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import streamlit as st
from .streamlit_utils import make_text_input
from .streamlit_utils import (
make_multiselect,
make_selectbox,
make_text_area,
make_text_input,
make_radio,
)
N_FIELDS_ORIGINAL = 4
N_FIELDS_LANGUAGE = 12
N_FIELDS_ANNOTATIONS = 10
N_FIELDS_CONSENT = 0
N_FIELDS_PII = 0
N_FIELDS_MAINTENANCE = 0
N_FIELDS_GEM = 0
N_FIELDS = (
N_FIELDS_ORIGINAL
+ N_FIELDS_LANGUAGE
+ N_FIELDS_ANNOTATIONS
+ N_FIELDS_CONSENT
+ N_FIELDS_PII
+ N_FIELDS_MAINTENANCE
+ N_FIELDS_GEM
)
"""
What was the selection criteria? [Describe the process for selecting instances to include in the dataset, including any tools used.]
"""
def curation_page():
st.session_state.card_dict["curation"] = st.session_state.card_dict.get(
"curation", {}
)
with st.expander("Original Curation", expanded=False):
key_pref = ["curation", "original"]
st.session_state.card_dict["curation"]["original"] = st.session_state.card_dict[
"curation"
].get("original", {})
make_text_area(
label="Original curation rationale",
key_list=key_pref + ["rationale"],
help="Describe the curation rationale behind the original dataset(s).",
)
make_text_area(
label="What was the communicative goal?",
key_list=key_pref + ["communicative"],
help="Describe the communicative goal that the original dataset(s) was trying to represent.",
)
make_radio(
label="Is the dataset aggregated from different data sources?",
options=["no", "yes"],
key_list=key_pref + ["is-aggregated"],
help="e.g. Wikipedia, movi dialogues, etc.",
)
make_text_area(
label="If yes, list the sources",
key_list=key_pref + ["aggregated-sources"],
help="Otherwise, type N/A",
)
with st.expander("Language Data", expanded=False):
key_pref = ["curation", "language"]
st.session_state.card_dict["curation"]["language"] = st.session_state.card_dict[
"curation"
].get("language", {})
make_multiselect(
label="How was the language data obtained?",
options=[
"Found",
"Created for the dataset",
"Crowdsourced",
"Machine-generated",
"Other",
],
key_list=key_pref + ["obtained"],
)
make_multiselect(
label="If found, where from?",
options=["Multiple websites", "Single website", "Offline media collection", "Other", "N/A"],
key_list=key_pref + ["found"],
help="select N/A if none of the language data was found",
)
make_multiselect(
label="If crowdsourced, where from?",
options=[
"Amazon Mechanical Turk",
"Other crowdworker platform",
"Participatory experiment",
"Other",
"N/A",
],
key_list=key_pref + ["crowdsourced"],
help="select N/A if none of the language data was crowdsourced",
)
make_text_area(
label="If created for the dataset, describe the creation process.",
key_list=key_pref + ["created"],
)
make_text_area(
label="What further information do we have on the language producers?",
key_list=key_pref + ["producers-description"],
help="Provide a description of the context in which the language was produced and who produced it.",
)
make_text_input(
label="If text was machine-generated for the dataset, provide a link to the generation method if available (N/A otherwise).",
key_list=key_pref + ["machine-generated"],
help="if the generation code is unavailable, enter N/A",
)
make_selectbox(
label="Was the text validated by a different worker or a data curator?",
options=[
"not validated",
"validated by crowdworker",
"validated by data curator",
"other",
],
key_list=key_pref + ["validated"],
help="this question is about human or human-in-the-loop validation only",
)
make_multiselect(
label="In what kind of organization did the curation happen?",
options=["industry", "academic", "independent", "other"],
key_list=key_pref + ["organization-type"],
)
make_text_input(
label="Name the organization(s).",
key_list=key_pref + ["organization-names"],
help="comma-separated",
)
make_text_area(
label="How was the text data pre-processed? (Enter N/A if the text was not pre-processed)",
key_list=key_pref + ["pre-processed"],
help="List the steps in preprocessing the data for the dataset. Enter N/A if no steps were taken.",
)
make_selectbox(
label="Were text instances selected or filtered?",
options=["not filtered", "manually", "algorithmically", "hybrid"],
key_list=key_pref + ["is-filtered"],
)
make_text_area(
label="What were the selection criteria?",
key_list=key_pref + ["filtered-criteria"],
help="Describe the process for selecting instances to include in the dataset, including any tools used. If no selection was done, enter N/A.",
)
with st.expander("Structured Annotations", expanded=False):
key_pref = ["curation", "annotations"]
st.session_state.card_dict["curation"][
"annotations"
] = st.session_state.card_dict["curation"].get("annotations", {})
make_radio(
label="Does the dataset have additional annotations for each instance?",
options=["none", "found", "automatically created", "expert created", "crowd-sourced"],
key_list=key_pref + ["origin"],
help="Was any additional data collected?",
)
# TODO: If yes....
# If expert or crowdsourced, this branch
make_radio(
label="What is the number of raters ",
options=["unknown", "1", "2<n<10", "11<n<50", "51<n<100", "n>100"],
key_list=key_pref + ["rater-number"],
help="How many raters were used to create the additional annotations?",
)
make_text_area(
label="Describe the qualifications required of an annotator.",
key_list=key_pref + ["rater-qualifications"],
help="e.g., languages or dialects they speak, education requirements, number of HITs (if MTurk).",
)
make_radio(
label="How many annotators saw each training example?",
options=["0", "1", "2", "3", "4", "5", ">5"],
key_list=key_pref + ["rater-training-num"],
help="",
)
make_radio(
label="How many annotators saw each test example?",
options=["0", "1", "2", "3", "4", "5", ">5"],
key_list=key_pref + ["rater-test-num"],
help="",
)
make_radio(
label="Was an annotation service used?",
options=["yes", "no", "unknown"],
key_list=key_pref + ["rater-annotation-service-bool"],
help="",
)
# TODO if yes
make_multiselect(
label="Which annotation services were used?",
options=[
"Amazon Mechanical Turk", "Prolific Academic",
"Upwork", "Appen", "Crowdflower", "other"
],
key_list=key_pref + ["rater-annotation-service"],
)
make_text_area(
label="Purpose and values for each annoation",
key_list=key_pref + ["values"],
help="Describe the purpose and possible values for each kind of annotation.",
)
make_multiselect(
label="Quality control measures?",
options=["none", "unknown", "validated by another rater", "validated by data curators", "validated through automated script", "other"],
key_list=key_pref + ["quality-control"],
help="How was annotation quality controlled for / what control measures were put in place to ensure annotation quality?",
)
# TODO: If not none / unknown
make_text_area(
label="Describe the quality control measures that were taken.",
key_list=key_pref + ["quality-control-details"],
help="Describe how quality was ensured in the data curation process.",
)
with st.expander("Consent", expanded=False):
key_pref = ["curation", "consent"]
st.session_state.card_dict["curation"]["consent"] = st.session_state.card_dict[
"curation"
].get("consent", {})
with st.expander("Private Identifying Information (PII)", expanded=False):
key_pref = ["curation", "pii"]
st.session_state.card_dict["curation"]["pii"] = st.session_state.card_dict[
"curation"
].get("pii", {})
with st.expander("Maintenance", expanded=False):
key_pref = ["curation", "maintenance"]
st.session_state.card_dict["curation"][
"maintenance"
] = st.session_state.card_dict["curation"].get("maintenance", {})
with st.expander("GEM Additional Curation", expanded=False):
key_pref = ["curation", "gem"]
st.session_state.card_dict["curation"]["gem"] = st.session_state.card_dict[
"curation"
].get("gem", {})
def curation_summary():
total_filled = sum(
[len(dct) for dct in st.session_state.card_dict.get("curation", {}).values()]
)
with st.expander(
f"Dataset Curation Completion - {total_filled} of {N_FIELDS}", expanded=False
):
completion_markdown = ""
completion_markdown += (
f"- **Overall competion:**\n - {total_filled} of {N_FIELDS} fields\n"
)
completion_markdown += f"- **Sub-section - Original Curation:**\n - {len(st.session_state.card_dict.get('curation', {}).get('original', {}))} of {N_FIELDS_ORIGINAL} fields\n"
completion_markdown += f"- **Sub-section - Language Data:**\n - {len(st.session_state.card_dict.get('curation', {}).get('language', {}))} of {N_FIELDS_LANGUAGE} fields\n"
completion_markdown += f"- **Sub-section - Structured Annotations:**\n - {len(st.session_state.card_dict.get('curation', {}).get('annotations', {}))} of {N_FIELDS_ANNOTATIONS} fields\n"
completion_markdown += f"- **Sub-section - Consent:**\n - {len(st.session_state.card_dict.get('curation', {}).get('consent', {}))} of {N_FIELDS_CONSENT} fields\n"
completion_markdown += f"- **Sub-section - PII:**\n - {len(st.session_state.card_dict.get('curation', {}).get('pii', {}))} of {N_FIELDS_PII} fields\n"
completion_markdown += f"- **Sub-section - Maintenance:**\n - {len(st.session_state.card_dict.get('curation', {}).get('maintenance', {}))} of {N_FIELDS_MAINTENANCE} fields\n"
completion_markdown += f"- **Sub-section - GEM Curation:**\n - {len(st.session_state.card_dict.get('curation', {}).get('gem', {}))} of {N_FIELDS_GEM} fields\n"
st.markdown(completion_markdown)
|