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| from datasets import load_dataset | |
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| dataset = load_dataset("GroNLP/divemt") | |
| df = dataset["train"].to_pandas() | |
| unique_src = df[["item_id", "src_text"]].drop_duplicates(subset="item_id") | |
| langs = list(df["lang_id"].unique()) | |
| st.title("DivEMT Explorer") | |
| cc1, _ = st.columns([2, 1]) | |
| with cc1: | |
| st.write(""" | |
| The DivEMT Explorer is a tool to explore translations and edits contained in the DivEMT corpus. | |
| Use the expandable section "Explore examples" below to visualize some of the original source sentences. When you found a sentence that you might be interested in, insert its numeric id (between 0 and 429) in the box below, and select all the languages for which you want to visualize the results. | |
| Inside every generated section you will find the translations for all the available settings, alongside aligned edits and a collection of collected metadata. You can filter the showed settings to better see the aligned edits annotations. | |
| """) | |
| with st.expander("Explore examples"): | |
| col1, col2, _ = st.columns([3,2,5]) | |
| with col1: | |
| offset = st.slider( | |
| "Select an offset", | |
| min_value=0, | |
| max_value=len(unique_src) - 5, | |
| value=0, | |
| ) | |
| with col2: | |
| count = st.number_input( | |
| 'Select the number of examples to display', | |
| min_value=3, | |
| max_value=len(unique_src), | |
| value=5, | |
| ) | |
| st.table(unique_src[offset:int(offset+count)]) | |
| col1_main, col2_main, _ = st.columns([1,1,3]) | |
| with col1_main: | |
| item_id = st.number_input( | |
| 'Select an item (0-429) to inspect', | |
| min_value=0, | |
| max_value=len(unique_src) - 1, | |
| ) | |
| with col2_main: | |
| langs = st.multiselect( | |
| 'Select languages', | |
| options=langs | |
| ) | |
| st.markdown("<b>Source text:</b> <span style='color: #ff4b4b'> " + unique_src.iloc[int(item_id)]["src_text"] + "</span>", unsafe_allow_html=True) | |
| task_names = ["From Scratch (HT)", "Google PE (PE1)", "mBART PE (PE2)"] | |
| for lang in langs: | |
| with st.expander(f"View {lang.upper()} data"): | |
| c1, _ = st.columns([1, 2]) | |
| with c1: | |
| tasks = st.multiselect( | |
| 'Select settings', | |
| options=task_names, | |
| default=task_names, | |
| key=f"{lang}_tasks" | |
| ) | |
| columns = st.columns(len(tasks)) | |
| lang_data = df[(df["item_id"] == unique_src.iloc[int(item_id)]["item_id"]) & (df["lang_id"] == lang)] | |
| lang_dicts = lang_data.to_dict("records") | |
| ht = [x for x in lang_dicts if x["task_type"] == "ht"][0] | |
| pe1 = [x for x in lang_dicts if x["task_type"] == "pe1"][0] | |
| pe2 = [x for x in lang_dicts if x["task_type"] == "pe2"][0] | |
| task_dict = {k:v for k,v in zip(task_names, [ht, pe1, pe2])} | |
| max_mt_length = max([len(x["mt_text"]) for x in lang_dicts if x["mt_text"] is not None]) | |
| for task_name, dic, col in zip(tasks, [task_dict[name] for name in tasks], columns): | |
| with col: | |
| st.header(task_name) | |
| st.markdown(f"<b>Translator</b>: {dic['subject_id']}", unsafe_allow_html=True) | |
| mt_text = dic["mt_text"] | |
| if mt_text is None: | |
| mt_text = "<span style='opacity:0'>" + "".join(["O " for i in range(max_mt_length // 2)]) + "</span>" | |
| st.markdown(f"<b>MT</b>: {mt_text}", unsafe_allow_html=True) | |
| st.markdown(f"<b>PE</b>: {dic['tgt_text']}", unsafe_allow_html=True) | |
| st.markdown(f"<b>Aligned edits</b>:", unsafe_allow_html=True) | |
| if dic["aligned_edit"] is not None: | |
| st.text(dic["aligned_edit"].replace("\\n", "\n").replace("REF:", "MT :").replace("HYP:", "PE :")) | |
| else: | |
| st.text("MT : N/A\nPE : N/A\nEVAL: N/A\n") | |
| st.markdown(f"<b>Metadata</b>:", unsafe_allow_html=True) | |
| st.json({k:v for k,v in dic.items() if k not in ["src_text", "mt_text", "tgt_text", "aligned_edit"]}) |