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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_RATIONALE = 5
N_FIELDS_STARTING = 2
N_FIELDS = N_FIELDS_RATIONALE + \
N_FIELDS_STARTING
def gem_page():
st.session_state.card_dict["gem"] = st.session_state.card_dict.get("gem", {})
with st.expander("Rationale", expanded=False):
key_pref = ["gem", "rationale"]
st.session_state.card_dict["gem"]["rationale"] = st.session_state.card_dict["gem"].get("rationale", {})
make_text_area(
label="What does this dataset contribute toward better generation evaluation and why is it part of GEM?",
key_list=key_pref + ["contribution"],
help="Describe briefly what makes this dataset an interesting target for NLG evaluations and why it is part of GEM",
)
make_radio(
label="Do other datasets for the high level task exist?",
options=["no", "yes"],
key_list=key_pref + ["sole-task-dataset"],
help="for example, is this the only summarization dataset proposed in GEM",
)
make_radio(
label="Does this dataset cover other languages than other datasets for the same task?",
options=["no", "yes"],
key_list=key_pref + ["sole-language-task-dataset"],
help="for example, is this the only summarization dataset proposed in GEM to have French text?",
)
make_text_area(
label="What else sets this dataset apart from other similar datasets in GEM?",
key_list=key_pref + ["distinction-description"],
help="Describe briefly for each similar dataset (same task/languages) what sets this one apart",
)
make_text_area(
label="What aspect of model ability can be measured with this dataset?",
key_list=key_pref + ["model-ability"],
help="What kind of abilities should a model exhibit that performs well on the task of this dataset (e.g., reasoning capability, morphological inflection)?",
)
with st.expander("Getting Started", expanded=False):
key_pref = ["gem", "starting"]
st.session_state.card_dict["gem"]["starting"] = st.session_state.card_dict["gem"].get("starting", {})
make_text_area(
label="Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task.",
key_list=key_pref + ["research-pointers"],
help=" These can include blog posts, research papers, literature surveys, etc. You can also link to tutorials on the GEM website.",
)
make_text_area(
label="Technical terms used in this card and the dataset and their definitions",
key_list=key_pref + ["technical-terms"],
help="Provide a brief definition of technical terms that are unique to this dataset",
)
def gem_summary():
total_filled = sum([len(dct) for dct in st.session_state.card_dict.get('gem', {}).values()])
with st.expander(f"Dataset in GEM 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 - Rationale:**\n - {len(st.session_state.card_dict.get('gem', {}).get('rationale', {}))} of {N_FIELDS_RATIONALE} fields\n"
completion_markdown += f"- **Sub-section - Getting Started:**\n - {len(st.session_state.card_dict.get('gem', {}).get('starting', {}))} of {N_FIELDS_STARTING} fields\n"
st.markdown(completion_markdown)
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