diff --git a/analysis/avg_agreement_final.py b/analysis/avg_agreement_final.py
index 2353eb2..4e48e04 100644
--- a/analysis/avg_agreement_final.py
+++ b/analysis/avg_agreement_final.py
@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
-import seaborn as sns
 import pandas as pd
+import seaborn as sns
 
 FONT_SIZES = {"small": 12, "medium": 16, "large": 18}
 COLORS = {"green": "#355145", "purple": "#d8a6e5", "orange": "#fe7759"}
diff --git a/analysis/compute_iaa.py b/analysis/compute_iaa.py
new file mode 100644
index 0000000..ccdb537
--- /dev/null
+++ b/analysis/compute_iaa.py
@@ -0,0 +1,177 @@
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from sklearn.metrics import cohen_kappa_score
+from datasets import load_dataset
+
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="sklearn")
+
+
+FONT_SIZES = {"small": 12, "medium": 16, "large": 18}
+COLORS = {
+    "green": "#355145",
+    "purple": "#d8a6e5",
+    "orange": "#fe7759",
+    "blue": "#4c6ee6",
+}
+
+PLOT_PARAMS = {
+    "font.family": "serif",
+    "font.serif": ["Times New Roman", "STIX"],
+    "font.size": FONT_SIZES.get("medium"),
+    "axes.titlesize": FONT_SIZES.get("large"),
+    "axes.labelsize": FONT_SIZES.get("large"),
+    "xtick.labelsize": FONT_SIZES.get("large"),
+    "ytick.labelsize": FONT_SIZES.get("large"),
+    "legend.fontsize": FONT_SIZES.get("medium"),
+    "figure.titlesize": FONT_SIZES.get("medium"),
+    "text.usetex": False,
+}
+
+LANG_STANDARDIZATION = {
+    "arb": "ar",
+    "ces": "cs",
+    "deu": "de",
+    "ell": "el",
+    "fra": "fr",
+    "heb": "he",
+    "hin": "hi",
+    "ind": "id",
+    "ita": "it",
+    "jpn": "jp",
+    "kor": "kr",
+    "nld": "nl",
+    "pes": "fa",
+    "pol": "pl",
+    "por": "pt",
+    "ron": "ro",
+    "rus": "ru",
+    "spa": "es",
+    "tur": "tr",
+    "ukr": "uk",
+    "vie": "vi",
+    "zho": "zh",
+}
+
+SUBSET_MAPPING = {
+    "Chat": [
+        "alpacaeval-easy",
+        "alpacaeval-length",
+        "alpacaeval-hard",
+        "mt-bench-easy",
+        "mt-bench-med",
+    ],
+    "Chat Hard": [
+        "mt-bench-hard",
+        "llmbar-natural",
+        "llmbar-adver-neighbor",
+        "llmbar-adver-GPTInst",
+        "llmbar-adver-GPTOut",
+        "llmbar-adver-manual",
+    ],
+    "Safety": [
+        "refusals-dangerous",
+        "refusals-offensive",
+        "xstest-should-refuse",
+        "xstest-should-respond",
+        "donotanswer",
+    ],
+    "Reasoning": [
+        "math-prm",
+        "hep-cpp",
+        "hep-go",
+        "hep-java",
+        "hep-js",
+        "hep-python",
+        "hep-rust",
+    ],
+}
+
+
+def find_key(d: dict[str, list[str]], value: str) -> str | None:
+    for key, values in d.items():
+        if value in values:
+            return key
+    return None
+
+
+plt.rcParams.update(PLOT_PARAMS)
+
+# annotations = Path("data/hin_Deva_histogram.csv")
+lang = "hin_Deva"
+lang = "ind_Latn"
+annotations = Path(f"plots/{lang}_histogram.csv")
+reference = Path("plots/eng_Latn_histogram.csv")
+
+annot_df = pd.read_csv(annotations).set_index("model").T
+ref_df = pd.read_csv(reference).set_index("model").T
+
+cohen_scores: dict[str, float] = {}
+for (idx, annot), (_, ref) in zip(annot_df.iterrows(), ref_df.iterrows()):
+    cohen_scores[idx] = cohen_kappa_score(annot.to_list(), ref.to_list(), labels=[0, 1, 2])
+
+
+df = pd.DataFrame([cohen_scores]).T.reset_index().rename(columns={0: "cohen", "index": "instance_id"}).dropna()
+sdf = load_dataset(
+    "aya-rm-multilingual/multilingual-reward-bench-gtranslate", "ind_Latn", split="filtered"
+).to_pandas()
+sdf = sdf[["prompt", "chosen", "rejected", "subset", "id"]].rename(columns={"id": "instance_id"})
+sdf["instance_id"] = sdf["instance_id"].apply(lambda x: str(x))
+combi = df.merge(sdf, on="instance_id").sort_values(by="cohen", ascending=False).reset_index(drop=True)
+combi["category"] = combi["subset"].apply(lambda x: find_key(SUBSET_MAPPING, x))
+
+# df_grouped = combi.groupby(["category", "cohen"]).count().groupby(level=0).apply(lambda x: x / x.sum()).reset_index()
+combi["count"] = 1
+
+# Bin the data and compute percentages
+
+
+fig, axs = plt.subplots(
+    2,
+    1,
+    figsize=(8, 6),
+    sharex=True,
+    gridspec_kw={"height_ratios": [5, 2]},
+)
+sns.histplot(
+    df["cohen"],
+    ax=axs[0],
+    stat="count",
+    fill=True,
+    color=COLORS.get("orange"),
+)
+
+axs[0].axvline(x=0, color=COLORS.get("green"), linestyle="--", linewidth=1)
+axs[0].axvline(x=0.60, color=COLORS.get("green"), linestyle="--", linewidth=1)
+
+sns.histplot(
+    data=combi,
+    x="cohen",
+    # weights="count",
+    hue="category",
+    multiple="fill",
+    ax=axs[1],
+    palette=[
+        COLORS.get("green"),
+        COLORS.get("purple"),
+        COLORS.get("orange"),
+        COLORS.get("blue"),
+    ],
+)
+
+lang_code = LANG_STANDARDIZATION[lang.split("_")[0]]
+axs[1].set_xlabel(f"Cohen's Kappa (Language: {lang_code})")
+axs[1].set_ylabel("Percentage")
+
+# annot_df["model_annotations"] = [i for i in annot_df.values]
+# annot_df["eng_reference"] = [i for i in ref_df.values]
+# annotations = annot_df[["model_annotations", "eng_reference"]].reset_index().rename(columns={"index": "instance_id"})
+# df = df.merge(annotations, how="left", on="instance_id")
+
+axs[0].set_axisbelow(True)
+axs[0].grid(True, color="gray", axis="y", alpha=0.2)
+plt.tight_layout()
+plt.savefig(f"plots/cohen_k_histogram_{lang}.svg", bbox_inches="tight")