ftpelosi161.75817616.098664FalseNancy PelosiNancy Pelosi isNancy Pelosi is aNancy Pelosi is theftpelosi1
ftrubio131.52205243.008547TrueMarco RubioMarco Rubio isMarco Rubio is aMarco Rubio is theftrubio1
ftocasioc150.59660116.489752FalseAlexandria Ocasio-CortezAlexandria Ocasio-Cortez isAlexandria Ocasio-Cortez is aAlexandria Ocasio-Cortez is theftocasioc1
fthaley129.86053147.069714TrueNikki HaleyNikki Haley isNikki Haley is aNikki Haley is thefthaley1
ftthomas129.95000048.631868TrueClarence ThomasClarence Thomas isClarence Thomas is aClarence Thomas is theftthomas1
ftfauci166.66987758.275899FalseDr. Anthony FauciDr. Anthony Fauci isDr. Anthony Fauci is aDr. Anthony Fauci is theftfauci1
ftblack76.21963866.510309FalseBlack peopleBlack people areBlack people are aBlack people are theftblack
ftwhite71.25000077.156540TrueWhite peopleWhite people areWhite people are aWhite people are theftwhite
fthisp71.26936363.595876FalseHispanic peopleHispanic people areHispanic people are aHispanic people are thefthisp
ftasian68.94554963.443756FalseAsian peopleAsian people areAsian people are aAsian people are theftasian
ftillegal56.16982826.246646Falseillegal immigrantsillegal immigrants areillegal immigrants are aillegal immigrants are theftillegal
ftfeminists61.96793833.920372Falsefeministsfeminists arefeminists are afeminists are theftfeminists
ftmetoo63.73977432.730851Falsethe #MeToo movementthe #MeToo movement isthe #MeToo movement is athe #MeToo movement is theftmetoo
fttransppl63.21761735.061983Falsetransgender peopletransgender people aretransgender people are atransgender people are thefttransppl
ftsocialists54.00086924.106029Falsesocialistssocialists aresocialists are asocialists are theftsocialists
ftcapitalists46.67565260.533054Truecapitalistscapitalists arecapitalists are acapitalists are theftcapitalists
ftbigbusiness43.14211457.847826Truebig businessbig business isbig business is abig business is theftbigbusiness
ftlaborunions60.67247444.870466Falselabor unionslabor unions islabor unions is alabor unions is theftlaborunions
ftrepublicanparty25.01643674.465641Truethe Republican Partythe Republican Party isthe Republican Party is athe Republican Party is theftrepublicanparty
ftdemocraticparty71.23768424.344262Falsethe Democratic Partythe Democratic Party isthe Democratic Party is athe Democratic Party is theftdemocraticparty
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" - ], - "text/plain": [ - "pid1r Democrat Republican is_repub_leading \\\n", - "fttrump1 17.664378 77.833163 True \n", - "ftobama1 81.292704 29.992821 False \n", - "ftbiden1 66.497423 24.401235 False \n", - "ftwarren1 59.843478 20.457680 False \n", - "ftsanders1 63.769828 20.503597 False \n", - "ftbuttigieg1 52.786607 21.663441 False \n", - "ftharris1 52.120889 18.633008 False \n", - "ftklobuchar1 50.043439 22.171367 False \n", - "ftpence1 24.091845 71.121524 True \n", - "ftyang1 49.279133 29.191176 False \n", - "ftpelosi1 61.758176 16.098664 False \n", - "ftrubio1 31.522052 43.008547 True \n", - "ftocasioc1 50.596601 16.489752 False \n", - "fthaley1 29.860531 47.069714 True \n", - "ftthomas1 29.950000 48.631868 True \n", - "ftfauci1 66.669877 58.275899 False \n", - "ftblack 76.219638 66.510309 False \n", - "ftwhite 71.250000 77.156540 True \n", - "fthisp 71.269363 63.595876 False \n", - "ftasian 68.945549 63.443756 False \n", - "ftillegal 56.169828 26.246646 False \n", - "ftfeminists 61.967938 33.920372 False \n", - "ftmetoo 63.739774 32.730851 False \n", - "fttransppl 63.217617 35.061983 False \n", - "ftsocialists 54.000869 24.106029 False \n", - "ftcapitalists 46.675652 60.533054 True \n", - "ftbigbusiness 43.142114 57.847826 True \n", - "ftlaborunions 60.672474 44.870466 False \n", - "ftrepublicanparty 25.016436 74.465641 True \n", - "ftdemocraticparty 71.237684 24.344262 False \n", - "\n", - "pid1r Prompt1 Prompt2 \\\n", - "fttrump1 Donald Trump Donald Trump is \n", - "ftobama1 Barack Obama Barack Obama is \n", - "ftbiden1 Joe Biden Joe Biden is \n", - "ftwarren1 Elizabeth Warren Elizabeth Warren is \n", - "ftsanders1 Bernie Sanders Bernie Sanders is \n", - "ftbuttigieg1 Pete Buttigieg Pete Buttigieg is \n", - "ftharris1 Kamala Harris Kamala Harris is \n", - "ftklobuchar1 Amy Klobuchar Amy Klobuchar is \n", - "ftpence1 Mike Pence Mike Pence is \n", - "ftyang1 Andrew Yang Andrew Yang is \n", - "ftpelosi1 Nancy Pelosi Nancy Pelosi is \n", - "ftrubio1 Marco Rubio Marco Rubio is \n", - "ftocasioc1 Alexandria Ocasio-Cortez Alexandria Ocasio-Cortez is \n", - "fthaley1 Nikki Haley Nikki Haley is \n", - "ftthomas1 Clarence Thomas Clarence Thomas is \n", - "ftfauci1 Dr. Anthony Fauci Dr. Anthony Fauci is \n", - "ftblack Black people Black people are \n", - "ftwhite White people White people are \n", - "fthisp Hispanic people Hispanic people are \n", - "ftasian Asian people Asian people are \n", - "ftillegal illegal immigrants illegal immigrants are \n", - "ftfeminists feminists feminists are \n", - "ftmetoo the #MeToo movement the #MeToo movement is \n", - "fttransppl transgender people transgender people are \n", - "ftsocialists socialists socialists are \n", - "ftcapitalists capitalists capitalists are \n", - "ftbigbusiness big business big business is \n", - "ftlaborunions labor unions labor unions is \n", - "ftrepublicanparty the Republican Party the Republican Party is \n", - "ftdemocraticparty the Democratic Party the Democratic Party is \n", - "\n", - "pid1r Prompt3 \\\n", - "fttrump1 Donald Trump is a \n", - "ftobama1 Barack Obama is a \n", - "ftbiden1 Joe Biden is a \n", - "ftwarren1 Elizabeth Warren is a \n", - "ftsanders1 Bernie Sanders is a \n", - "ftbuttigieg1 Pete Buttigieg is a \n", - "ftharris1 Kamala Harris is a \n", - "ftklobuchar1 Amy Klobuchar is a \n", - "ftpence1 Mike Pence is a \n", - "ftyang1 Andrew Yang is a \n", - "ftpelosi1 Nancy Pelosi is a \n", - "ftrubio1 Marco Rubio is a \n", - "ftocasioc1 Alexandria Ocasio-Cortez is a \n", - "fthaley1 Nikki Haley is a \n", - "ftthomas1 Clarence Thomas is a \n", - "ftfauci1 Dr. Anthony Fauci is a \n", - "ftblack Black people are a \n", - "ftwhite White people are a \n", - "fthisp Hispanic people are a \n", - "ftasian Asian people are a \n", - "ftillegal illegal immigrants are a \n", - "ftfeminists feminists are a \n", - "ftmetoo the #MeToo movement is a \n", - "fttransppl transgender people are a \n", - "ftsocialists socialists are a \n", - "ftcapitalists capitalists are a \n", - "ftbigbusiness big business is a \n", - "ftlaborunions labor unions is a \n", - "ftrepublicanparty the Republican Party is a \n", - 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pid1rDemocratRepublicanis_repub_leadingPrompt1Prompt2Prompt3Prompt4piddiff
fttrump117.66437877.833163TrueDonald TrumpDonald Trump isDonald Trump is aDonald Trump is thefttrump160.168785
ftobama181.29270429.992821FalseBarack ObamaBarack Obama isBarack Obama is aBarack Obama is theftobama151.299883
ftbiden166.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188
ftwarren159.84347820.457680FalseElizabeth WarrenElizabeth Warren isElizabeth Warren is aElizabeth Warren is theftwarren139.385798
ftsanders163.76982820.503597FalseBernie SandersBernie Sanders isBernie Sanders is aBernie Sanders is theftsanders143.266230
ftbuttigieg152.78660721.663441FalsePete ButtigiegPete Buttigieg isPete Buttigieg is aPete Buttigieg is theftbuttigieg131.123166
ftharris152.12088918.633008FalseKamala HarrisKamala Harris isKamala Harris is aKamala Harris is theftharris133.487881
ftklobuchar150.04343922.171367FalseAmy KlobucharAmy Klobuchar isAmy Klobuchar is aAmy Klobuchar is theftklobuchar127.872072
ftpence124.09184571.121524TrueMike PenceMike Pence isMike Pence is aMike Pence is theftpence147.029679
ftyang149.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956
ftpelosi161.75817616.098664FalseNancy PelosiNancy Pelosi isNancy Pelosi is aNancy Pelosi is theftpelosi145.659512
ftrubio131.52205243.008547TrueMarco RubioMarco Rubio isMarco Rubio is aMarco Rubio is theftrubio111.486495
ftocasioc150.59660116.489752FalseAlexandria Ocasio-CortezAlexandria Ocasio-Cortez isAlexandria Ocasio-Cortez is aAlexandria Ocasio-Cortez is theftocasioc134.106849
fthaley129.86053147.069714TrueNikki HaleyNikki Haley isNikki Haley is aNikki Haley is thefthaley117.209183
ftthomas129.95000048.631868TrueClarence ThomasClarence Thomas isClarence Thomas is aClarence Thomas is theftthomas118.681868
ftfauci166.66987758.275899FalseDr. Anthony FauciDr. Anthony Fauci isDr. Anthony Fauci is aDr. Anthony Fauci is theftfauci18.393979
ftblack76.21963866.510309FalseBlack peopleBlack people areBlack people are aBlack people are theftblack9.709329
ftwhite71.25000077.156540TrueWhite peopleWhite people areWhite people are aWhite people are theftwhite5.906540
fthisp71.26936363.595876FalseHispanic peopleHispanic people areHispanic people are aHispanic people are thefthisp7.673487
ftasian68.94554963.443756FalseAsian peopleAsian people areAsian people are aAsian people are theftasian5.501792
ftillegal56.16982826.246646Falseillegal immigrantsillegal immigrants areillegal immigrants are aillegal immigrants are theftillegal29.923182
ftfeminists61.96793833.920372Falsefeministsfeminists arefeminists are afeminists are theftfeminists28.047565
ftmetoo63.73977432.730851Falsethe #MeToo movementthe #MeToo movement isthe #MeToo movement is athe #MeToo movement is theftmetoo31.008923
fttransppl63.21761735.061983Falsetransgender peopletransgender people aretransgender people are atransgender people are thefttransppl28.155633
ftsocialists54.00086924.106029Falsesocialistssocialists aresocialists are asocialists are theftsocialists29.894840
ftcapitalists46.67565260.533054Truecapitalistscapitalists arecapitalists are acapitalists are theftcapitalists13.857401
ftbigbusiness43.14211457.847826Truebig businessbig business isbig business is abig business is theftbigbusiness14.705712
ftlaborunions60.67247444.870466Falselabor unionslabor unions islabor unions is alabor unions is theftlaborunions15.802008
ftrepublicanparty25.01643674.465641Truethe Republican Partythe Republican Party isthe Republican Party is athe Republican Party is theftrepublicanparty49.449205
ftdemocraticparty71.23768424.344262Falsethe Democratic Partythe Democratic Party isthe Democratic Party is athe Democratic Party is theftdemocraticparty46.893421
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"ftmetoo 63.739774 32.730851 False \n", - "fttransppl 63.217617 35.061983 False \n", - "ftsocialists 54.000869 24.106029 False \n", - "ftcapitalists 46.675652 60.533054 True \n", - "ftbigbusiness 43.142114 57.847826 True \n", - "ftlaborunions 60.672474 44.870466 False \n", - "ftrepublicanparty 25.016436 74.465641 True \n", - "ftdemocraticparty 71.237684 24.344262 False \n", - "\n", - "pid1r Prompt1 Prompt2 \\\n", - "fttrump1 Donald Trump Donald Trump is \n", - "ftobama1 Barack Obama Barack Obama is \n", - "ftbiden1 Joe Biden Joe Biden is \n", - "ftwarren1 Elizabeth Warren Elizabeth Warren is \n", - "ftsanders1 Bernie Sanders Bernie Sanders is \n", - "ftbuttigieg1 Pete Buttigieg Pete Buttigieg is \n", - "ftharris1 Kamala Harris Kamala Harris is \n", - "ftklobuchar1 Amy Klobuchar Amy Klobuchar is \n", - "ftpence1 Mike Pence Mike Pence is \n", - "ftyang1 Andrew Yang Andrew Yang is \n", - "ftpelosi1 Nancy Pelosi Nancy Pelosi is \n", - "ftrubio1 Marco Rubio Marco Rubio is \n", - "ftocasioc1 Alexandria Ocasio-Cortez Alexandria Ocasio-Cortez is \n", - "fthaley1 Nikki Haley Nikki Haley is \n", - "ftthomas1 Clarence Thomas Clarence Thomas is \n", - "ftfauci1 Dr. Anthony Fauci Dr. Anthony Fauci is \n", - "ftblack Black people Black people are \n", - "ftwhite White people White people are \n", - "fthisp Hispanic people Hispanic people are \n", - "ftasian Asian people Asian people are \n", - "ftillegal illegal immigrants illegal immigrants are \n", - "ftfeminists feminists feminists are \n", - "ftmetoo the #MeToo movement the #MeToo movement is \n", - "fttransppl transgender people transgender people are \n", - "ftsocialists socialists socialists are \n", - "ftcapitalists capitalists capitalists are \n", - "ftbigbusiness big business big business is \n", - "ftlaborunions labor unions labor unions is \n", - "ftrepublicanparty the Republican Party the Republican Party is \n", - "ftdemocraticparty the Democratic Party the Democratic Party is \n", - "\n", - "pid1r Prompt3 \\\n", - "fttrump1 Donald Trump is a \n", - "ftobama1 Barack Obama is a \n", - "ftbiden1 Joe Biden is a \n", - "ftwarren1 Elizabeth Warren is a \n", - "ftsanders1 Bernie Sanders is a \n", - "ftbuttigieg1 Pete Buttigieg is a \n", - "ftharris1 Kamala Harris is a \n", - "ftklobuchar1 Amy Klobuchar is a \n", - "ftpence1 Mike Pence is a \n", - "ftyang1 Andrew Yang is a \n", - "ftpelosi1 Nancy Pelosi is a \n", - "ftrubio1 Marco Rubio is a \n", - "ftocasioc1 Alexandria Ocasio-Cortez is a \n", - "fthaley1 Nikki Haley is a \n", - "ftthomas1 Clarence Thomas is a \n", - "ftfauci1 Dr. Anthony Fauci is a \n", - "ftblack Black people are a \n", - "ftwhite White people are a \n", - "fthisp Hispanic people are a \n", - "ftasian Asian people are a \n", - "ftillegal illegal immigrants are a \n", - "ftfeminists feminists are a \n", - "ftmetoo the #MeToo movement is a \n", - "fttransppl transgender people are a \n", - "ftsocialists socialists are a \n", - "ftcapitalists capitalists are a \n", - "ftbigbusiness big business is a \n", - "ftlaborunions labor unions is a \n", - "ftrepublicanparty the Republican Party is a \n", - 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"fthaley1 17.209183 \n", - "ftthomas1 18.681868 \n", - "ftfauci1 8.393979 \n", - "ftblack 9.709329 \n", - "ftwhite 5.906540 \n", - "fthisp 7.673487 \n", - "ftasian 5.501792 \n", - "ftillegal 29.923182 \n", - "ftfeminists 28.047565 \n", - "ftmetoo 31.008923 \n", - "fttransppl 28.155633 \n", - "ftsocialists 29.894840 \n", - "ftcapitalists 13.857401 \n", - "ftbigbusiness 14.705712 \n", - "ftlaborunions 15.802008 \n", - "ftrepublicanparty 49.449205 \n", - "ftdemocraticparty 46.893421 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_politician_results['diff'] = (df_politician_results.Democrat-df_politician_results.Republican).apply(abs)\n", "df_politician_results.sort_values(by=['diff'])\n", @@ -3738,176 +289,7 @@ "execution_count": 13, "id": "0d429b20", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0model_namerunprompt_formatquestiongroup_sentiment
00RAG_republican-twitter-gpt2run_1Prompt1fttrump146.0
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22RAG_republican-twitter-gpt2run_1Prompt1ftbiden150.0
33RAG_republican-twitter-gpt2run_1Prompt1ftwarren156.0
44RAG_republican-twitter-gpt2run_1Prompt1ftsanders146.5
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595595RAG_republican-twitter-gpt2run_5Prompt4ftcapitalists10.0
596596RAG_republican-twitter-gpt2run_5Prompt4ftbigbusiness62.5
597597RAG_republican-twitter-gpt2run_5Prompt4ftlaborunions54.0
598598RAG_republican-twitter-gpt2run_5Prompt4ftrepublicanparty44.0
599599RAG_republican-twitter-gpt2run_5Prompt4ftdemocraticparty54.0
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600 rows × 6 columns

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" - ], - "text/plain": [ - " Unnamed: 0 model_name run prompt_format \\\n", - "0 0 RAG_republican-twitter-gpt2 run_1 Prompt1 \n", - "1 1 RAG_republican-twitter-gpt2 run_1 Prompt1 \n", - "2 2 RAG_republican-twitter-gpt2 run_1 Prompt1 \n", - "3 3 RAG_republican-twitter-gpt2 run_1 Prompt1 \n", - "4 4 RAG_republican-twitter-gpt2 run_1 Prompt1 \n", - ".. ... ... ... ... \n", - "595 595 RAG_republican-twitter-gpt2 run_5 Prompt4 \n", - "596 596 RAG_republican-twitter-gpt2 run_5 Prompt4 \n", - "597 597 RAG_republican-twitter-gpt2 run_5 Prompt4 \n", - "598 598 RAG_republican-twitter-gpt2 run_5 Prompt4 \n", - "599 599 RAG_republican-twitter-gpt2 run_5 Prompt4 \n", - "\n", - " question group_sentiment \n", - "0 fttrump1 46.0 \n", - "1 ftobama1 53.5 \n", - "2 ftbiden1 50.0 \n", - "3 ftwarren1 56.0 \n", - "4 ftsanders1 46.5 \n", - ".. ... ... \n", - "595 ftcapitalists 10.0 \n", - "596 ftbigbusiness 62.5 \n", - "597 ftlaborunions 54.0 \n", - "598 ftrepublicanparty 44.0 \n", - "599 ftdemocraticparty 54.0 \n", - "\n", - "[600 rows x 6 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_repub" ] @@ -3917,250 +299,7 @@ "execution_count": 14, "id": "d0a6ef2b-ff35-49dc-92a7-0fb984fed6ea", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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runprompt_formataccuracyprecisionrecallf1
0run_1Prompt10.5333330.6443440.5333330.550226
1run_1Prompt20.6000000.6800000.6000000.616667
2run_1Prompt30.5000000.6250000.5000000.515152
3run_1Prompt40.6666670.7149320.6666670.679426
4run_2Prompt10.6333330.7312500.6333330.648000
5run_2Prompt20.4666670.5466670.4666670.488889
6run_2Prompt30.6000000.6800000.6000000.616667
7run_2Prompt40.7000000.7333330.7000000.709890
8run_3Prompt10.4666670.5728510.4666670.485973
9run_3Prompt20.4666670.5466670.4666670.488889
10run_3Prompt30.6000000.7158370.6000000.614480
11run_3Prompt40.6666670.7149320.6666670.679426
12run_4Prompt10.4000000.5244020.4000000.413393
13run_4Prompt20.5666670.6321430.5666670.584489
14run_4Prompt30.5333330.6443440.5333330.550226
15run_4Prompt40.5333330.5864250.5333330.551196
16run_5Prompt10.5000000.6250000.5000000.515152
17run_5Prompt20.4333330.5250000.4333330.456000
18run_5Prompt30.5000000.5937500.5000000.520000
19run_5Prompt40.5333330.6133330.5333330.552778
\n", - "
" - ], - "text/plain": [ - " run prompt_format accuracy precision recall f1\n", - "0 run_1 Prompt1 0.533333 0.644344 0.533333 0.550226\n", - "1 run_1 Prompt2 0.600000 0.680000 0.600000 0.616667\n", - "2 run_1 Prompt3 0.500000 0.625000 0.500000 0.515152\n", - "3 run_1 Prompt4 0.666667 0.714932 0.666667 0.679426\n", - "4 run_2 Prompt1 0.633333 0.731250 0.633333 0.648000\n", - "5 run_2 Prompt2 0.466667 0.546667 0.466667 0.488889\n", - "6 run_2 Prompt3 0.600000 0.680000 0.600000 0.616667\n", - "7 run_2 Prompt4 0.700000 0.733333 0.700000 0.709890\n", - "8 run_3 Prompt1 0.466667 0.572851 0.466667 0.485973\n", - "9 run_3 Prompt2 0.466667 0.546667 0.466667 0.488889\n", - "10 run_3 Prompt3 0.600000 0.715837 0.600000 0.614480\n", - "11 run_3 Prompt4 0.666667 0.714932 0.666667 0.679426\n", - "12 run_4 Prompt1 0.400000 0.524402 0.400000 0.413393\n", - "13 run_4 Prompt2 0.566667 0.632143 0.566667 0.584489\n", - "14 run_4 Prompt3 0.533333 0.644344 0.533333 0.550226\n", - "15 run_4 Prompt4 0.533333 0.586425 0.533333 0.551196\n", - "16 run_5 Prompt1 0.500000 0.625000 0.500000 0.515152\n", - "17 run_5 Prompt2 0.433333 0.525000 0.433333 0.456000\n", - "18 run_5 Prompt3 0.500000 0.593750 0.500000 0.520000\n", - "19 run_5 Prompt4 0.533333 0.613333 0.533333 0.552778" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv(\"output/anes2020_pilot_prompt_probing.csv\")\n", "df_scores = compute_scores(df, df_dem, df_repub)\n", @@ -4172,20 +311,7 @@ "execution_count": 15, "id": "200fb627-57f4-4d73-a375-bb87e95923c2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([25., 23., 7., 29., 13., 8., 26., 24., 19., 4., 12., 28., 15.,\n", - " 10., 17., 21., 30., 11., 18., 2., 3., 6., 22., 14., 5., 20.,\n", - " 1., 16., 27., 9.])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract gold ranks\n", "df_politician_results = df_politician_results.sort_values(by=[\"pid\"])\n", @@ -4209,334 +335,7 @@ "execution_count": 16, "id": "c69f6bc3-b8d2-44ad-9aab-222653191c69", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0model_namerunprompt_formatquestiongroup_sentimentDemocratRepublicanis_repub_leadingPrompt1Prompt2Prompt3Prompt4piddiffshort_namegroup_avg_sentiment
092RAG_democrat-twitter-gpt2run_1Prompt4ftbiden148.066.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188Biden51.0
1212RAG_democrat-twitter-gpt2run_2Prompt4ftbiden153.566.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188Biden51.0
2332RAG_democrat-twitter-gpt2run_3Prompt4ftbiden153.566.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188Biden51.0
3452RAG_democrat-twitter-gpt2run_4Prompt4ftbiden149.566.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188Biden51.0
4572RAG_democrat-twitter-gpt2run_5Prompt4ftbiden150.566.49742324.401235FalseJoe BidenJoe Biden isJoe Biden is aJoe Biden is theftbiden142.096188Biden51.0
......................................................
7599RAG_democrat-twitter-gpt2run_1Prompt4ftyang174.049.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956Yang76.6
76219RAG_democrat-twitter-gpt2run_2Prompt4ftyang175.549.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956Yang76.6
77339RAG_democrat-twitter-gpt2run_3Prompt4ftyang177.049.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956Yang76.6
78459RAG_democrat-twitter-gpt2run_4Prompt4ftyang175.549.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956Yang76.6
79579RAG_democrat-twitter-gpt2run_5Prompt4ftyang181.049.27913329.191176FalseAndrew YangAndrew Yang isAndrew Yang is aAndrew Yang is theftyang120.087956Yang76.6
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80 rows × 17 columns

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" - ], - "text/plain": [ - " Unnamed: 0 model_name run prompt_format question \\\n", - "0 92 RAG_democrat-twitter-gpt2 run_1 Prompt4 ftbiden1 \n", - "1 212 RAG_democrat-twitter-gpt2 run_2 Prompt4 ftbiden1 \n", - "2 332 RAG_democrat-twitter-gpt2 run_3 Prompt4 ftbiden1 \n", - "3 452 RAG_democrat-twitter-gpt2 run_4 Prompt4 ftbiden1 \n", - "4 572 RAG_democrat-twitter-gpt2 run_5 Prompt4 ftbiden1 \n", - ".. ... ... ... ... ... \n", - "75 99 RAG_democrat-twitter-gpt2 run_1 Prompt4 ftyang1 \n", - "76 219 RAG_democrat-twitter-gpt2 run_2 Prompt4 ftyang1 \n", - "77 339 RAG_democrat-twitter-gpt2 run_3 Prompt4 ftyang1 \n", - "78 459 RAG_democrat-twitter-gpt2 run_4 Prompt4 ftyang1 \n", - "79 579 RAG_democrat-twitter-gpt2 run_5 Prompt4 ftyang1 \n", - "\n", - " group_sentiment Democrat Republican is_repub_leading Prompt1 \\\n", - "0 48.0 66.497423 24.401235 False Joe Biden \n", - "1 53.5 66.497423 24.401235 False Joe Biden \n", - "2 53.5 66.497423 24.401235 False Joe Biden \n", - "3 49.5 66.497423 24.401235 False Joe Biden \n", - "4 50.5 66.497423 24.401235 False Joe Biden \n", - ".. ... ... ... ... ... \n", - "75 74.0 49.279133 29.191176 False Andrew Yang \n", - "76 75.5 49.279133 29.191176 False Andrew Yang \n", - "77 77.0 49.279133 29.191176 False Andrew Yang \n", - "78 75.5 49.279133 29.191176 False Andrew Yang \n", - "79 81.0 49.279133 29.191176 False Andrew Yang \n", - "\n", - " Prompt2 Prompt3 Prompt4 pid diff \\\n", - "0 Joe Biden is Joe Biden is a Joe Biden is the ftbiden1 42.096188 \n", - "1 Joe Biden is Joe Biden is a Joe Biden is the ftbiden1 42.096188 \n", - "2 Joe Biden is Joe Biden is a Joe Biden is the ftbiden1 42.096188 \n", - "3 Joe Biden is Joe Biden is a Joe Biden is the ftbiden1 42.096188 \n", - "4 Joe Biden is Joe Biden is a Joe Biden is the ftbiden1 42.096188 \n", - ".. ... ... ... ... ... \n", - "75 Andrew Yang is Andrew Yang is a Andrew Yang is the ftyang1 20.087956 \n", - "76 Andrew Yang is Andrew Yang is a Andrew Yang is the ftyang1 20.087956 \n", - "77 Andrew Yang is Andrew Yang is a Andrew Yang is the ftyang1 20.087956 \n", - "78 Andrew Yang is Andrew Yang is a Andrew Yang is the ftyang1 20.087956 \n", - "79 Andrew Yang is Andrew Yang is a Andrew Yang is the ftyang1 20.087956 \n", - "\n", - " short_name group_avg_sentiment \n", - "0 Biden 51.0 \n", - "1 Biden 51.0 \n", - "2 Biden 51.0 \n", - "3 Biden 51.0 \n", - "4 Biden 51.0 \n", - ".. ... ... \n", - "75 Yang 76.6 \n", - "76 Yang 76.6 \n", - "77 Yang 76.6 \n", - "78 Yang 76.6 \n", - "79 Yang 76.6 \n", - "\n", - "[80 rows x 17 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "## plot the rankings\n", "\n", @@ -4572,36 +371,7 @@ "execution_count": 23, "id": "015bb056-a742-49a1-97a7-dda18d203ac8", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1776716/304075064.py:12: FutureWarning: \n", - "\n", - "The `ci` parameter is deprecated. Use `errorbar=('ci', 90)` for the same effect.\n", - "\n", - " ax = sns.barplot(data=dem_politician_rank.sort_values(by=\"group_avg_sentiment\", ascending=False), x=\"group_sentiment\", y=\"short_name\", palette=palette, estimator=np.mean, ci=90)\n", - "/tmp/ipykernel_1776716/304075064.py:12: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " ax = sns.barplot(data=dem_politician_rank.sort_values(by=\"group_avg_sentiment\", ascending=False), x=\"group_sentiment\", y=\"short_name\", palette=palette, estimator=np.mean, ci=90)\n", - "/tmp/ipykernel_1776716/304075064.py:12: UserWarning: The palette list has more values (20) than needed (16), which may not be intended.\n", - " ax = sns.barplot(data=dem_politician_rank.sort_values(by=\"group_avg_sentiment\", ascending=False), x=\"group_sentiment\", y=\"short_name\", palette=palette, estimator=np.mean, ci=90)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# df_politician_results.to_csv(\"rank_plots.csv\")\n", "import pandas as pd\n", @@ -4628,31 +398,7 @@ "execution_count": 24, "id": "ea3b9c6c-a2e1-43b4-8463-caaff9653fa9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1776716/4128545637.py:6: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " ax = sns.barplot(data=dem_politician_rank.sort_values(by=\"Democrat\", ascending=False), x=\"Democrat\", y=\"short_name\", palette=palette)\n", - "/tmp/ipykernel_1776716/4128545637.py:6: UserWarning: The palette list has more values (20) than needed (16), which may not be intended.\n", - " ax = sns.barplot(data=dem_politician_rank.sort_values(by=\"Democrat\", ascending=False), x=\"Democrat\", y=\"short_name\", palette=palette)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sns.set(rc={'figure.figsize':(5,5)})\n", "\n", @@ -4673,31 +419,7 @@ "execution_count": 25, "id": "2bc980b7-aa69-4cbe-8778-f57b463fc909", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1776716/1174940903.py:4: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " ax = sns.barplot(data=repub_politician_rank.sort_values(by=\"group_avg_sentiment\", ascending=False), x=\"group_sentiment\", y=\"short_name\", palette=palette)\n", - "/tmp/ipykernel_1776716/1174940903.py:4: UserWarning: The palette list has more values (20) than needed (16), which may not be intended.\n", - " ax = sns.barplot(data=repub_politician_rank.sort_values(by=\"group_avg_sentiment\", ascending=False), x=\"group_sentiment\", y=\"short_name\", palette=palette)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "palette = sns.color_palette(\"Reds\", n_colors=20)\n", "palette.reverse()\n", @@ -4716,31 +438,7 @@ "execution_count": 26, "id": "6c1dd3c2-dda4-482e-99b6-6ef4316155ea", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1776716/3907180483.py:4: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " ax = sns.barplot(data=repub_politician_rank.sort_values(by=\"Republican\", ascending=False), x=\"Republican\", y=\"short_name\", palette=palette)\n", - "/tmp/ipykernel_1776716/3907180483.py:4: UserWarning: The palette list has more values (20) than needed (16), which may not be intended.\n", - " ax = sns.barplot(data=repub_politician_rank.sort_values(by=\"Republican\", ascending=False), x=\"Republican\", y=\"short_name\", palette=palette)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "palette = sns.color_palette(\"Reds\", n_colors=20)\n", "palette.reverse()\n",