From 317cd1fb5654c8be1f6291b7df10497da6c3e4fc Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Wed, 1 Apr 2026 13:03:18 -0400
Subject: [PATCH 01/10] Update metrics.py

---
 src/muse/evaluation/metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/muse/evaluation/metrics.py b/src/muse/evaluation/metrics.py
index 0d7f773..43cda3a 100644
--- a/src/muse/evaluation/metrics.py
+++ b/src/muse/evaluation/metrics.py
@@ -139,6 +139,6 @@ def compute_cometkiwi(
     # Predict returns a Prediction object; access the first score
     model_output = model.predict(data, batch_size=1, gpus=gpus)
     # The Prediction object can be indexed to get individual scores
-    score = model_output[0]
+    score = model_output[0][0]
 
     return score

From 4d4c6effbdef58a051ecbe71cc65b6112f47c77d Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Wed, 1 Apr 2026 13:06:21 -0400
Subject: [PATCH 02/10] ruff format

---
 src/muse/evaluation/metrics.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/muse/evaluation/metrics.py b/src/muse/evaluation/metrics.py
index 43cda3a..2c07ef7 100644
--- a/src/muse/evaluation/metrics.py
+++ b/src/muse/evaluation/metrics.py
@@ -120,7 +120,9 @@ def compute_cometkiwi(
         try:
             model_path = download_model("Unbabel/wmt22-cometkiwi-da")
             LOADED_METRICS["cometkiwi"] = load_from_checkpoint(model_path)
-        except KeyError as e:  # download_model catches all exceptions and re-raises as KeyError
+        except (
+            KeyError
+        ) as e:  # download_model catches all exceptions and re-raises as KeyError
             msg = (
                 "Authentication required for CometKiwi model. "
                 "Please:\n"

From dfcd2193f31728f8e86e7f7d2c756eb93185cb10 Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Mon, 6 Apr 2026 09:32:45 -0400
Subject: [PATCH 03/10] Add qualitative analysis marimo notebook

---
 notebooks/mt_eval_analysis.py | 658 ++++++++++++++++++++++++++++++++++
 1 file changed, 658 insertions(+)
 create mode 100644 notebooks/mt_eval_analysis.py

diff --git a/notebooks/mt_eval_analysis.py b/notebooks/mt_eval_analysis.py
new file mode 100644
index 0000000..3e1d54d
--- /dev/null
+++ b/notebooks/mt_eval_analysis.py
@@ -0,0 +1,658 @@
+import marimo
+
+__generated_with = "0.22.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+
+    return (mo,)
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    # MT Evaluation Score Analysis
+
+    chrF, COMET, CometKiwi across three models (google_tllm, hymt, gemma)
+    and two corpora (sentences: 427 pairs, paragraphs: 526 pairs).
+    """)
+    return
+
+
+@app.cell
+def _():
+    import pathlib
+
+    import altair as alt
+    import pandas as pd
+    import polars as pl
+    from scipy import stats
+
+    ROOT = pathlib.Path(__file__).parent.parent
+    return ROOT, alt, pd, pl, stats
+
+
+@app.cell
+def _(ROOT, pl):
+    # Load sentence eval CSVs and join with metadata from notion-concept-tasks.jsonl
+    sents_meta = pl.read_ndjson(ROOT / "notion-concept-tasks.jsonl").select(
+        ["tr_id", "model", "src_lang", "tr_lang", "pair_id"]
+    )
+
+
+    def load_sents_csv(model: str) -> pl.DataFrame:
+        return pl.read_csv(ROOT / f"eval-sents-{model}.csv").join(
+            sents_meta.filter(pl.col("model") == model), on="tr_id"
+        )
+
+
+    sents_df = pl.concat(
+        [
+            load_sents_csv("google_tllm"),
+            load_sents_csv("hymt"),
+            load_sents_csv("gemma"),
+        ]
+    )
+
+    sents_df.head(3)
+    return (sents_df,)
+
+
+@app.cell
+def _(ROOT, pl):
+    # Load paragraph eval CSVs and join with metadata from tr-pars-*.jsonl
+    def load_pars_csv(model: str) -> pl.DataFrame:
+        meta = pl.read_ndjson(ROOT / f"tr-pars-{model}.jsonl").select(
+            ["tr_id", "model", "src_lang", "tr_lang", "pair_id"]
+        )
+        return pl.read_csv(ROOT / f"eval-pars-{model}.csv").join(meta, on="tr_id")
+
+
+    pars_df = pl.concat(
+        [
+            load_pars_csv("google_tllm"),
+            load_pars_csv("hymt"),
+            load_pars_csv("gemma"),
+        ]
+    )
+
+    pars_df.head(3)
+    return (pars_df,)
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## 1. Score distributions by model
+    """)
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, sents_df):
+    def score_dist_chart(df, title: str):
+        melted = (
+            df.select(["model", "chrf", "comet", "cometkiwi"])
+            .unpivot(index="model", variable_name="metric", value_name="score")
+            .to_pandas()
+        )
+        return (
+            alt.Chart(melted, title=title)
+            .mark_boxplot(extent="min-max")
+            .encode(
+                x=alt.X("model:N", title="Model"),
+                y=alt.Y("score:Q", title="Score", scale=alt.Scale(zero=False)),
+                color="model:N",
+                column=alt.Column("metric:N", title="Metric"),
+            )
+            .properties(width=160, height=220)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences**"),
+            mo.ui.altair_chart(score_dist_chart(sents_df, "Sentences")),
+            mo.md("**Paragraphs**"),
+            mo.ui.altair_chart(score_dist_chart(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## 2. Model ranking
+    """)
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, pl, sents_df):
+    def model_ranking_chart(df, title: str):
+        melted = (
+            df.group_by("model")
+            .agg(
+                pl.col("chrf").mean().alias("chrf"),
+                pl.col("comet").mean().alias("comet"),
+                pl.col("cometkiwi").mean().alias("cometkiwi"),
+            )
+            .unpivot(index="model", variable_name="metric", value_name="score")
+            .to_pandas()
+        )
+        return (
+            alt.Chart(melted, title=title)
+            .mark_bar()
+            .encode(
+                x=alt.X(
+                    "score:Q", scale=alt.Scale(zero=False), title="Mean score"
+                ),
+                y=alt.Y("model:N", sort="-x", title=None),
+                color="model:N",
+                row=alt.Row("metric:N", title="Metric"),
+            )
+            .properties(width=300, height=70)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences**"),
+            mo.ui.altair_chart(model_ranking_chart(sents_df, "Sentences")),
+            mo.md("**Paragraphs**"),
+            mo.ui.altair_chart(model_ranking_chart(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## 3. Metric agreement
+
+    Key finding: chrF vs CometKiwi correlation collapses on paragraphs (r ≈ −0.05), suggesting reference translations are paraphrastic rather than literal. Low paragraph COMET/CometKiwi correlation (r ≈ 0.28) means they diverge on longer texts — CometKiwi is arguably more honest since it doesn't penalise paraphrastic output.
+    """)
+    return
+
+
+@app.cell
+def _(mo, pars_df, sents_df, stats):
+    def correlation_table(df, label: str) -> str:
+        pairs = [("chrf", "comet"), ("chrf", "cometkiwi"), ("comet", "cometkiwi")]
+        rows = []
+        for a, b in pairs:
+            x = df[a].to_numpy()
+            y = df[b].to_numpy()
+            r, p = stats.pearsonr(x, y)
+            rho, p_rho = stats.spearmanr(x, y)
+            rows.append(
+                f"| {a} vs {b} | {r:.3f} | {p:.2e} | {rho:.3f} | {p_rho:.2e} |"
+            )
+        header = (
+            f"**{label}**\n\n"
+            "| Pair | Pearson r | p | Spearman ρ | p |\n"
+            "|------|-----------|---|------------|---|"
+        )
+        return header + "\n" + "\n".join(rows)
+
+
+    mo.vstack(
+        [
+            mo.md(correlation_table(sents_df, "Sentences")),
+            mo.md(correlation_table(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, sents_df):
+    def scatter_matrix(df, title: str):
+        pdf = df.select(["model", "chrf", "comet", "cometkiwi"]).to_pandas()
+        return (
+            alt.Chart(pdf)
+            .mark_point(opacity=0.4, size=20)
+            .encode(
+                alt.X(
+                    alt.repeat("column"),
+                    type="quantitative",
+                    scale=alt.Scale(zero=False),
+                ),
+                alt.Y(
+                    alt.repeat("row"),
+                    type="quantitative",
+                    scale=alt.Scale(zero=False),
+                ),
+                color="model:N",
+            )
+            .repeat(
+                row=["chrf", "comet", "cometkiwi"],
+                column=["chrf", "comet", "cometkiwi"],
+            )
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences**"),
+            mo.ui.altair_chart(scatter_matrix(sents_df, "Sentences")),
+            mo.md("**Paragraphs**"),
+            mo.ui.altair_chart(scatter_matrix(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, sents_df):
+    def kiwi_vs_comet(df, title: str):
+        pdf = df.select(["model", "comet", "cometkiwi"]).to_pandas()
+        base = alt.Chart(pdf, title=title).properties(width=300, height=260)
+        points = base.mark_point(opacity=0.5, size=25).encode(
+            x=alt.X("comet:Q", scale=alt.Scale(zero=False), title="COMET"),
+            y=alt.Y("cometkiwi:Q", scale=alt.Scale(zero=False), title="CometKiwi"),
+            color="model:N",
+        )
+        regression = (
+            base.transform_regression("comet", "cometkiwi")
+            .mark_line(color="gray", strokeDash=[4, 2])
+            .encode(
+                x=alt.X("comet:Q"),
+                y=alt.Y("cometkiwi:Q"),
+            )
+        )
+        return alt.layer(points, regression)
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences — COMET vs CometKiwi**"),
+            mo.ui.altair_chart(kiwi_vs_comet(sents_df, "Sentences")),
+            mo.md("**Paragraphs — COMET vs CometKiwi**"),
+            mo.ui.altair_chart(kiwi_vs_comet(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, pd, pl, sents_df, stats):
+    def agreement_heatmap(df, title: str):
+        pairs = [("chrf", "comet"), ("chrf", "cometkiwi"), ("comet", "cometkiwi")]
+        rows = [
+            {
+                "model": model,
+                "pair": f"{a} vs {b}",
+                "rho": round(
+                    stats.spearmanr(
+                        df.filter(pl.col("model") == model)[a].to_numpy(),
+                        df.filter(pl.col("model") == model)[b].to_numpy(),
+                    )[0],
+                    3,
+                ),
+            }
+            for model in sorted(df["model"].unique().to_list())
+            for a, b in pairs
+        ]
+        pdf = pd.DataFrame(rows)
+        return (
+            alt.Chart(pdf, title=title)
+            .mark_rect()
+            .encode(
+                x=alt.X("pair:N", title=None),
+                y=alt.Y("model:N", title=None),
+                color=alt.Color(
+                    "rho:Q",
+                    scale=alt.Scale(scheme="redblue", domain=[-1, 1]),
+                    title="Spearman ρ",
+                ),
+                tooltip=["model", "pair", "rho"],
+            )
+            .properties(width=280, height=100)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences — instance-level metric agreement (Spearman ρ)**"),
+            mo.ui.altair_chart(agreement_heatmap(sents_df, "Sentences")),
+            mo.md("**Paragraphs — instance-level metric agreement (Spearman ρ)**"),
+            mo.ui.altair_chart(agreement_heatmap(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## 4. Language breakdown
+
+    If only chrF drops for CJK languages, it's a script artefact. If COMET/CometKiwi also drop, it's a real quality signal.
+    """)
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, pl, sents_df):
+    def lang_dir_chart(df, title: str):
+        melted = (
+            df.with_columns(
+                (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("direction")
+            )
+            .select(["direction", "chrf", "comet", "cometkiwi"])
+            .unpivot(index="direction", variable_name="metric", value_name="score")
+            .to_pandas()
+        )
+        return (
+            alt.Chart(melted, title=title)
+            .mark_boxplot(extent="min-max")
+            .encode(
+                x=alt.X("direction:N", title="Direction"),
+                y=alt.Y("score:Q", scale=alt.Scale(zero=False)),
+                color="direction:N",
+                column=alt.Column("metric:N"),
+            )
+            .properties(width=160, height=220)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences — score by direction**"),
+            mo.ui.altair_chart(lang_dir_chart(sents_df, "Sentences")),
+            mo.md("**Paragraphs — score by direction**"),
+            mo.ui.altair_chart(lang_dir_chart(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, pl, sents_df):
+    def lang_breakdown_chart(df, title: str) -> alt.Chart:
+        melted = (
+            df.with_columns(
+                (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("direction")
+            )
+            .group_by(["direction", "model"])
+            .agg(
+                pl.col("chrf").mean().round(4).alias("chrf"),
+                pl.col("comet").mean().round(4).alias("comet"),
+                pl.col("cometkiwi").mean().round(4).alias("cometkiwi"),
+            )
+            .unpivot(
+                index=["direction", "model"],
+                variable_name="metric",
+                value_name="score",
+            )
+            .to_pandas()
+        )
+        return (
+            alt.Chart(melted, title=title)
+            .mark_bar()
+            .encode(
+                x=alt.X("model:N", title=None),
+                y=alt.Y("score:Q", scale=alt.Scale(zero=False)),
+                color="model:N",
+                column=alt.Column("direction:N", title="Direction"),
+                row=alt.Row("metric:N", title="Metric"),
+            )
+            .properties(width=80, height=100)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences — mean score by direction and model**"),
+            mo.ui.altair_chart(lang_breakdown_chart(sents_df, "Sentences")),
+            mo.md("**Paragraphs — mean score by direction and model**"),
+            mo.ui.altair_chart(lang_breakdown_chart(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(alt, mo, pars_df, pd, pl, sents_df):
+    def direction_chart(df, title: str):
+        rows = [
+            {
+                "direction": direction,
+                "metric": metric,
+                "score": group[metric].mean(),
+            }
+            for direction, group in [
+                ("src→en", df.filter(pl.col("tr_lang") == "en")),
+                ("en→src", df.filter(pl.col("src_lang") == "en")),
+            ]
+            for metric in ["chrf", "comet", "cometkiwi"]
+        ]
+        pdf = pd.DataFrame(rows)
+        return (
+            alt.Chart(pdf, title=title)
+            .mark_bar()
+            .encode(
+                x=alt.X("direction:N", title=None),
+                y=alt.Y(
+                    "score:Q", scale=alt.Scale(zero=False), title="Mean score"
+                ),
+                color="direction:N",
+                column=alt.Column("metric:N", title="Metric"),
+            )
+            .properties(width=120, height=180)
+        )
+
+
+    mo.vstack(
+        [
+            mo.md("**Sentences — src→en vs en→src**"),
+            mo.ui.altair_chart(direction_chart(sents_df, "Sentences")),
+            mo.md("**Paragraphs — src→en vs en→src**"),
+            mo.ui.altair_chart(direction_chart(pars_df, "Paragraphs")),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(ROOT, pars_df, pl, sents_df):
+    # Load full text corpora and join with scores for qualitative browser
+    _sents_full = (
+        pl.read_ndjson(ROOT / "notion-concept-tasks.jsonl")
+        .rename({"text": "tr_text"})
+        .select(
+            [
+                "tr_id",
+                "pair_id",
+                "model",
+                "src_lang",
+                "tr_lang",
+                "src_text",
+                "ref_text",
+                "tr_text",
+                "term",
+            ]
+        )
+        .with_columns(pl.lit("sentences").alias("corpus"))
+    )
+    _pars_full = pl.concat(
+        [
+            pl.read_ndjson(ROOT / f"tr-pars-{m}.jsonl").select(
+                [
+                    "tr_id",
+                    "pair_id",
+                    "model",
+                    "src_lang",
+                    "tr_lang",
+                    "src_text",
+                    "ref_text",
+                    "tr_text",
+                ]
+            )
+            for m in ["google_tllm", "hymt", "gemma"]
+        ]
+    ).with_columns(
+        pl.lit(None).cast(pl.String).alias("term"),
+        pl.lit("paragraphs").alias("corpus"),
+    )
+
+    _scores = pl.concat(
+        [
+            sents_df.select(["tr_id", "chrf", "comet", "cometkiwi"]),
+            pars_df.select(["tr_id", "chrf", "comet", "cometkiwi"]),
+        ]
+    )
+
+    browser_data = (
+        pl.concat([_sents_full, _pars_full])
+        .join(_scores, on="tr_id", how="left")
+        .with_columns(
+            (pl.col("cometkiwi") - pl.col("chrf")).alias("kiwi_minus_chrf")
+        )
+    )
+    return (browser_data,)
+
+
+@app.cell
+def _(browser_data, mo, pl):
+    _corpora = ["sentences", "paragraphs"]
+    _models = sorted(browser_data["model"].unique().to_list())
+    _directions = sorted(
+        browser_data.with_columns(
+            (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir")
+        )["dir"]
+        .unique()
+        .to_list()
+    )
+    _terms = ["all", *sorted(browser_data["term"].drop_nulls().unique().to_list())]
+    _sort_opts = [
+        "kiwi_minus_chrf ↓",  # high CometKiwi but low chrF — paraphrase cases
+        "kiwi_minus_chrf ↑",
+        "cometkiwi ↓",
+        "cometkiwi ↑",
+        "comet ↓",
+        "comet ↑",
+        "chrf ↓",
+        "chrf ↑",
+        "pair_id",
+    ]
+
+    corpus_sel = mo.ui.dropdown(_corpora, value="sentences", label="Corpus")
+    model_sel = mo.ui.dropdown(["all", *_models], value="all", label="Model")
+    dir_sel = mo.ui.dropdown(["all", *_directions], value="all", label="Direction")
+    term_sel = mo.ui.dropdown(_terms, value="all", label="Term")
+    sort_sel = mo.ui.dropdown(
+        _sort_opts, value="kiwi_minus_chrf ↓", label="Sort by"
+    )
+    n_sel = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
+
+    mo.vstack(
+        [
+            mo.md(
+                "## 5. Qualitative translation browser\n\nRead src / ref / translation side-by-side with scores. Sort by `kiwi_minus_chrf ↓` to surface cases where CometKiwi rates the translation highly but chrF doesn't — these are the paraphrase cases."
+            ),
+            mo.hstack(
+                [corpus_sel, model_sel, dir_sel, term_sel, sort_sel, n_sel],
+                justify="start",
+            ),
+        ]
+    )
+    return corpus_sel, dir_sel, model_sel, n_sel, sort_sel, term_sel
+
+
+@app.cell
+def _(
+    browser_data,
+    corpus_sel,
+    dir_sel,
+    mo,
+    model_sel,
+    n_sel,
+    pl,
+    sort_sel,
+    term_sel,
+):
+    _df = browser_data.filter(pl.col("corpus") == corpus_sel.value)
+
+    if model_sel.value != "all":
+        _df = _df.filter(pl.col("model") == model_sel.value)
+
+    if dir_sel.value != "all":
+        _src, _tgt = dir_sel.value.split("→")
+        _df = _df.filter(
+            (pl.col("src_lang") == _src) & (pl.col("tr_lang") == _tgt)
+        )
+
+    if term_sel.value != "all":
+        _df = _df.filter(pl.col("term") == term_sel.value)
+
+    _sort_col, _sort_dir = (
+        sort_sel.value.rsplit(" ", 1)
+        if " " in sort_sel.value
+        else (sort_sel.value, "↑")
+    )
+    _df = _df.sort(_sort_col, descending=(_sort_dir == "↓"))
+
+    _display = (
+        _df.head(n_sel.value)
+        .select(
+            [
+                "pair_id",
+                "model",
+                "src_lang",
+                "tr_lang",
+                "chrf",
+                "comet",
+                "cometkiwi",
+                "kiwi_minus_chrf",
+                "term",
+                "src_text",
+                "ref_text",
+                "tr_text",
+            ]
+        )
+        .with_columns(
+            [
+                pl.col("chrf").round(3),
+                pl.col("comet").round(3),
+                pl.col("cometkiwi").round(3),
+                pl.col("kiwi_minus_chrf").round(3),
+            ]
+        )
+    )
+
+    mo.plain(_display)
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## Summary
+
+    **Models**
+    google_tllm is the clear winner on all metrics across both corpora, with statistically
+    significant margins. gemma and hymt are statistically indistinguishable from each other.
+
+    **Metrics**
+    - **COMET** — It's trained on human judgements, robust to paraphrase,
+      and shows consistent model separation. The compressed range (0.77–0.81) is normal.
+    - **CometKiwi** — worth reporting alongside COMET, especially for paragraphs where
+      it diverges from COMET (r ≈ 0.28). Being reference-free makes it more honest when
+      the reference translations are paraphrastic, which is likely here.
+    - **chrF** — use with caution. It's the most discriminating metric numerically (high CV),
+      but for non-literal, domain-specific translations it mostly measures surface similarity
+      to the reference rather than actual quality. The near-zero correlation with CometKiwi
+      on paragraphs (r ≈ −0.05) is a red flag.
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

From f674c78996ef3cda44969ba3a2410c7b5d384a10 Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Wed, 8 Apr 2026 08:22:50 -0400
Subject: [PATCH 04/10] Update notebook to use Phase 1 data paths and add
 missing dependencies

- Add DATA_DIR variable pointing to Phase 1 data directory
- Update all data paths to canonical Phase 1 structure
- Add scipy and pandas to pyproject.toml dependencies
- Exclude notebook from ruff linting
---
 notebooks/mt_eval_analysis.py |  50 +++-
 pyproject.toml                |  14 +-
 uv.lock                       | 484 +++++++++++++++++++++++++++++++++-
 3 files changed, 528 insertions(+), 20 deletions(-)

diff --git a/notebooks/mt_eval_analysis.py b/notebooks/mt_eval_analysis.py
index 3e1d54d..1c340df 100644
--- a/notebooks/mt_eval_analysis.py
+++ b/notebooks/mt_eval_analysis.py
@@ -31,20 +31,38 @@ def _():
     import polars as pl
     from scipy import stats
 
-    ROOT = pathlib.Path(__file__).parent.parent
-    return ROOT, alt, pd, pl, stats
+    return alt, pathlib, pd, pl, stats
 
 
 @app.cell
-def _(ROOT, pl):
+def _(mo):
+    mo.md("""
+    ## Configuration
+
+    Set `DATA_DIR` to the phase-1 data directory (e.g. the project drive or TigerData mount).
+    All data files are resolved relative to this path.
+    """)
+    return
+
+
+@app.cell
+def _(pathlib):
+    # Set this to the phase-1 data directory on the project drive / TigerData mount.
+    # Defaults to the local data directory for local development.
+    DATA_DIR = pathlib.Path(__file__).parent.parent / "data" / "Phase 1"
+    return (DATA_DIR,)
+
+
+@app.cell
+def _(DATA_DIR, pl):
     # Load sentence eval CSVs and join with metadata from notion-concept-tasks.jsonl
-    sents_meta = pl.read_ndjson(ROOT / "notion-concept-tasks.jsonl").select(
-        ["tr_id", "model", "src_lang", "tr_lang", "pair_id"]
-    )
+    sents_meta = pl.read_ndjson(
+        DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
+    ).select(["tr_id", "model", "src_lang", "tr_lang", "pair_id"])
 
 
     def load_sents_csv(model: str) -> pl.DataFrame:
-        return pl.read_csv(ROOT / f"eval-sents-{model}.csv").join(
+        return pl.read_csv(DATA_DIR / f"notion-sents/eval-sents-{model}.csv").join(
             sents_meta.filter(pl.col("model") == model), on="tr_id"
         )
 
@@ -62,13 +80,15 @@ def load_sents_csv(model: str) -> pl.DataFrame:
 
 
 @app.cell
-def _(ROOT, pl):
-    # Load paragraph eval CSVs and join with metadata from tr-pars-*.jsonl
+def _(DATA_DIR, pl):
+    # Load paragraph eval CSVs and join with metadata from mt-pars-*.jsonl
     def load_pars_csv(model: str) -> pl.DataFrame:
-        meta = pl.read_ndjson(ROOT / f"tr-pars-{model}.jsonl").select(
+        meta = pl.read_ndjson(DATA_DIR / f"mto-pars/mt-pars-{model}.jsonl").select(
             ["tr_id", "model", "src_lang", "tr_lang", "pair_id"]
         )
-        return pl.read_csv(ROOT / f"eval-pars-{model}.csv").join(meta, on="tr_id")
+        return pl.read_csv(DATA_DIR / f"mto-pars/eval-pars-{model}.csv").join(
+            meta, on="tr_id"
+        )
 
 
     pars_df = pl.concat(
@@ -461,10 +481,12 @@ def direction_chart(df, title: str):
 
 
 @app.cell
-def _(ROOT, pars_df, pl, sents_df):
+def _(DATA_DIR, pars_df, pl, sents_df):
     # Load full text corpora and join with scores for qualitative browser
     _sents_full = (
-        pl.read_ndjson(ROOT / "notion-concept-tasks.jsonl")
+        pl.read_ndjson(
+            DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
+        )
         .rename({"text": "tr_text"})
         .select(
             [
@@ -483,7 +505,7 @@ def _(ROOT, pars_df, pl, sents_df):
     )
     _pars_full = pl.concat(
         [
-            pl.read_ndjson(ROOT / f"tr-pars-{m}.jsonl").select(
+            pl.read_ndjson(DATA_DIR / f"mto-pars/mt-pars-{m}.jsonl").select(
                 [
                     "tr_id",
                     "pair_id",
diff --git a/pyproject.toml b/pyproject.toml
index 9740195..5aca88d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ classifiers = [
 ]
 dependencies = [
   "pip", # required for downloading spaCy model in uv-created virtualenvs
-  "marimo>=0.13.11",
+  "marimo>=0.22.0",
   "polars>=0.20.4",
   "numpy",
   "ipython", # Required by transformers for trainer functionality
@@ -36,6 +36,12 @@ dependencies = [
   "datasets",
   "sacrebleu",
   "unbabel-comet",
+  "altair>=5",
+  "pandas",
+  "scipy",
+  "nbformat>=5.10.4",
+  "nbconvert>=7.17.0",
+  "playwright>=1.58.0",
 ]
 
 [project.optional-dependencies]
@@ -56,6 +62,8 @@ path = "src/muse/__init__.py"
 target-version = "py312"
 # Configure src path so ruff import fixes can identify local imports
 src = [ "src" ]
+# Ignore notebooks — marimo notebooks are not linted
+extend-exclude = [ "notebooks/mt_eval_analysis.py" ]
 
 [tool.ruff.lint]
 # Include these rules in addition to ruff's defaults
@@ -73,6 +81,10 @@ extend-select = [
 # Can use to ignore specific rules within above selection
 ignore = []
 
+[tool.ruff.lint.per-file-ignores]
+# Notebooks use typographic Unicode in markdown strings intentionally
+"notebooks/*.py" = ["RUF001"]
+
 [tool.marimo.display]
 cell_output = "below"
 theme = "system"      # honor user preference, including in static html exports
diff --git a/uv.lock b/uv.lock
index a7ae4b6..8424a6e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -138,6 +138,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
+[[package]]
+name = "altair"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "narwhals" },
+    { name = "packaging" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f7/c0/184a89bd5feba14ff3c41cfaf1dd8a82c05f5ceedbc92145e17042eb08a4/altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4", size = 763834, upload-time = "2025-11-12T08:59:11.519Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/33/ef2f2409450ef6daa61459d5de5c08128e7d3edb773fefd0a324d1310238/altair-6.0.0-py3-none-any.whl", hash = "sha256:09ae95b53d5fe5b16987dccc785a7af8588f2dca50de1e7a156efa8a461515f8", size = 795410, upload-time = "2025-11-12T08:59:09.804Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -169,6 +185,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.14.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
+]
+
 [[package]]
 name = "black"
 version = "26.1.0"
@@ -201,6 +230,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e4/3d/51bdb3ecbfadfaf825ec0c75e1de6077422b4afa2091c6c9ba34fbfc0c2d/black-26.1.0-py3-none-any.whl", hash = "sha256:1054e8e47ebd686e078c0bb0eaf31e6ce69c966058d122f2c0c950311f9f3ede", size = 204010, upload-time = "2026-01-18T04:50:09.978Z" },
 ]
 
+[[package]]
+name = "bleach"
+version = "6.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533, upload-time = "2025-10-27T17:57:39.211Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437, upload-time = "2025-10-27T17:57:37.538Z" },
+]
+
+[package.optional-dependencies]
+css = [
+    { name = "tinycss2" },
+]
+
 [[package]]
 name = "blobfile"
 version = "3.1.0"
@@ -470,6 +516,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" },
 ]
 
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
+]
+
 [[package]]
 name = "dill"
 version = "0.4.0"
@@ -553,6 +608,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" },
 ]
 
+[[package]]
+name = "fastjsonschema"
+version = "2.21.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" },
+]
+
 [[package]]
 name = "filelock"
 version = "3.20.3"
@@ -798,6 +862,49 @@ grpc = [
     { name = "grpcio" },
 ]
 
+[[package]]
+name = "greenlet"
+version = "3.3.2"
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@@ -2869,6 +3296,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "soupsieve"
+version = "2.8.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7b/ae/2d9c981590ed9999a0d91755b47fc74f74de286b0f5cee14c9269041e6c4/soupsieve-2.8.3.tar.gz", hash = "sha256:3267f1eeea4251fb42728b6dfb746edc9acaffc4a45b27e19450b676586e8349", size = 118627, upload-time = "2026-01-20T04:27:02.457Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/46/2c/1462b1d0a634697ae9e55b3cecdcb64788e8b7d63f54d923fcd0bb140aed/soupsieve-2.8.3-py3-none-any.whl", hash = "sha256:ed64f2ba4eebeab06cc4962affce381647455978ffc1e36bb79a545b91f45a95", size = 37016, upload-time = "2026-01-20T04:27:01.012Z" },
+]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -2964,6 +3400,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/af/df/c7891ef9d2712ad774777271d39fdef63941ffba0a9d59b7ad1fd2765e57/tiktoken-0.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71", size = 920667, upload-time = "2025-10-06T20:22:34.444Z" },
 ]
 
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" },
+]
+
 [[package]]
 name = "tokenizers"
 version = "0.22.2"
@@ -3065,6 +3513,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/08/b7/f1e49be0e076c8ec981f1d4cea1f32da2bd754eaeaf6ed74d5add3f840b4/torchmetrics-0.10.3-py3-none-any.whl", hash = "sha256:b12cf92897545e24a825b0d168888c0f3052700c2901e2d4f7d90b252bc4a343", size = 529740, upload-time = "2022-11-16T18:15:16.597Z" },
 ]
 
+[[package]]
+name = "tornado"
+version = "6.5.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/f1/3173dfa4a18db4a9b03e5d55325559dab51ee653763bb8745a75af491286/tornado-6.5.5.tar.gz", hash = "sha256:192b8f3ea91bd7f1f50c06955416ed76c6b72f96779b962f07f911b91e8d30e9", size = 516006, upload-time = "2026-03-10T21:31:02.067Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/59/8c/77f5097695f4dd8255ecbd08b2a1ed8ba8b953d337804dd7080f199e12bf/tornado-6.5.5-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:487dc9cc380e29f58c7ab88f9e27cdeef04b2140862e5076a66fb6bb68bb1bfa", size = 445983, upload-time = "2026-03-10T21:30:44.28Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/5e/7625b76cd10f98f1516c36ce0346de62061156352353ef2da44e5c21523c/tornado-6.5.5-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:65a7f1d46d4bb41df1ac99f5fcb685fb25c7e61613742d5108b010975a9a6521", size = 444246, upload-time = "2026-03-10T21:30:46.571Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/04/7b5705d5b3c0fab088f434f9c83edac1573830ca49ccf29fb83bf7178eec/tornado-6.5.5-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e74c92e8e65086b338fd56333fb9a68b9f6f2fe7ad532645a290a464bcf46be5", size = 447229, upload-time = "2026-03-10T21:30:48.273Z" },
+    { url = "https://files.pythonhosted.org/packages/34/01/74e034a30ef59afb4097ef8659515e96a39d910b712a89af76f5e4e1f93c/tornado-6.5.5-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:435319e9e340276428bbdb4e7fa732c2d399386d1de5686cb331ec8eee754f07", size = 448192, upload-time = "2026-03-10T21:30:51.22Z" },
+    { url = "https://files.pythonhosted.org/packages/be/00/fe9e02c5a96429fce1a1d15a517f5d8444f9c412e0bb9eadfbe3b0fc55bf/tornado-6.5.5-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3f54aa540bdbfee7b9eb268ead60e7d199de5021facd276819c193c0fb28ea4e", size = 448039, upload-time = "2026-03-10T21:30:53.52Z" },
+    { url = "https://files.pythonhosted.org/packages/82/9e/656ee4cec0398b1d18d0f1eb6372c41c6b889722641d84948351ae19556d/tornado-6.5.5-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:36abed1754faeb80fbd6e64db2758091e1320f6bba74a4cf8c09cd18ccce8aca", size = 447445, upload-time = "2026-03-10T21:30:55.541Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/76/4921c00511f88af86a33de770d64141170f1cfd9c00311aea689949e274e/tornado-6.5.5-cp39-abi3-win32.whl", hash = "sha256:dd3eafaaeec1c7f2f8fdcd5f964e8907ad788fe8a5a32c4426fbbdda621223b7", size = 448582, upload-time = "2026-03-10T21:30:57.142Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/23/f6c6112a04d28eed765e374435fb1a9198f73e1ec4b4024184f21faeb1ad/tornado-6.5.5-cp39-abi3-win_amd64.whl", hash = "sha256:6443a794ba961a9f619b1ae926a2e900ac20c34483eea67be4ed8f1e58d3ef7b", size = 448990, upload-time = "2026-03-10T21:30:58.857Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/c8/876602cbc96469911f0939f703453c1157b0c826ecb05bdd32e023397d4e/tornado-6.5.5-cp39-abi3-win_arm64.whl", hash = "sha256:2c9a876e094109333f888539ddb2de4361743e5d21eece20688e3e351e4990a6", size = 448016, upload-time = "2026-03-10T21:31:00.43Z" },
+]
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -3273,6 +3738,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" },
 ]
 
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" },
+]
+
 [[package]]
 name = "websockets"
 version = "16.0"

From 59895b476dd87a59d61eb9c52589e03a04e2475c Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Mon, 13 Apr 2026 14:37:06 -0400
Subject: [PATCH 05/10] Add more notes and analysis

---
 notebooks/mt_eval_analysis.py | 99 +++++++++++++++++++++++++++++++++--
 1 file changed, 96 insertions(+), 3 deletions(-)

diff --git a/notebooks/mt_eval_analysis.py b/notebooks/mt_eval_analysis.py
index 1c340df..4757d23 100644
--- a/notebooks/mt_eval_analysis.py
+++ b/notebooks/mt_eval_analysis.py
@@ -107,6 +107,28 @@ def load_pars_csv(model: str) -> pl.DataFrame:
 def _(mo):
     mo.md("""
     ## 1. Score distributions by model
+
+    Box plots showing the full distribution of each metric score per model, across all translation
+    pairs in each corpus. Each box spans the interquartile range (Q1–Q3); whiskers extend to the
+    min/max.
+
+    **Key finding:**
+
+    Observation 1: chrF distributions are wide for all three models (whiskers spanning roughly
+    0.2–1.0), pulled down at the low end by cross-script directions (CJK ↔ Latin). The median
+    is slightly higher for google_tllm (~0.55–0.58), but the large variance makes it hard to
+    draw firm conclusions from chrF alone.
+
+    Observation 2: COMET distributions are much tighter (~0.6–0.9). google_tllm has the highest
+    median (~0.80–0.82); gemma and hymt are close to each other (~0.77–0.79). On paragraphs the
+    three models converge further and the boxes nearly overlap.
+
+    Observation 3: CometKiwi follows the same pattern as COMET but with an even narrower spread.
+    google_tllm remains the top model; gemma and hymt are nearly indistinguishable.
+
+    Conclusion: google_tllm consistently outperforms the other two models across both corpora and
+    all three metrics. gemma and hymt perform at a similar level. The wide chrF spread is driven
+    by the cross-script artefact (see section 4), not by genuine quality variation.
     """)
     return
 
@@ -147,6 +169,25 @@ def score_dist_chart(df, title: str):
 def _(mo):
     mo.md("""
     ## 2. Model ranking
+
+    Bar charts showing the mean score per model for each metric, sorted highest to lowest.
+    Aggregated across all translation pairs in each corpus.
+
+    **Key finding:**
+
+    Observation 1: google_tllm ranks first on every metric and both corpora, with a consistent
+    lead. On sentences: chrF ~0.55, COMET ~0.81, CometKiwi ~0.84. On paragraphs the absolute
+    scores are similar but the gap over the other two models narrows slightly.
+
+    Observation 2: gemma and hymt are very close to each other on all metrics — the bars are
+    nearly the same length. Neither consistently beats the other across all metrics and corpora.
+
+    Observation 3: The ranking is stable across metrics (google_tllm > gemma ≈ hymt) even though
+    the absolute scale differs a lot between chrF and the two neural metrics.
+
+    Conclusion: google_tllm is the clear winner. gemma and hymt are statistically hard to
+    separate — the mean score difference between them is smaller than the within-model variance
+    seen in section 1.
     """)
     return
 
@@ -195,7 +236,37 @@ def _(mo):
     mo.md("""
     ## 3. Metric agreement
 
-    Key finding: chrF vs CometKiwi correlation collapses on paragraphs (r ≈ −0.05), suggesting reference translations are paraphrastic rather than literal. Low paragraph COMET/CometKiwi correlation (r ≈ 0.28) means they diverge on longer texts — CometKiwi is arguably more honest since it doesn't penalise paraphrastic output.
+    How well do the three metrics agree with each other at the instance level? Computed using
+    Pearson r (linear correlation) and Spearman ρ (rank correlation) across all translation pairs
+    in each corpus, pooled across models. The scatter matrix plots every metric pair against each
+    other; the correlation tables give exact coefficients and p-values. The COMET vs CometKiwi
+    scatter adds a linear regression line to make the trend visible.
+
+    **Key finding:**
+
+    Observation 1: On sentences, all three metrics show positive correlation with each other
+    (all blue in the heatmap), with chrF vs COMET being the strongest pair. The scatter plots
+    show a positive trend but with considerable spread — the metrics broadly agree on direction
+    but not on exact rankings.
+
+    Observation 2: On paragraphs, the scatter plots split into two visible clusters — one group
+    with low chrF (CJK directions) and one with higher chrF (Latin-script directions). This
+    distorts the chrF correlations. The heatmap shows chrF vs CometKiwi turning near-zero or
+    slightly negative on paragraphs (r ≈ −0.05), and chrF vs COMET also weakens substantially.
+
+    Observation 3: COMET vs CometKiwi correlation is moderate on sentences but drops further on
+    paragraphs (r ≈ 0.28), meaning the two neural metrics diverge on longer texts — they are
+    measuring somewhat different things.
+
+    Analysis: The chrF correlation collapse on paragraphs has two causes: (1) the cross-script
+    artefact described in section 4, and (2) reference translations that are paraphrastic rather
+    than literal — chrF penalises valid paraphrases, while CometKiwi (being reference-free) does
+    not. The COMET/CometKiwi divergence on paragraphs suggests CometKiwi is the more honest
+    signal for longer texts.
+
+    Conclusion: On sentences, the three metrics broadly agree. On paragraphs, chrF becomes
+    unreliable and COMET and CometKiwi should be weighted more heavily — with CometKiwi
+    preferred where reference quality is uncertain.
     """)
     return
 
@@ -353,7 +424,29 @@ def _(mo):
     mo.md("""
     ## 4. Language breakdown
 
-    If only chrF drops for CJK languages, it's a script artefact. If COMET/CometKiwi also drop, it's a real quality signal.
+    Scores broken down by translation direction (e.g. `zh→en`, `en→zh`), derived from the
+    `src_lang` and `tr_lang` fields in the eval CSVs. The box plots show the full score
+    distribution per direction; the grouped bar charts show mean scores per direction × model;
+    the final chart collapses all directions into two groups — into English (`src→en`) vs. out
+    of English (`en→src`) — to test for a systematic directionality bias.
+
+    **Key finding:**
+
+    Observation 1: chrF scores vary dramatically by direction — `ja→en` and `zh→en` score around 0.4 on
+    sentences, while `pt→en` reaches ~0.7. On paragraphs, `en→zh` collapses to near zero
+    (median ~0.08) and `en→ja` is similarly low (~0.25).
+
+    Analysis: This is not caused by quality difference. Because:
+    chrF works by counting shared characters between the translation and the reference, so when
+    the two are in completely different writing systems (e.g. CJK vs. Latin), they share no
+    characters at all and the score is near zero by construction — regardless of translation
+    quality.
+
+    Observation 2: COMET and CometKiwi are unaffected — both stay flat across all directions (~0.75–0.85),
+    with no CJK gap.
+
+    Conclusion: For cross-script directions, chrF is meaningless; COMET and CometKiwi
+    are the only reliable metrics.
     """)
     return
 
@@ -577,7 +670,7 @@ def _(browser_data, mo, pl):
     mo.vstack(
         [
             mo.md(
-                "## 5. Qualitative translation browser\n\nRead src / ref / translation side-by-side with scores. Sort by `kiwi_minus_chrf ↓` to surface cases where CometKiwi rates the translation highly but chrF doesn't — these are the paraphrase cases."
+                "## 5. Qualitative translation browser\n\nRead src / ref / translation side-by-side with scores. Data is joined from the full-text JSONL files (`notion-concept-tasks.jsonl` for sentences, `mt-pars-{model}.jsonl` for paragraphs) with the metric scores from the eval CSVs. The `kiwi_minus_chrf` column is computed as CometKiwi − chrF: a high value means the model produced a fluent translation that diverges from the reference wording. Sort by `kiwi_minus_chrf ↓` to surface the clearest paraphrase cases; sort by `cometkiwi ↓` or `comet ↓` to find the worst translations."
             ),
             mo.hstack(
                 [corpus_sel, model_sel, dir_sel, term_sel, sort_sel, n_sel],

From aa9bbd6ba5806335f554144328f8ad16da0fdb6c Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Thu, 16 Apr 2026 13:55:04 -0400
Subject: [PATCH 06/10] Revise notebook to address PR #64 review feedback

- Replace quantitative analysis (sections 1-4) with a qualitative
  translation browser, aligning the notebook with issue #17
- Split browser into two sections: Translations and Backtranslations
- Add DATA_DIR variable at top for configurable data path
- Broaden ruff exclude to cover entire notebooks/ directory
---
 notebooks/mt_eval_analysis.py | 704 +++++-----------------------------
 pyproject.toml                |   2 +-
 2 files changed, 101 insertions(+), 605 deletions(-)

diff --git a/notebooks/mt_eval_analysis.py b/notebooks/mt_eval_analysis.py
index 4757d23..0f247c3 100644
--- a/notebooks/mt_eval_analysis.py
+++ b/notebooks/mt_eval_analysis.py
@@ -14,10 +14,15 @@ def _():
 @app.cell
 def _(mo):
     mo.md("""
-    # MT Evaluation Score Analysis
+    # Phase 1 Machine Translation Browser
 
-    chrF, COMET, CometKiwi across three models (google_tllm, hymt, gemma)
-    and two corpora (sentences: 427 pairs, paragraphs: 526 pairs).
+    Browse Phase 1 machine translations side-by-side with source, reference, and evaluation
+    scores (chrF, COMET, CometKiwi). Translations and backtranslations are shown in separate
+    sections.
+
+    **Metrics:** chrF measures character n-gram overlap with the reference (unreliable for
+    cross-script directions such as `en→zh`). COMET and CometKiwi are neural metrics trained
+    on human judgements; CometKiwi is reference-free and does not penalise paraphrastic output.
     """)
     return
 
@@ -26,12 +31,9 @@ def _(mo):
 def _():
     import pathlib
 
-    import altair as alt
-    import pandas as pd
     import polars as pl
-    from scipy import stats
 
-    return alt, pathlib, pd, pl, stats
+    return pathlib, pl
 
 
 @app.cell
@@ -60,13 +62,11 @@ def _(DATA_DIR, pl):
         DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
     ).select(["tr_id", "model", "src_lang", "tr_lang", "pair_id"])
 
-
     def load_sents_csv(model: str) -> pl.DataFrame:
         return pl.read_csv(DATA_DIR / f"notion-sents/eval-sents-{model}.csv").join(
             sents_meta.filter(pl.col("model") == model), on="tr_id"
         )
 
-
     sents_df = pl.concat(
         [
             load_sents_csv("google_tllm"),
@@ -74,8 +74,6 @@ def load_sents_csv(model: str) -> pl.DataFrame:
             load_sents_csv("gemma"),
         ]
     )
-
-    sents_df.head(3)
     return (sents_df,)
 
 
@@ -90,7 +88,6 @@ def load_pars_csv(model: str) -> pl.DataFrame:
             meta, on="tr_id"
         )
 
-
     pars_df = pl.concat(
         [
             load_pars_csv("google_tllm"),
@@ -98,484 +95,12 @@ def load_pars_csv(model: str) -> pl.DataFrame:
             load_pars_csv("gemma"),
         ]
     )
-
-    pars_df.head(3)
     return (pars_df,)
 
 
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 1. Score distributions by model
-
-    Box plots showing the full distribution of each metric score per model, across all translation
-    pairs in each corpus. Each box spans the interquartile range (Q1–Q3); whiskers extend to the
-    min/max.
-
-    **Key finding:**
-
-    Observation 1: chrF distributions are wide for all three models (whiskers spanning roughly
-    0.2–1.0), pulled down at the low end by cross-script directions (CJK ↔ Latin). The median
-    is slightly higher for google_tllm (~0.55–0.58), but the large variance makes it hard to
-    draw firm conclusions from chrF alone.
-
-    Observation 2: COMET distributions are much tighter (~0.6–0.9). google_tllm has the highest
-    median (~0.80–0.82); gemma and hymt are close to each other (~0.77–0.79). On paragraphs the
-    three models converge further and the boxes nearly overlap.
-
-    Observation 3: CometKiwi follows the same pattern as COMET but with an even narrower spread.
-    google_tllm remains the top model; gemma and hymt are nearly indistinguishable.
-
-    Conclusion: google_tllm consistently outperforms the other two models across both corpora and
-    all three metrics. gemma and hymt perform at a similar level. The wide chrF spread is driven
-    by the cross-script artefact (see section 4), not by genuine quality variation.
-    """)
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, sents_df):
-    def score_dist_chart(df, title: str):
-        melted = (
-            df.select(["model", "chrf", "comet", "cometkiwi"])
-            .unpivot(index="model", variable_name="metric", value_name="score")
-            .to_pandas()
-        )
-        return (
-            alt.Chart(melted, title=title)
-            .mark_boxplot(extent="min-max")
-            .encode(
-                x=alt.X("model:N", title="Model"),
-                y=alt.Y("score:Q", title="Score", scale=alt.Scale(zero=False)),
-                color="model:N",
-                column=alt.Column("metric:N", title="Metric"),
-            )
-            .properties(width=160, height=220)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences**"),
-            mo.ui.altair_chart(score_dist_chart(sents_df, "Sentences")),
-            mo.md("**Paragraphs**"),
-            mo.ui.altair_chart(score_dist_chart(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 2. Model ranking
-
-    Bar charts showing the mean score per model for each metric, sorted highest to lowest.
-    Aggregated across all translation pairs in each corpus.
-
-    **Key finding:**
-
-    Observation 1: google_tllm ranks first on every metric and both corpora, with a consistent
-    lead. On sentences: chrF ~0.55, COMET ~0.81, CometKiwi ~0.84. On paragraphs the absolute
-    scores are similar but the gap over the other two models narrows slightly.
-
-    Observation 2: gemma and hymt are very close to each other on all metrics — the bars are
-    nearly the same length. Neither consistently beats the other across all metrics and corpora.
-
-    Observation 3: The ranking is stable across metrics (google_tllm > gemma ≈ hymt) even though
-    the absolute scale differs a lot between chrF and the two neural metrics.
-
-    Conclusion: google_tllm is the clear winner. gemma and hymt are statistically hard to
-    separate — the mean score difference between them is smaller than the within-model variance
-    seen in section 1.
-    """)
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, pl, sents_df):
-    def model_ranking_chart(df, title: str):
-        melted = (
-            df.group_by("model")
-            .agg(
-                pl.col("chrf").mean().alias("chrf"),
-                pl.col("comet").mean().alias("comet"),
-                pl.col("cometkiwi").mean().alias("cometkiwi"),
-            )
-            .unpivot(index="model", variable_name="metric", value_name="score")
-            .to_pandas()
-        )
-        return (
-            alt.Chart(melted, title=title)
-            .mark_bar()
-            .encode(
-                x=alt.X(
-                    "score:Q", scale=alt.Scale(zero=False), title="Mean score"
-                ),
-                y=alt.Y("model:N", sort="-x", title=None),
-                color="model:N",
-                row=alt.Row("metric:N", title="Metric"),
-            )
-            .properties(width=300, height=70)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences**"),
-            mo.ui.altair_chart(model_ranking_chart(sents_df, "Sentences")),
-            mo.md("**Paragraphs**"),
-            mo.ui.altair_chart(model_ranking_chart(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 3. Metric agreement
-
-    How well do the three metrics agree with each other at the instance level? Computed using
-    Pearson r (linear correlation) and Spearman ρ (rank correlation) across all translation pairs
-    in each corpus, pooled across models. The scatter matrix plots every metric pair against each
-    other; the correlation tables give exact coefficients and p-values. The COMET vs CometKiwi
-    scatter adds a linear regression line to make the trend visible.
-
-    **Key finding:**
-
-    Observation 1: On sentences, all three metrics show positive correlation with each other
-    (all blue in the heatmap), with chrF vs COMET being the strongest pair. The scatter plots
-    show a positive trend but with considerable spread — the metrics broadly agree on direction
-    but not on exact rankings.
-
-    Observation 2: On paragraphs, the scatter plots split into two visible clusters — one group
-    with low chrF (CJK directions) and one with higher chrF (Latin-script directions). This
-    distorts the chrF correlations. The heatmap shows chrF vs CometKiwi turning near-zero or
-    slightly negative on paragraphs (r ≈ −0.05), and chrF vs COMET also weakens substantially.
-
-    Observation 3: COMET vs CometKiwi correlation is moderate on sentences but drops further on
-    paragraphs (r ≈ 0.28), meaning the two neural metrics diverge on longer texts — they are
-    measuring somewhat different things.
-
-    Analysis: The chrF correlation collapse on paragraphs has two causes: (1) the cross-script
-    artefact described in section 4, and (2) reference translations that are paraphrastic rather
-    than literal — chrF penalises valid paraphrases, while CometKiwi (being reference-free) does
-    not. The COMET/CometKiwi divergence on paragraphs suggests CometKiwi is the more honest
-    signal for longer texts.
-
-    Conclusion: On sentences, the three metrics broadly agree. On paragraphs, chrF becomes
-    unreliable and COMET and CometKiwi should be weighted more heavily — with CometKiwi
-    preferred where reference quality is uncertain.
-    """)
-    return
-
-
-@app.cell
-def _(mo, pars_df, sents_df, stats):
-    def correlation_table(df, label: str) -> str:
-        pairs = [("chrf", "comet"), ("chrf", "cometkiwi"), ("comet", "cometkiwi")]
-        rows = []
-        for a, b in pairs:
-            x = df[a].to_numpy()
-            y = df[b].to_numpy()
-            r, p = stats.pearsonr(x, y)
-            rho, p_rho = stats.spearmanr(x, y)
-            rows.append(
-                f"| {a} vs {b} | {r:.3f} | {p:.2e} | {rho:.3f} | {p_rho:.2e} |"
-            )
-        header = (
-            f"**{label}**\n\n"
-            "| Pair | Pearson r | p | Spearman ρ | p |\n"
-            "|------|-----------|---|------------|---|"
-        )
-        return header + "\n" + "\n".join(rows)
-
-
-    mo.vstack(
-        [
-            mo.md(correlation_table(sents_df, "Sentences")),
-            mo.md(correlation_table(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, sents_df):
-    def scatter_matrix(df, title: str):
-        pdf = df.select(["model", "chrf", "comet", "cometkiwi"]).to_pandas()
-        return (
-            alt.Chart(pdf)
-            .mark_point(opacity=0.4, size=20)
-            .encode(
-                alt.X(
-                    alt.repeat("column"),
-                    type="quantitative",
-                    scale=alt.Scale(zero=False),
-                ),
-                alt.Y(
-                    alt.repeat("row"),
-                    type="quantitative",
-                    scale=alt.Scale(zero=False),
-                ),
-                color="model:N",
-            )
-            .repeat(
-                row=["chrf", "comet", "cometkiwi"],
-                column=["chrf", "comet", "cometkiwi"],
-            )
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences**"),
-            mo.ui.altair_chart(scatter_matrix(sents_df, "Sentences")),
-            mo.md("**Paragraphs**"),
-            mo.ui.altair_chart(scatter_matrix(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, sents_df):
-    def kiwi_vs_comet(df, title: str):
-        pdf = df.select(["model", "comet", "cometkiwi"]).to_pandas()
-        base = alt.Chart(pdf, title=title).properties(width=300, height=260)
-        points = base.mark_point(opacity=0.5, size=25).encode(
-            x=alt.X("comet:Q", scale=alt.Scale(zero=False), title="COMET"),
-            y=alt.Y("cometkiwi:Q", scale=alt.Scale(zero=False), title="CometKiwi"),
-            color="model:N",
-        )
-        regression = (
-            base.transform_regression("comet", "cometkiwi")
-            .mark_line(color="gray", strokeDash=[4, 2])
-            .encode(
-                x=alt.X("comet:Q"),
-                y=alt.Y("cometkiwi:Q"),
-            )
-        )
-        return alt.layer(points, regression)
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences — COMET vs CometKiwi**"),
-            mo.ui.altair_chart(kiwi_vs_comet(sents_df, "Sentences")),
-            mo.md("**Paragraphs — COMET vs CometKiwi**"),
-            mo.ui.altair_chart(kiwi_vs_comet(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, pd, pl, sents_df, stats):
-    def agreement_heatmap(df, title: str):
-        pairs = [("chrf", "comet"), ("chrf", "cometkiwi"), ("comet", "cometkiwi")]
-        rows = [
-            {
-                "model": model,
-                "pair": f"{a} vs {b}",
-                "rho": round(
-                    stats.spearmanr(
-                        df.filter(pl.col("model") == model)[a].to_numpy(),
-                        df.filter(pl.col("model") == model)[b].to_numpy(),
-                    )[0],
-                    3,
-                ),
-            }
-            for model in sorted(df["model"].unique().to_list())
-            for a, b in pairs
-        ]
-        pdf = pd.DataFrame(rows)
-        return (
-            alt.Chart(pdf, title=title)
-            .mark_rect()
-            .encode(
-                x=alt.X("pair:N", title=None),
-                y=alt.Y("model:N", title=None),
-                color=alt.Color(
-                    "rho:Q",
-                    scale=alt.Scale(scheme="redblue", domain=[-1, 1]),
-                    title="Spearman ρ",
-                ),
-                tooltip=["model", "pair", "rho"],
-            )
-            .properties(width=280, height=100)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences — instance-level metric agreement (Spearman ρ)**"),
-            mo.ui.altair_chart(agreement_heatmap(sents_df, "Sentences")),
-            mo.md("**Paragraphs — instance-level metric agreement (Spearman ρ)**"),
-            mo.ui.altair_chart(agreement_heatmap(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 4. Language breakdown
-
-    Scores broken down by translation direction (e.g. `zh→en`, `en→zh`), derived from the
-    `src_lang` and `tr_lang` fields in the eval CSVs. The box plots show the full score
-    distribution per direction; the grouped bar charts show mean scores per direction × model;
-    the final chart collapses all directions into two groups — into English (`src→en`) vs. out
-    of English (`en→src`) — to test for a systematic directionality bias.
-
-    **Key finding:**
-
-    Observation 1: chrF scores vary dramatically by direction — `ja→en` and `zh→en` score around 0.4 on
-    sentences, while `pt→en` reaches ~0.7. On paragraphs, `en→zh` collapses to near zero
-    (median ~0.08) and `en→ja` is similarly low (~0.25).
-
-    Analysis: This is not caused by quality difference. Because:
-    chrF works by counting shared characters between the translation and the reference, so when
-    the two are in completely different writing systems (e.g. CJK vs. Latin), they share no
-    characters at all and the score is near zero by construction — regardless of translation
-    quality.
-
-    Observation 2: COMET and CometKiwi are unaffected — both stay flat across all directions (~0.75–0.85),
-    with no CJK gap.
-
-    Conclusion: For cross-script directions, chrF is meaningless; COMET and CometKiwi
-    are the only reliable metrics.
-    """)
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, pl, sents_df):
-    def lang_dir_chart(df, title: str):
-        melted = (
-            df.with_columns(
-                (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("direction")
-            )
-            .select(["direction", "chrf", "comet", "cometkiwi"])
-            .unpivot(index="direction", variable_name="metric", value_name="score")
-            .to_pandas()
-        )
-        return (
-            alt.Chart(melted, title=title)
-            .mark_boxplot(extent="min-max")
-            .encode(
-                x=alt.X("direction:N", title="Direction"),
-                y=alt.Y("score:Q", scale=alt.Scale(zero=False)),
-                color="direction:N",
-                column=alt.Column("metric:N"),
-            )
-            .properties(width=160, height=220)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences — score by direction**"),
-            mo.ui.altair_chart(lang_dir_chart(sents_df, "Sentences")),
-            mo.md("**Paragraphs — score by direction**"),
-            mo.ui.altair_chart(lang_dir_chart(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, pl, sents_df):
-    def lang_breakdown_chart(df, title: str) -> alt.Chart:
-        melted = (
-            df.with_columns(
-                (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("direction")
-            )
-            .group_by(["direction", "model"])
-            .agg(
-                pl.col("chrf").mean().round(4).alias("chrf"),
-                pl.col("comet").mean().round(4).alias("comet"),
-                pl.col("cometkiwi").mean().round(4).alias("cometkiwi"),
-            )
-            .unpivot(
-                index=["direction", "model"],
-                variable_name="metric",
-                value_name="score",
-            )
-            .to_pandas()
-        )
-        return (
-            alt.Chart(melted, title=title)
-            .mark_bar()
-            .encode(
-                x=alt.X("model:N", title=None),
-                y=alt.Y("score:Q", scale=alt.Scale(zero=False)),
-                color="model:N",
-                column=alt.Column("direction:N", title="Direction"),
-                row=alt.Row("metric:N", title="Metric"),
-            )
-            .properties(width=80, height=100)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences — mean score by direction and model**"),
-            mo.ui.altair_chart(lang_breakdown_chart(sents_df, "Sentences")),
-            mo.md("**Paragraphs — mean score by direction and model**"),
-            mo.ui.altair_chart(lang_breakdown_chart(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
-@app.cell
-def _(alt, mo, pars_df, pd, pl, sents_df):
-    def direction_chart(df, title: str):
-        rows = [
-            {
-                "direction": direction,
-                "metric": metric,
-                "score": group[metric].mean(),
-            }
-            for direction, group in [
-                ("src→en", df.filter(pl.col("tr_lang") == "en")),
-                ("en→src", df.filter(pl.col("src_lang") == "en")),
-            ]
-            for metric in ["chrf", "comet", "cometkiwi"]
-        ]
-        pdf = pd.DataFrame(rows)
-        return (
-            alt.Chart(pdf, title=title)
-            .mark_bar()
-            .encode(
-                x=alt.X("direction:N", title=None),
-                y=alt.Y(
-                    "score:Q", scale=alt.Scale(zero=False), title="Mean score"
-                ),
-                color="direction:N",
-                column=alt.Column("metric:N", title="Metric"),
-            )
-            .properties(width=120, height=180)
-        )
-
-
-    mo.vstack(
-        [
-            mo.md("**Sentences — src→en vs en→src**"),
-            mo.ui.altair_chart(direction_chart(sents_df, "Sentences")),
-            mo.md("**Paragraphs — src→en vs en→src**"),
-            mo.ui.altair_chart(direction_chart(pars_df, "Paragraphs")),
-        ]
-    )
-    return
-
-
 @app.cell
 def _(DATA_DIR, pars_df, pl, sents_df):
-    # Load full text corpora and join with scores for qualitative browser
+    # Load full text corpora and join with scores
     _sents_full = (
         pl.read_ndjson(
             DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
@@ -627,147 +152,118 @@ def _(DATA_DIR, pars_df, pl, sents_df):
     browser_data = (
         pl.concat([_sents_full, _pars_full])
         .join(_scores, on="tr_id", how="left")
-        .with_columns(
-            (pl.col("cometkiwi") - pl.col("chrf")).alias("kiwi_minus_chrf")
-        )
     )
     return (browser_data,)
 
 
 @app.cell
-def _(browser_data, mo, pl):
-    _corpora = ["sentences", "paragraphs"]
-    _models = sorted(browser_data["model"].unique().to_list())
-    _directions = sorted(
-        browser_data.with_columns(
-            (pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir")
-        )["dir"]
-        .unique()
-        .to_list()
-    )
-    _terms = ["all", *sorted(browser_data["term"].drop_nulls().unique().to_list())]
-    _sort_opts = [
-        "kiwi_minus_chrf ↓",  # high CometKiwi but low chrF — paraphrase cases
-        "kiwi_minus_chrf ↑",
-        "cometkiwi ↓",
-        "cometkiwi ↑",
-        "comet ↓",
-        "comet ↑",
-        "chrf ↓",
-        "chrf ↑",
-        "pair_id",
-    ]
-
-    corpus_sel = mo.ui.dropdown(_corpora, value="sentences", label="Corpus")
-    model_sel = mo.ui.dropdown(["all", *_models], value="all", label="Model")
-    dir_sel = mo.ui.dropdown(["all", *_directions], value="all", label="Direction")
-    term_sel = mo.ui.dropdown(_terms, value="all", label="Term")
-    sort_sel = mo.ui.dropdown(
-        _sort_opts, value="kiwi_minus_chrf ↓", label="Sort by"
-    )
-    n_sel = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
+def _(mo):
+    mo.md("""
+    ## 1. Translations
+    """)
+    return
 
-    mo.vstack(
-        [
-            mo.md(
-                "## 5. Qualitative translation browser\n\nRead src / ref / translation side-by-side with scores. Data is joined from the full-text JSONL files (`notion-concept-tasks.jsonl` for sentences, `mt-pars-{model}.jsonl` for paragraphs) with the metric scores from the eval CSVs. The `kiwi_minus_chrf` column is computed as CometKiwi − chrF: a high value means the model produced a fluent translation that diverges from the reference wording. Sort by `kiwi_minus_chrf ↓` to surface the clearest paraphrase cases; sort by `cometkiwi ↓` or `comet ↓` to find the worst translations."
-            ),
-            mo.hstack(
-                [corpus_sel, model_sel, dir_sel, term_sel, sort_sel, n_sel],
-                justify="start",
-            ),
-        ]
+
+@app.cell
+def _(browser_data, mo, pl):
+    # Sentences are all forward translations (src != en).
+    # Paragraphs: forward translations have src_lang != en.
+    _fwd = browser_data.filter(pl.col("src_lang") != "en")
+    _corpora = sorted(_fwd["corpus"].unique().to_list())
+    corpus_sel_fwd = mo.ui.dropdown(_corpora, value="sentences", label="Corpus")
+
+    _models = sorted(_fwd["model"].unique().to_list())
+    model_sel_fwd = mo.ui.dropdown(["all", *_models], value="all", label="Model")
+
+    _all_dirs = sorted(
+        _fwd.with_columns((pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir"))["dir"]
+        .unique().to_list()
     )
-    return corpus_sel, dir_sel, model_sel, n_sel, sort_sel, term_sel
+    dir_sel_fwd = mo.ui.dropdown(["all", *_all_dirs], value="all", label="Direction")
 
+    _sort_opts = ["cometkiwi ↓", "cometkiwi ↑", "comet ↓", "comet ↑", "chrf ↓", "chrf ↑", "pair_id"]
+    sort_sel_fwd = mo.ui.dropdown(_sort_opts, value="cometkiwi ↓", label="Sort by")
+    n_sel_fwd = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
 
-@app.cell
-def _(
-    browser_data,
-    corpus_sel,
-    dir_sel,
-    mo,
-    model_sel,
-    n_sel,
-    pl,
-    sort_sel,
-    term_sel,
-):
-    _df = browser_data.filter(pl.col("corpus") == corpus_sel.value)
-
-    if model_sel.value != "all":
-        _df = _df.filter(pl.col("model") == model_sel.value)
-
-    if dir_sel.value != "all":
-        _src, _tgt = dir_sel.value.split("→")
-        _df = _df.filter(
-            (pl.col("src_lang") == _src) & (pl.col("tr_lang") == _tgt)
-        )
+    mo.hstack([corpus_sel_fwd, model_sel_fwd, dir_sel_fwd, sort_sel_fwd, n_sel_fwd], justify="start")
+    return corpus_sel_fwd, dir_sel_fwd, model_sel_fwd, n_sel_fwd, sort_sel_fwd
 
-    if term_sel.value != "all":
-        _df = _df.filter(pl.col("term") == term_sel.value)
 
-    _sort_col, _sort_dir = (
-        sort_sel.value.rsplit(" ", 1)
-        if " " in sort_sel.value
-        else (sort_sel.value, "↑")
+@app.cell
+def _(browser_data, corpus_sel_fwd, dir_sel_fwd, model_sel_fwd, mo, n_sel_fwd, pl, sort_sel_fwd):
+    _df = browser_data.filter(
+        (pl.col("src_lang") != "en") & (pl.col("corpus") == corpus_sel_fwd.value)
     )
-    _df = _df.sort(_sort_col, descending=(_sort_dir == "↓"))
-
-    _display = (
-        _df.head(n_sel.value)
-        .select(
-            [
-                "pair_id",
-                "model",
-                "src_lang",
-                "tr_lang",
-                "chrf",
-                "comet",
-                "cometkiwi",
-                "kiwi_minus_chrf",
-                "term",
-                "src_text",
-                "ref_text",
-                "tr_text",
-            ]
-        )
-        .with_columns(
-            [
-                pl.col("chrf").round(3),
-                pl.col("comet").round(3),
-                pl.col("cometkiwi").round(3),
-                pl.col("kiwi_minus_chrf").round(3),
-            ]
-        )
+    if model_sel_fwd.value != "all":
+        _df = _df.filter(pl.col("model") == model_sel_fwd.value)
+    if dir_sel_fwd.value != "all":
+        _s, _t = dir_sel_fwd.value.split("→")
+        _df = _df.filter((pl.col("src_lang") == _s) & (pl.col("tr_lang") == _t))
+    _sc, _sd = (
+        sort_sel_fwd.value.rsplit(" ", 1)
+        if " " in sort_sel_fwd.value
+        else (sort_sel_fwd.value, "↑")
+    )
+    _df = _df.sort(_sc, descending=(_sd == "↓"))
+    mo.plain(
+        _df.head(n_sel_fwd.value)
+        .select(["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"])
+        .with_columns(pl.col("chrf").round(3), pl.col("comet").round(3), pl.col("cometkiwi").round(3))
     )
-
-    mo.plain(_display)
     return
 
 
 @app.cell
 def _(mo):
     mo.md("""
-    ## Summary
-
-    **Models**
-    google_tllm is the clear winner on all metrics across both corpora, with statistically
-    significant margins. gemma and hymt are statistically indistinguishable from each other.
-
-    **Metrics**
-    - **COMET** — It's trained on human judgements, robust to paraphrase,
-      and shows consistent model separation. The compressed range (0.77–0.81) is normal.
-    - **CometKiwi** — worth reporting alongside COMET, especially for paragraphs where
-      it diverges from COMET (r ≈ 0.28). Being reference-free makes it more honest when
-      the reference translations are paraphrastic, which is likely here.
-    - **chrF** — use with caution. It's the most discriminating metric numerically (high CV),
-      but for non-literal, domain-specific translations it mostly measures surface similarity
-      to the reference rather than actual quality. The near-zero correlation with CometKiwi
-      on paragraphs (r ≈ −0.05) is a red flag.
+    ## 2. Backtranslations
     """)
     return
 
 
+@app.cell
+def _(browser_data, mo, pl):
+    # Backtranslations: src_lang == en (paragraphs only — sentences have no backtranslations).
+    _back = browser_data.filter(pl.col("src_lang") == "en")
+
+    _models_b = sorted(_back["model"].unique().to_list())
+    model_sel_back = mo.ui.dropdown(["all", *_models_b], value="all", label="Model")
+
+    _all_dirs_b = sorted(
+        _back.with_columns((pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir"))["dir"]
+        .unique().to_list()
+    )
+    dir_sel_back = mo.ui.dropdown(["all", *_all_dirs_b], value="all", label="Direction")
+
+    _sort_opts_b = ["cometkiwi ↓", "cometkiwi ↑", "comet ↓", "comet ↑", "chrf ↓", "chrf ↑", "pair_id"]
+    sort_sel_back = mo.ui.dropdown(_sort_opts_b, value="cometkiwi ↓", label="Sort by")
+    n_sel_back = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
+
+    mo.hstack([model_sel_back, dir_sel_back, sort_sel_back, n_sel_back], justify="start")
+    return dir_sel_back, model_sel_back, n_sel_back, sort_sel_back
+
+
+@app.cell
+def _(browser_data, dir_sel_back, model_sel_back, mo, n_sel_back, pl, sort_sel_back):
+    _df = browser_data.filter(pl.col("src_lang") == "en")
+    if model_sel_back.value != "all":
+        _df = _df.filter(pl.col("model") == model_sel_back.value)
+    if dir_sel_back.value != "all":
+        _s, _t = dir_sel_back.value.split("→")
+        _df = _df.filter((pl.col("src_lang") == _s) & (pl.col("tr_lang") == _t))
+    _sc, _sd = (
+        sort_sel_back.value.rsplit(" ", 1)
+        if " " in sort_sel_back.value
+        else (sort_sel_back.value, "↑")
+    )
+    _df = _df.sort(_sc, descending=(_sd == "↓"))
+    mo.plain(
+        _df.head(n_sel_back.value)
+        .select(["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"])
+        .with_columns(pl.col("chrf").round(3), pl.col("comet").round(3), pl.col("cometkiwi").round(3))
+    )
+    return
+
+
 if __name__ == "__main__":
     app.run()
diff --git a/pyproject.toml b/pyproject.toml
index 5aca88d..88de31e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,7 +63,7 @@ target-version = "py312"
 # Configure src path so ruff import fixes can identify local imports
 src = [ "src" ]
 # Ignore notebooks — marimo notebooks are not linted
-extend-exclude = [ "notebooks/mt_eval_analysis.py" ]
+extend-exclude = [ "notebooks/" ]
 
 [tool.ruff.lint]
 # Include these rules in addition to ruff's defaults

From c399c4984182672d23aeefcc298eb60de3276af7 Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Fri, 17 Apr 2026 09:40:49 -0400
Subject: [PATCH 07/10] Rewrite notebook as mt_browser.py per review feedback
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Rename to mt_browser.py
- Use full sentence corpus (mt-sents-*.jsonl, 854 rows, 4 models including madlad)
  instead of annotation subset (notion-concept-tasks.jsonl, 300 rows)
- Add sentence backtranslations section (en→* directions)
- Replace mo.ui widgets with native marimo dataframe viewer for full-text hover
- Remove metric descriptions from notebook header
- Split into 4 sections: sentence translations, sentence backtranslations,
  paragraph translations, paragraph backtranslations
---
 notebooks/mt_browser.py       | 155 ++++++++++++++++++++
 notebooks/mt_eval_analysis.py | 269 ----------------------------------
 2 files changed, 155 insertions(+), 269 deletions(-)
 create mode 100644 notebooks/mt_browser.py
 delete mode 100644 notebooks/mt_eval_analysis.py

diff --git a/notebooks/mt_browser.py b/notebooks/mt_browser.py
new file mode 100644
index 0000000..7a9e81a
--- /dev/null
+++ b/notebooks/mt_browser.py
@@ -0,0 +1,155 @@
+import marimo
+
+__generated_with = "0.22.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+
+    return (mo,)
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    # Phase 1 Machine Translation Browser
+
+    Browse Phase 1 machine translations side-by-side with source, reference, and evaluation
+    scores (chrF, COMET, CometKiwi). Translations and backtranslations are shown in separate
+    sections.
+    """)
+    return
+
+
+@app.cell
+def _():
+    import pathlib
+
+    import polars as pl
+
+    return pathlib, pl
+
+
+@app.cell
+def _(mo):
+    mo.md("""
+    ## Configuration
+
+    Set `DATA_DIR` to the phase-1 data directory (e.g. the project drive or TigerData mount).
+    All data files are resolved relative to this path.
+    """)
+    return
+
+
+@app.cell
+def _(pathlib):
+    # Set this to the phase-1 data directory on the project drive / TigerData mount.
+    # Defaults to the local data directory for local development.
+    DATA_DIR = pathlib.Path(__file__).parent.parent / "data" / "Phase 1"
+    return (DATA_DIR,)
+
+
+@app.cell
+def _(DATA_DIR, pl):
+    # Load full sentence translations and join with eval scores.
+    # Uses the complete notion-sents corpus (all Notion concepts), not the annotation subset.
+    # Note: madlad does not have eval scores.
+    _sents_meta = pl.concat(
+        [
+            pl.read_ndjson(DATA_DIR / f"notion-sents/mt-sents-{m}.jsonl")
+            for m in ["google_tllm", "hymt", "gemma", "madlad"]
+        ]
+    )
+    _sents_scores = pl.concat(
+        [
+            pl.read_csv(DATA_DIR / f"notion-sents/eval-sents-{m}.csv")
+            for m in ["google_tllm", "hymt", "gemma"]
+        ]
+    )
+    sents_df = _sents_meta.join(_sents_scores, on="tr_id", how="left").with_columns(
+        pl.col("chrf").round(3),
+        pl.col("comet").round(3),
+        pl.col("cometkiwi").round(3),
+    )
+    return (sents_df,)
+
+
+@app.cell
+def _(DATA_DIR, pl):
+    # Load paragraph translations and join with eval scores.
+    _pars_meta = pl.concat(
+        [
+            pl.read_ndjson(DATA_DIR / f"mto-pars/mt-pars-{m}.jsonl")
+            for m in ["google_tllm", "hymt", "gemma"]
+        ]
+    )
+    _pars_scores = pl.concat(
+        [
+            pl.read_csv(DATA_DIR / f"mto-pars/eval-pars-{m}.csv")
+            for m in ["google_tllm", "hymt", "gemma"]
+        ]
+    )
+    pars_df = _pars_meta.join(_pars_scores, on="tr_id", how="left").with_columns(
+        pl.col("chrf").round(3),
+        pl.col("comet").round(3),
+        pl.col("cometkiwi").round(3),
+    )
+    return (pars_df,)
+
+
+@app.cell
+def _(mo):
+    mo.md("## 1. Sentence translations")
+    return
+
+
+@app.cell
+def _(pl, sents_df):
+    sents_df.filter(pl.col("src_lang") != "en").select(
+        ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
+    )
+
+
+@app.cell
+def _(mo):
+    mo.md("## 2. Sentence backtranslations")
+    return
+
+
+@app.cell
+def _(pl, sents_df):
+    sents_df.filter(pl.col("src_lang") == "en").select(
+        ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
+    )
+
+
+@app.cell
+def _(mo):
+    mo.md("## 3. Paragraph translations")
+    return
+
+
+@app.cell
+def _(pars_df, pl):
+    pars_df.filter(pl.col("src_lang") != "en").select(
+        ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
+    )
+
+
+@app.cell
+def _(mo):
+    mo.md("## 4. Paragraph backtranslations")
+    return
+
+
+@app.cell
+def _(pars_df, pl):
+    pars_df.filter(pl.col("src_lang") == "en").select(
+        ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/notebooks/mt_eval_analysis.py b/notebooks/mt_eval_analysis.py
deleted file mode 100644
index 0f247c3..0000000
--- a/notebooks/mt_eval_analysis.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import marimo
-
-__generated_with = "0.22.0"
-app = marimo.App(width="medium")
-
-
-@app.cell
-def _():
-    import marimo as mo
-
-    return (mo,)
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    # Phase 1 Machine Translation Browser
-
-    Browse Phase 1 machine translations side-by-side with source, reference, and evaluation
-    scores (chrF, COMET, CometKiwi). Translations and backtranslations are shown in separate
-    sections.
-
-    **Metrics:** chrF measures character n-gram overlap with the reference (unreliable for
-    cross-script directions such as `en→zh`). COMET and CometKiwi are neural metrics trained
-    on human judgements; CometKiwi is reference-free and does not penalise paraphrastic output.
-    """)
-    return
-
-
-@app.cell
-def _():
-    import pathlib
-
-    import polars as pl
-
-    return pathlib, pl
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## Configuration
-
-    Set `DATA_DIR` to the phase-1 data directory (e.g. the project drive or TigerData mount).
-    All data files are resolved relative to this path.
-    """)
-    return
-
-
-@app.cell
-def _(pathlib):
-    # Set this to the phase-1 data directory on the project drive / TigerData mount.
-    # Defaults to the local data directory for local development.
-    DATA_DIR = pathlib.Path(__file__).parent.parent / "data" / "Phase 1"
-    return (DATA_DIR,)
-
-
-@app.cell
-def _(DATA_DIR, pl):
-    # Load sentence eval CSVs and join with metadata from notion-concept-tasks.jsonl
-    sents_meta = pl.read_ndjson(
-        DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
-    ).select(["tr_id", "model", "src_lang", "tr_lang", "pair_id"])
-
-    def load_sents_csv(model: str) -> pl.DataFrame:
-        return pl.read_csv(DATA_DIR / f"notion-sents/eval-sents-{model}.csv").join(
-            sents_meta.filter(pl.col("model") == model), on="tr_id"
-        )
-
-    sents_df = pl.concat(
-        [
-            load_sents_csv("google_tllm"),
-            load_sents_csv("hymt"),
-            load_sents_csv("gemma"),
-        ]
-    )
-    return (sents_df,)
-
-
-@app.cell
-def _(DATA_DIR, pl):
-    # Load paragraph eval CSVs and join with metadata from mt-pars-*.jsonl
-    def load_pars_csv(model: str) -> pl.DataFrame:
-        meta = pl.read_ndjson(DATA_DIR / f"mto-pars/mt-pars-{model}.jsonl").select(
-            ["tr_id", "model", "src_lang", "tr_lang", "pair_id"]
-        )
-        return pl.read_csv(DATA_DIR / f"mto-pars/eval-pars-{model}.csv").join(
-            meta, on="tr_id"
-        )
-
-    pars_df = pl.concat(
-        [
-            load_pars_csv("google_tllm"),
-            load_pars_csv("hymt"),
-            load_pars_csv("gemma"),
-        ]
-    )
-    return (pars_df,)
-
-
-@app.cell
-def _(DATA_DIR, pars_df, pl, sents_df):
-    # Load full text corpora and join with scores
-    _sents_full = (
-        pl.read_ndjson(
-            DATA_DIR / "prodigy/notion-concept/notion-concept-tasks.jsonl"
-        )
-        .rename({"text": "tr_text"})
-        .select(
-            [
-                "tr_id",
-                "pair_id",
-                "model",
-                "src_lang",
-                "tr_lang",
-                "src_text",
-                "ref_text",
-                "tr_text",
-                "term",
-            ]
-        )
-        .with_columns(pl.lit("sentences").alias("corpus"))
-    )
-    _pars_full = pl.concat(
-        [
-            pl.read_ndjson(DATA_DIR / f"mto-pars/mt-pars-{m}.jsonl").select(
-                [
-                    "tr_id",
-                    "pair_id",
-                    "model",
-                    "src_lang",
-                    "tr_lang",
-                    "src_text",
-                    "ref_text",
-                    "tr_text",
-                ]
-            )
-            for m in ["google_tllm", "hymt", "gemma"]
-        ]
-    ).with_columns(
-        pl.lit(None).cast(pl.String).alias("term"),
-        pl.lit("paragraphs").alias("corpus"),
-    )
-
-    _scores = pl.concat(
-        [
-            sents_df.select(["tr_id", "chrf", "comet", "cometkiwi"]),
-            pars_df.select(["tr_id", "chrf", "comet", "cometkiwi"]),
-        ]
-    )
-
-    browser_data = (
-        pl.concat([_sents_full, _pars_full])
-        .join(_scores, on="tr_id", how="left")
-    )
-    return (browser_data,)
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 1. Translations
-    """)
-    return
-
-
-@app.cell
-def _(browser_data, mo, pl):
-    # Sentences are all forward translations (src != en).
-    # Paragraphs: forward translations have src_lang != en.
-    _fwd = browser_data.filter(pl.col("src_lang") != "en")
-    _corpora = sorted(_fwd["corpus"].unique().to_list())
-    corpus_sel_fwd = mo.ui.dropdown(_corpora, value="sentences", label="Corpus")
-
-    _models = sorted(_fwd["model"].unique().to_list())
-    model_sel_fwd = mo.ui.dropdown(["all", *_models], value="all", label="Model")
-
-    _all_dirs = sorted(
-        _fwd.with_columns((pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir"))["dir"]
-        .unique().to_list()
-    )
-    dir_sel_fwd = mo.ui.dropdown(["all", *_all_dirs], value="all", label="Direction")
-
-    _sort_opts = ["cometkiwi ↓", "cometkiwi ↑", "comet ↓", "comet ↑", "chrf ↓", "chrf ↑", "pair_id"]
-    sort_sel_fwd = mo.ui.dropdown(_sort_opts, value="cometkiwi ↓", label="Sort by")
-    n_sel_fwd = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
-
-    mo.hstack([corpus_sel_fwd, model_sel_fwd, dir_sel_fwd, sort_sel_fwd, n_sel_fwd], justify="start")
-    return corpus_sel_fwd, dir_sel_fwd, model_sel_fwd, n_sel_fwd, sort_sel_fwd
-
-
-@app.cell
-def _(browser_data, corpus_sel_fwd, dir_sel_fwd, model_sel_fwd, mo, n_sel_fwd, pl, sort_sel_fwd):
-    _df = browser_data.filter(
-        (pl.col("src_lang") != "en") & (pl.col("corpus") == corpus_sel_fwd.value)
-    )
-    if model_sel_fwd.value != "all":
-        _df = _df.filter(pl.col("model") == model_sel_fwd.value)
-    if dir_sel_fwd.value != "all":
-        _s, _t = dir_sel_fwd.value.split("→")
-        _df = _df.filter((pl.col("src_lang") == _s) & (pl.col("tr_lang") == _t))
-    _sc, _sd = (
-        sort_sel_fwd.value.rsplit(" ", 1)
-        if " " in sort_sel_fwd.value
-        else (sort_sel_fwd.value, "↑")
-    )
-    _df = _df.sort(_sc, descending=(_sd == "↓"))
-    mo.plain(
-        _df.head(n_sel_fwd.value)
-        .select(["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"])
-        .with_columns(pl.col("chrf").round(3), pl.col("comet").round(3), pl.col("cometkiwi").round(3))
-    )
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md("""
-    ## 2. Backtranslations
-    """)
-    return
-
-
-@app.cell
-def _(browser_data, mo, pl):
-    # Backtranslations: src_lang == en (paragraphs only — sentences have no backtranslations).
-    _back = browser_data.filter(pl.col("src_lang") == "en")
-
-    _models_b = sorted(_back["model"].unique().to_list())
-    model_sel_back = mo.ui.dropdown(["all", *_models_b], value="all", label="Model")
-
-    _all_dirs_b = sorted(
-        _back.with_columns((pl.col("src_lang") + "→" + pl.col("tr_lang")).alias("dir"))["dir"]
-        .unique().to_list()
-    )
-    dir_sel_back = mo.ui.dropdown(["all", *_all_dirs_b], value="all", label="Direction")
-
-    _sort_opts_b = ["cometkiwi ↓", "cometkiwi ↑", "comet ↓", "comet ↑", "chrf ↓", "chrf ↑", "pair_id"]
-    sort_sel_back = mo.ui.dropdown(_sort_opts_b, value="cometkiwi ↓", label="Sort by")
-    n_sel_back = mo.ui.slider(5, 50, step=5, value=10, label="Show N")
-
-    mo.hstack([model_sel_back, dir_sel_back, sort_sel_back, n_sel_back], justify="start")
-    return dir_sel_back, model_sel_back, n_sel_back, sort_sel_back
-
-
-@app.cell
-def _(browser_data, dir_sel_back, model_sel_back, mo, n_sel_back, pl, sort_sel_back):
-    _df = browser_data.filter(pl.col("src_lang") == "en")
-    if model_sel_back.value != "all":
-        _df = _df.filter(pl.col("model") == model_sel_back.value)
-    if dir_sel_back.value != "all":
-        _s, _t = dir_sel_back.value.split("→")
-        _df = _df.filter((pl.col("src_lang") == _s) & (pl.col("tr_lang") == _t))
-    _sc, _sd = (
-        sort_sel_back.value.rsplit(" ", 1)
-        if " " in sort_sel_back.value
-        else (sort_sel_back.value, "↑")
-    )
-    _df = _df.sort(_sc, descending=(_sd == "↓"))
-    mo.plain(
-        _df.head(n_sel_back.value)
-        .select(["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"])
-        .with_columns(pl.col("chrf").round(3), pl.col("comet").round(3), pl.col("cometkiwi").round(3))
-    )
-    return
-
-
-if __name__ == "__main__":
-    app.run()

From 2dd44a91b24c09be7116a3c81778e538cc2b3eb2 Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Fri, 17 Apr 2026 11:30:33 -0400
Subject: [PATCH 08/10] Remove unused dependencies from pyproject.toml

altair, pandas, scipy, nbformat, nbconvert, and playwright were added
for the previous notebook version and are no longer used.
---
 pyproject.toml |   6 -
 uv.lock        | 431 -------------------------------------------------
 2 files changed, 437 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 88de31e..3a9d966 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,12 +36,6 @@ dependencies = [
   "datasets",
   "sacrebleu",
   "unbabel-comet",
-  "altair>=5",
-  "pandas",
-  "scipy",
-  "nbformat>=5.10.4",
-  "nbconvert>=7.17.0",
-  "playwright>=1.58.0",
 ]
 
 [project.optional-dependencies]
diff --git a/uv.lock b/uv.lock
index 8424a6e..a16f73c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -138,22 +138,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
-[[package]]
-name = "altair"
-version = "6.0.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "jinja2" },
-    { name = "jsonschema" },
-    { name = "narwhals" },
-    { name = "packaging" },
-    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/c0/184a89bd5feba14ff3c41cfaf1dd8a82c05f5ceedbc92145e17042eb08a4/altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4", size = 763834, upload-time = "2025-11-12T08:59:11.519Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/db/33/ef2f2409450ef6daa61459d5de5c08128e7d3edb773fefd0a324d1310238/altair-6.0.0-py3-none-any.whl", hash = "sha256:09ae95b53d5fe5b16987dccc785a7af8588f2dca50de1e7a156efa8a461515f8", size = 795410, upload-time = "2025-11-12T08:59:09.804Z" },
-]
-
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -185,19 +169,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
-[[package]]
-name = "beautifulsoup4"
-version = "4.14.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "soupsieve" },
-    { name = "typing-extensions" },
-]
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From c7351dd3c3bc309421648c7329ccbc3219588080 Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Fri, 17 Apr 2026 11:39:31 -0400
Subject: [PATCH 09/10] Fix ruff config per review feedback

- Change extend-exclude to notebooks/*.py (more precise)
- Remove per-file-ignores section for notebooks (redundant when excluded)
---
 pyproject.toml | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3a9d966..9194bfc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,7 +57,7 @@ target-version = "py312"
 # Configure src path so ruff import fixes can identify local imports
 src = [ "src" ]
 # Ignore notebooks — marimo notebooks are not linted
-extend-exclude = [ "notebooks/" ]
+extend-exclude = [ "notebooks/*.py" ]
 
 [tool.ruff.lint]
 # Include these rules in addition to ruff's defaults
@@ -75,10 +75,6 @@ extend-select = [
 # Can use to ignore specific rules within above selection
 ignore = []
 
-[tool.ruff.lint.per-file-ignores]
-# Notebooks use typographic Unicode in markdown strings intentionally
-"notebooks/*.py" = ["RUF001"]
-
 [tool.marimo.display]
 cell_output = "below"
 theme = "system"      # honor user preference, including in static html exports

From 5811462d98b9b2884eb720bca4f8a51a5fa018ca Mon Sep 17 00:00:00 2001
From: hao <97079365+tanhaow@users.noreply.github.com>
Date: Fri, 17 Apr 2026 12:54:45 -0400
Subject: [PATCH 10/10] Drop madlad from sentence corpus per review feedback

madlad was replaced by TranslateGemma in our working set of models.
---
 notebooks/mt_browser.py | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/notebooks/mt_browser.py b/notebooks/mt_browser.py
index 7a9e81a..77a656a 100644
--- a/notebooks/mt_browser.py
+++ b/notebooks/mt_browser.py
@@ -55,11 +55,10 @@ def _(pathlib):
 def _(DATA_DIR, pl):
     # Load full sentence translations and join with eval scores.
     # Uses the complete notion-sents corpus (all Notion concepts), not the annotation subset.
-    # Note: madlad does not have eval scores.
     _sents_meta = pl.concat(
         [
             pl.read_ndjson(DATA_DIR / f"notion-sents/mt-sents-{m}.jsonl")
-            for m in ["google_tllm", "hymt", "gemma", "madlad"]
+            for m in ["google_tllm", "hymt", "gemma"]
         ]
     )
     _sents_scores = pl.concat(
@@ -101,7 +100,9 @@ def _(DATA_DIR, pl):
 
 @app.cell
 def _(mo):
-    mo.md("## 1. Sentence translations")
+    mo.md("""
+    ## 1. Sentence translations
+    """)
     return
 
 
@@ -110,11 +111,14 @@ def _(pl, sents_df):
     sents_df.filter(pl.col("src_lang") != "en").select(
         ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
     )
+    return
 
 
 @app.cell
 def _(mo):
-    mo.md("## 2. Sentence backtranslations")
+    mo.md("""
+    ## 2. Sentence backtranslations
+    """)
     return
 
 
@@ -123,11 +127,14 @@ def _(pl, sents_df):
     sents_df.filter(pl.col("src_lang") == "en").select(
         ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
     )
+    return
 
 
 @app.cell
 def _(mo):
-    mo.md("## 3. Paragraph translations")
+    mo.md("""
+    ## 3. Paragraph translations
+    """)
     return
 
 
@@ -136,11 +143,14 @@ def _(pars_df, pl):
     pars_df.filter(pl.col("src_lang") != "en").select(
         ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
     )
+    return
 
 
 @app.cell
 def _(mo):
-    mo.md("## 4. Paragraph backtranslations")
+    mo.md("""
+    ## 4. Paragraph backtranslations
+    """)
     return
 
 
@@ -149,6 +159,7 @@ def _(pars_df, pl):
     pars_df.filter(pl.col("src_lang") == "en").select(
         ["pair_id", "model", "src_lang", "tr_lang", "chrf", "comet", "cometkiwi", "src_text", "ref_text", "tr_text"]
     )
+    return
 
 
 if __name__ == "__main__":