datacards/gem.py

import streamlit as st

from .streamlit_utils import make_text_input

from .streamlit_utils import (
    make_text_area,
    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)