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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# QASPER evaluation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ag2435/anaconda3/envs/arxiv-agent/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import json\n",
"import os\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"import dataset_reader\n",
"from transformers.tokenization_utils_base import BatchEncoding\n",
"from datetime import date\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"input_paths = {\n",
" 'train': 'original_data/qasper-train-dev-v0.3/qasper-train-v0.3.json',\n",
" 'dev': 'original_data/qasper-train-dev-v0.3/qasper-dev-v0.3.json',\n",
" 'test': 'original_data/qasper-test-and-evaluator-v0.3/qasper-test-v0.3.json',\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"output_path = 'data'\n",
"split = 'dev'\n",
"input_path = input_paths[split]\n",
"assert split in input_path"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"reader = dataset_reader.QasperReader(include_global_attention_mask=False)\n",
"instances = list(reader._read(input_path))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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"data": {
"text/plain": [
"defaultdict(int,\n",
" {'number of documents': 281,\n",
" 'number of questions': 1005,\n",
" 'number of answers': 1764,\n",
" 'questions with multiple answers': 744,\n",
" 'extractive questions': 962,\n",
" 'extractive questions with multiple spans': 406,\n",
" 'multiple_evidence_spans_count': 536,\n",
" 'answers with table or figure as evidence': 212,\n",
" 'freeform answers': 431,\n",
" 'yes/no questions': 208,\n",
" 'answers with no evidence': 212,\n",
" 'unanswerable questions': 163,\n",
" 'number of truncated contexts': 15})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reader._stats"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# filter instances that have valid html\n",
"from arxiv_agent.tools.paper.utils import get_soup\n",
"from functools import cache\n",
"from time import sleep\n",
"\n",
"@cache\n",
"def has_html(arxiv_id):\n",
" sleep(0.1)\n",
" try:\n",
" ERROR_MSG = \"Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged.\"\n",
" soup = get_soup(arxiv_id)\n",
" return ERROR_MSG not in soup.text\n",
" except Exception as e:\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
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},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
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"source": [
"has_html(instances[0]['metadata']['article_id'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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"source": [
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]
},
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"cell_type": "code",
"execution_count": 10,
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"question_token_lengths = []\n",
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" question_token_lengths.append(len(inst['question_with_context']))\n",
" \n",
"px.histogram(question_token_lengths, \n",
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"source": [
"# arxiv id to year\n",
"def article_id_to_date(article_id):\n",
" year = article_id[:2]\n",
" month = article_id[2:4]\n",
" dt = date(int('20' + year), int(month), 1)\n",
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"cell_type": "code",
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"# get year distribution\n",
"years = []\n",
"for inst in instances:\n",
" dt = article_id_to_date(inst['metadata']['article_id'])\n",
" # print(dt)\n",
" years.append(dt)"
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"ticks": ""
}
},
"type": "scatterpolar"
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"ticks": ""
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},
"type": "scatterpolargl"
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"type": "scatterternary"
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[
1,
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"type": "surface"
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{
"cells": {
"fill": {
"color": "#EBF0F8"
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"line": {
"color": "white"
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},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
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"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
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"autotypenumbers": "strict",
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0.1,
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0.3,
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0.4,
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0.5,
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"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
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},
"bgcolor": "#E5ECF6",
"radialaxis": {
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"linecolor": "white",
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}
},
"scene": {
"xaxis": {
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"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
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"yaxis": {
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"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
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"gridwidth": 2,
"linecolor": "white",
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"line": {
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"ternary": {
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"linecolor": "white",
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},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
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},
"zerolinecolor": "white",
"zerolinewidth": 2
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},
"xaxis": {
"anchor": "y",
"domain": [
0,
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],
"title": {
"text": "value"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "count"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"px.histogram(years,)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save instances to JSON"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# recursively check the type of each field in the dataset\n",
"def check_type(data, level=0):\n",
" tabs = '\\t' * level\n",
" if isinstance(data, dict):\n",
" for key, value in data.items():\n",
" print(f'{tabs}{key}: {type(value)}')\n",
" check_type(value, level + 1)\n",
" elif isinstance(data, list):\n",
" for i in data:\n",
" check_type(i, level + 1)\n",
" else:\n",
" pass\n",
" # print(f'{tabs}{type(data)}')\n",
" # if isinstance(data, BatchEncoding):\n",
" # print(f'{tabs}{data[:100]}')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"question_with_context: \n",
"paragraph_indices: \n",
"evidence: \n",
"answer: \n",
"metadata: \n",
"\tquestion: \n",
"\tquestion_tokens: \n",
"\tparagraphs: \n",
"\tcontext_tokens: \n",
"\tquestion_id: \n",
"\tarticle_id: \n",
"\tall_answers: \n",
"\t\t\ttext: \n",
"\t\t\ttype: \n",
"\t\t\ttext: \n",
"\t\t\ttype: \n",
"\tall_evidence: \n",
"\tall_evidence_masks: \n"
]
}
],
"source": [
"check_type(instances[0])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/1005 [00:00