Hugging Face's logo

Datasets:
reglab
/
ui_survey

Modalities:
Tabular
Text
Formats:
parquet
Size:
1M - 10M
Libraries:
Datasets
Dask
Dataset card Data Studio Files Community
1
ui_survey / README.md
ehariri's picture
ehariri
Upload dataset
446768e verified
preview code
|
raw
history blame contribute delete
19.3 kB
---
dataset_info:
- config_name: all_codes
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 4098779991
num_examples: 1844116
download_size: 1401775509
dataset_size: 4098779991
- config_name: all_regs
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 3199856796
num_examples: 2208863
download_size: 718784236
dataset_size: 3199856796
- config_name: codes
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: AL
num_bytes: 43731588
num_examples: 22200
- name: AK
num_bytes: 32184571
num_examples: 17779
- name: AZ
num_bytes: 56665824
num_examples: 24623
- name: AR
num_bytes: 88392840
num_examples: 38121
- name: CA
num_bytes: 290455932
num_examples: 158333
- name: CO
num_bytes: 124828839
num_examples: 36191
- name: CT
num_bytes: 90023924
num_examples: 27290
- name: DE
num_bytes: 48197245
num_examples: 20855
- name: FL
num_bytes: 85789801
num_examples: 24560
- name: GA
num_bytes: 74831320
num_examples: 29282
- name: HI
num_bytes: 46836244
num_examples: 22022
- name: ID
num_bytes: 47178706
num_examples: 22712
- name: IL
num_bytes: 118398324
num_examples: 4745
- name: IN
num_bytes: 110050649
num_examples: 80902
- name: IA
num_bytes: 61048195
num_examples: 40965
- name: KS
num_bytes: 65423047
num_examples: 28869
- name: LA
num_bytes: 102840709
num_examples: 51205
- name: ME
num_bytes: 87323065
num_examples: 45542
- name: MD
num_bytes: 76998733
num_examples: 38070
- name: MA
num_bytes: 60263360
num_examples: 24385
- name: MI
num_bytes: 115910375
num_examples: 45274
- name: MN
num_bytes: 85378645
num_examples: 27391
- name: MS
num_bytes: 72048841
num_examples: 30070
- name: MO
num_bytes: 75588171
num_examples: 29948
- name: MT
num_bytes: 69870385
num_examples: 44042
- name: NE
num_bytes: 70590689
num_examples: 39587
- name: NV
num_bytes: 79466582
num_examples: 48720
- name: NH
num_bytes: 51559565
num_examples: 29035
- name: NJ
num_bytes: 103603084
num_examples: 55270
- name: NM
num_bytes: 86303928
num_examples: 31146
- name: NY
num_bytes: 144693313
num_examples: 38636
- name: NC
num_bytes: 70874230
num_examples: 29172
- name: ND
num_bytes: 977500
num_examples: 2499
- name: OH
num_bytes: 93420849
num_examples: 33306
- name: OK
num_bytes: 72415016
num_examples: 32299
- name: OR
num_bytes: 87859395
num_examples: 47446
- name: PA
num_bytes: 36036513
num_examples: 14464
- name: RI
num_bytes: 65282284
num_examples: 34035
- name: SC
num_bytes: 62871267
num_examples: 30972
- name: SD
num_bytes: 15545185
num_examples: 11406
- name: TN
num_bytes: 24195712
num_examples: 11140
- name: TX
num_bytes: 212279038
num_examples: 122031
- name: UT
num_bytes: 26657635
num_examples: 10969
- name: VT
num_bytes: 23384551
num_examples: 11075
- name: VA
num_bytes: 21372134
num_examples: 10698
- name: WA
num_bytes: 23574520
num_examples: 11182
- name: WV
num_bytes: 27748050
num_examples: 10928
- name: WI
num_bytes: 46634714
num_examples: 11288
- name: WY
num_bytes: 21276332
num_examples: 10658
- name: DC
num_bytes: 69524738
num_examples: 23687
- name: PR
num_bytes: 61028048
num_examples: 28118
download_size: 1270324283
dataset_size: 3729434205
- config_name: default
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: codes
num_bytes: 3729434205
num_examples: 1675143
download_size: 1269141079
dataset_size: 3729434205
- config_name: ui_codes
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 24646719
num_examples: 8261
download_size: 8814028
dataset_size: 24646719
- config_name: ui_qa
features:
- name: idx
dtype: int64
- name: table_id
dtype: string
- name: column_num
dtype: int64
- name: jurisdiction
dtype: string
- name: jur_abb
dtype: string
- name: question_context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 6987039
num_examples: 3400
download_size: 214970
dataset_size: 6987039
- config_name: ui_qas
features:
- name: idx
dtype: int64
- name: table_id
dtype: string
- name: column_num
dtype: int64
- name: column_name
dtype: string
- name: column_dtype
dtype: string
- name: jurisdiction
dtype: string
- name: jur_abb
dtype: string
- name: question_context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 7451560
num_examples: 3700
download_size: 218717
dataset_size: 7451560
- config_name: ui_qas_bool_supp
features:
- name: index
dtype: int64
- name: idx
dtype: int64
- name: table_id
dtype: string
- name: column_num
dtype: int64
- name: column_name
dtype: string
- name: column_dtype
dtype: string
- name: jurisdiction
dtype: string
- name: jur_abb
dtype: string
- name: question_context
dtype: string
- name: question
dtype: string
- name: answer
dtype: bool
splits:
- name: train
num_bytes: 6442222
num_examples: 3015
download_size: 178156
dataset_size: 6442222
- config_name: ui_regs
features:
- name: url
dtype: string
- name: state
dtype: string
- name: path
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 8893603
num_examples: 5639
download_size: 2687386
dataset_size: 8893603
- config_name: ui_tables
features:
- name: table_id
dtype: string
- name: table_name
dtype: string
- name: table_description
dtype: string
- name: notes
dtype: string
- name: table_data
dtype: string
- name: chapter
dtype: int64
- name: headers
dtype: string
- name: prompt_context
dtype: string
- name: prompts
sequence: string
- name: abbs
dtype: string
splits:
- name: train
num_bytes: 853904
num_examples: 83
download_size: 218612
dataset_size: 853904
configs:
- config_name: all_codes
data_files:
- split: train
path: all_codes/train-*
- config_name: all_regs
data_files:
- split: train
path: all_regs/train-*
- config_name: codes
data_files:
- split: AL
path: codes/AL-*
- split: AK
path: codes/AK-*
- split: AZ
path: codes/AZ-*
- split: AR
path: codes/AR-*
- split: CA
path: codes/CA-*
- split: CO
path: codes/CO-*
- split: CT
path: codes/CT-*
- split: DE
path: codes/DE-*
- split: FL
path: codes/FL-*
- split: GA
path: codes/GA-*
- split: HI
path: codes/HI-*
- split: ID
path: codes/ID-*
- split: IL
path: codes/IL-*
- split: IN
path: codes/IN-*
- split: IA
path: codes/IA-*
- split: KS
path: codes/KS-*
- split: LA
path: codes/LA-*
- split: ME
path: codes/ME-*
- split: MD
path: codes/MD-*
- split: MA
path: codes/MA-*
- split: MI
path: codes/MI-*
- split: MN
path: codes/MN-*
- split: MS
path: codes/MS-*
- split: MO
path: codes/MO-*
- split: MT
path: codes/MT-*
- split: NE
path: codes/NE-*
- split: NV
path: codes/NV-*
- split: NH
path: codes/NH-*
- split: NJ
path: codes/NJ-*
- split: NM
path: codes/NM-*
- split: NY
path: codes/NY-*
- split: NC
path: codes/NC-*
- split: ND
path: codes/ND-*
- split: OH
path: codes/OH-*
- split: OK
path: codes/OK-*
- split: OR
path: codes/OR-*
- split: PA
path: codes/PA-*
- split: RI
path: codes/RI-*
- split: SC
path: codes/SC-*
- split: SD
path: codes/SD-*
- split: TN
path: codes/TN-*
- split: TX
path: codes/TX-*
- split: UT
path: codes/UT-*
- split: VT
path: codes/VT-*
- split: VA
path: codes/VA-*
- split: WA
path: codes/WA-*
- split: WV
path: codes/WV-*
- split: WI
path: codes/WI-*
- split: WY
path: codes/WY-*
- split: DC
path: codes/DC-*
- split: PR
path: codes/PR-*
- config_name: default
data_files:
- split: codes
path: data/codes-*
- config_name: ui_codes
data_files:
- split: train
path: ui_codes/train-*
- config_name: ui_qa
data_files:
- split: train
path: ui_qa/train-*
- config_name: ui_qas
data_files:
- split: train
path: ui_qas/train-*
- config_name: ui_qas_bool_supp
data_files:
- split: train
path: ui_qas_bool_supp/train-*
- config_name: ui_regs
data_files:
- split: train
path: ui_regs/train-*
- config_name: ui_tables
data_files:
- split: train
path: ui_tables/train-*
---
# UI_Survey
The `ui_survey` dataset is a collection of datasets derived from unemployment insurance state survey. Specifically, it is derived from the Department of Labor's (DoL) [Comparison of State Unemployment Insurance Laws](https://oui.doleta.gov/unemploy/pdf/uilawcompar/2023/complete.pdf) which they publish annually. (This dataset is based off of the 2023 survey.) The dataset was designed for the benchmarking of both LLMs' retrieval and statutory reasoning ability.
The first dataset is `ui_tables` which aims to store the information from the DoL's survey. `ui_tables` contains _82 rows_ corresponding roughly to the .
## Dataset Structure
```python
>>> load_dataset('reglab/ui_survey', 'ui_tables')
Dataset({
features: ['table_id', 'table_name', 'table_description', 'notes', 'table_data', 'chapter', 'headers', 'prompt_context', 'prompts'],
num_rows: 82
})
>>> load_dataset('reglab/ui_survey', 'ui_qas')
Dataset({
features: ['idx', 'table_id', 'column_num', 'jurisdiction', 'question_context', 'question', 'answer'],
num_rows: 3400
})
```
### Data Instance
An instance of `ui_survey` (here, index 13) looks like
```json
{
"table_id": "2-5",
"table_name": "BENEFIT-WAGE-RATIO FORMULA STATES",
"table_description": "Details of years used for benefits and payrolls in benefit-wage-ratio formula states",
"notes": "",
"table_data": "[{'state': 'DE', 'years_of_benefits_used': 'Last 3 years', 'years_of_payrolls_used': 'Last 3 years'}, {'state': 'OK', 'years_of_benefits_used': 'Last 3 years', 'years_of_payrolls_used': 'Last 3 years'}]", 'chapter': '2', 'headers': "{'state': string[python], 'years_of_benefits_used': string[python], 'years_of_payrolls_used': string[python]}", 'prompt_context': 'BENEFIT-WAGE-RATIO FORMULA—The benefit-wage-ratio formula is significantly different from the other formulas. It makes no attempt to measure all benefits paid to the workers of individual employers. The relative experience of employers is measured by the separations of workers which result in benefit payments, but the duration of their benefits is not a factor. The separations, weighted with the wages earned by the workers with each base period employer, are recorded on each employer’s experience rating record as “benefit wages.” Only one separation per beneficiary per benefit year is recorded for any one employer. The index which is used to establish the relative experience of employers is the proportion of each employer’s payroll which is paid to those workers who become unemployed and receive benefits (i.e., the ratio of an employer’s benefit wages to total taxable wages). The ratio of total benefit payments and total benefit wages, known as the state experience factor, means that, on average, the workers who drew benefits received a certain amount of benefits for each dollar of benefit wages paid and the same amount of taxes per dollar of benefit wages is needed to replenish the fund. The total amount to be raised is distributed among employers in accordance with their benefit-wage-ratios; the higher the ratio, the higher the rate. \nIndividual employer rates are determined by multiplying the employer’s experience factor by the state experience factor. The multiplication is facilitated by a table, which assigns rates that are the same as, or slightly more than, the product of the employer’s benefit-wage-ratio and the state factor.', 'prompts': "['Given the description above, what are the years of benefits used to calculate the benefit-wage ratio in {jurisdiction}?', 'Given the description above, what are the years of payrolls used to calculate the benefit-wage ratio in {jurisdiction}?']"
"chapter": "2",
"headers": "{'state': string[python], 'years_of_benefits_used': string[python], 'years_of_payrolls_used': string[python]}",
"prompt_context": "BENEFIT-WAGE-RATIO FORMULA—The benefit-wage-ratio formula is significantly different from the other formulas. It makes no attempt to measure all benefits paid to the workers of individual employers. The relative experience of employers is measured by the separations of workers which result in benefit payments, but the duration of their benefits is not a factor. The separations, weighted with the wages earned by the workers with each base period employer, are recorded on each employer’s experience rating record as “benefit wages.” Only one separation per beneficiary per benefit year is recorded for any one employer. The index which is used to establish the relative experience of employers is the proportion of each employer’s payroll which is paid to those workers who become unemployed and receive benefits (i.e., the ratio of an employer’s benefit wages to total taxable wages). The ratio of total benefit payments and total benefit wages, known as the state experience factor, means that, on average, the workers who drew benefits received a certain amount of benefits for each dollar of benefit wages paid and the same amount of taxes per dollar of benefit wages is needed to replenish the fund. The total amount to be raised is distributed among employers in accordance with their benefit-wage-ratios; the higher the ratio, the higher the rate. \nIndividual employer rates are determined by multiplying the employer’s experience factor by the state experience factor. The multiplication is facilitated by a table, which assigns rates that are the same as, or slightly more than, the product of the employer’s benefit-wage-ratio and the state factor.",
"prompts": "['Given the description above, what are the years of benefits used to calculate the benefit-wage ratio in {jurisdiction}?', 'Given the description above, what are the years of payrolls used to calculate the benefit-wage ratio in {jurisdiction}?']"
}
```
where in this instance
```python
>>> pd.DataFrame.from_records(table_data[13])
state years_of_benefits_used years_of_payrolls_used
0 DE Last 3 years Last 3 years
1 OK Last 3 years Last 3 years
```
An instance of `ui_qas` looks like
```json
{
"idx": 0,
"table_id": "1-1",
"column_num": 0,
"jurisdiction": "Alaska",
"question_context": "EMPLOYERS As mentioned above, one of the basic factors in determining coverage is whether services are performed for employers. The coverage provisions of most state laws use the terms “employing unit” and “employer” to make the distinctions needed to address this issue. “Employing unit” is the more generic term, it applies to any one of several specified types of legal entities that has one or more individuals performing service for it within a state. An “employer” is an employing unit that meets the specific requirements of UI law. Accordingly, services provided for an “employer” are covered, and, as a result, an employer is subject to UI tax liability and its workers accrue rights to receive UI benefits. \nFor federal UI purposes, whether an employing unit is an employer depends on the number of days or weeks a worker is employed or the amount of the employing unit’s quarterly or yearly payroll. Except for agricultural labor and domestic service, FUTA applies to employing units who paid wages of $1,500 or more during any calendar quarter in the current or immediately preceding calendar year, or to employing units with one or more workers on at least one day in each of 20 different weeks during the current or immediately preceding calendar year. About half of the states use this federal definition. \nThe following table provides information on which employing units are considered employers in each state that uses a definition other than the one in FUTA.",
"question": "Given the description above, what is either the minimum period of time or the payroll required for an employing unit to be considered an employer in Alaska?",
"answer": "Any time"
}
```
### Data Fields
#### `ui_tables`
* `table_id`: str, ID of table written as "[chapter no.]-[table no. within chapter]" (derived from DoL report)
* `table_name`: str, title of the table as noted on the DoL report
* `table_description`: str, description of the table
* `note`: str, any footnotes and other key information stored for this table
* `table_data`: str, the data from this table as derived from the DoL report
* `eval(...)` should produce a list of dictionaries corressponding to the table
* `chapter`: str, chapter the table appears in (TODO: replace with descriptor)
* `header`: str, string representation of headers with their respective datatypes
* `eval(...)` should produce a dictionary
* `prompt_context`: str, context for the table and prompts associated with the table (intended to precede the prompts in queries)
* `prompts`: str, prompts to be used
* It should be that `len(prompts) == len(headers)`
* These prompts are used to derive the questions in `ui_tables`
#### `ui_qas`
* `idx`: int, index of question-answer pair
* `table_id`: str, ID of table the question corresponds to, written as "[chapter no.]-[table no. within chapter]" (derived from DoL report)
* `column_num`: str, the column number that the (relative to headers)
* `jurisdiction`: jurisidction (state or territory)
* `question_context`: str, context for the table in question (intended to precede the question in queries)
* `question`: str, question whose response is intended to fill in the entry corressponding to `table_id` and `column_num` for `jurisidiction`
* `answer`: str, answer to the question (from DoL report)