David Pomerenke
commited on
Commit
·
d1a7111
1
Parent(s):
9dbdcb2
Basic language table
Browse files- evals/languages.py +15 -16
- evals/main.py +16 -2
- frontend/public/results.json +0 -0
- frontend/src/App.js +38 -28
- frontend/src/components/LanguageTable.js +197 -0
- frontend/src/components/ModelTable.js +6 -6
- results.json +0 -0
evals/languages.py
CHANGED
@@ -21,21 +21,6 @@ languages["language_name"] = languages["bcp_47"].apply(
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lambda x: Language.get(x).display_name()
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)
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-
# load script codes and names
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-
scripts = pd.read_csv("data/ScriptCodes.csv").rename(
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columns={"Code": "iso15924", "English Name": "script_name"}
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-
)
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-
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-
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-
def population(bcp_47):
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-
items = {
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re.sub(r"^[a-z]+-", "", lang): pop
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for lang, pop in LANGUAGE_SPEAKING_POPULATION.items()
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if re.match(rf"^{bcp_47}-[A-Z]{{2}}$", lang)
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}
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return items
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-
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-
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glottolog = pd.read_csv(
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"data/glottolog_languoid.csv/languoid.csv", na_values=[""], keep_default_na=False
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) # Min _Nan_ Chinese is not N/A!
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@@ -43,7 +28,6 @@ glottolog["bcp_47"] = glottolog["iso639P3code"].apply(
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lambda x: standardize_tag(x, macro=True) if not pd.isna(x) else None
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)
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-
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@cache
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def language_family(bcp_47):
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languoid = glottolog[glottolog["bcp_47"] == bcp_47].iloc[0]
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@@ -52,6 +36,21 @@ def language_family(bcp_47):
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family = glottolog[glottolog["id"] == languoid["family_id"]].iloc[0]
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return family["name"]
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def script_name(iso15924):
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return scripts[scripts["iso15924"] == iso15924]["script_name"].values[0]
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lambda x: Language.get(x).display_name()
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)
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glottolog = pd.read_csv(
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"data/glottolog_languoid.csv/languoid.csv", na_values=[""], keep_default_na=False
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) # Min _Nan_ Chinese is not N/A!
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lambda x: standardize_tag(x, macro=True) if not pd.isna(x) else None
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)
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@cache
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def language_family(bcp_47):
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languoid = glottolog[glottolog["bcp_47"] == bcp_47].iloc[0]
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family = glottolog[glottolog["id"] == languoid["family_id"]].iloc[0]
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return family["name"]
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+
languages["family"] = languages["bcp_47"].apply(language_family)
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+
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+
# load script codes and names
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+
scripts = pd.read_csv("data/ScriptCodes.csv").rename(
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columns={"Code": "iso15924", "English Name": "script_name"}
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)
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+
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+
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+
def population(bcp_47):
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items = {
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re.sub(r"^[a-z]+-", "", lang): pop
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+
for lang, pop in LANGUAGE_SPEAKING_POPULATION.items()
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if re.match(rf"^{bcp_47}-[A-Z]{{2}}$", lang)
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}
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return items
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def script_name(iso15924):
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return scripts[scripts["iso15924"] == iso15924]["script_name"].values[0]
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evals/main.py
CHANGED
@@ -95,6 +95,20 @@ def make_model_table(df):
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return df
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async def main():
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results = await evaluate()
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results, lang_results, model_results, task_results = aggregate(results)
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@@ -107,9 +121,9 @@ async def main():
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with open("results.json", "w") as f:
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json.dump(all_results, f, indent=2, ensure_ascii=False)
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-
model_table = make_model_table(model_results)
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all_tables = {
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-
"model_table": serialize(
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}
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with open("frontend/public/results.json", "w") as f:
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json.dump(all_tables, f, indent=2, ensure_ascii=False)
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return df
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+
def make_language_table(df):
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df["task_metric"] = df["task"] + "_" + df["metric"]
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df = df.drop(columns=["task", "metric"])
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task_metrics = df["task_metric"].unique()
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df = df.pivot(index="bcp_47", columns="task_metric", values="score").fillna(0).reset_index()
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df["average"] = df[task_metrics].mean(axis=1)
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for row in [*task_metrics, "average"]:
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df[row] = df[row].round(2)
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df = pd.merge(languages, df, on="bcp_47", how="outer")
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df = df.sort_values(by="average", ascending=False)
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df = df[["language_name", "speakers", "family", "average", "in_benchmark", *task_metrics]]
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return df
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+
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async def main():
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results = await evaluate()
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results, lang_results, model_results, task_results = aggregate(results)
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with open("results.json", "w") as f:
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json.dump(all_results, f, indent=2, ensure_ascii=False)
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all_tables = {
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"model_table": serialize(make_model_table(model_results)),
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"language_table": serialize(make_language_table(lang_results)),
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}
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with open("frontend/public/results.json", "w") as f:
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json.dump(all_tables, f, indent=2, ensure_ascii=False)
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frontend/public/results.json
CHANGED
The diff for this file is too large to render.
See raw diff
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frontend/src/App.js
CHANGED
@@ -1,53 +1,63 @@
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-
import './App.css'
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import { useState, useEffect } from 'react'
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import { PrimeReactProvider } from 'primereact/api'
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import
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import ModelTable from './components/ModelTable'
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-
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-
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-
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const [
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const [loading, setLoading] = useState(true);
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const [error, setError] = useState(null);
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useEffect(() => {
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fetch('/results.json')
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.then(response => {
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if (!response.ok) {
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throw new Error('Network response was not ok')
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}
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return response.json()
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})
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.then(jsonData => {
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setData(jsonData)
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setLoading(false)
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})
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.catch(err => {
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setError(err.message)
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setLoading(false)
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})
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}, [])
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return (
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-
<div className=
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<header className=
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<div className=
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-
<span
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</div>
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<h1>Global AI Language Monitor</h1>
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<p>Tracking language proficiency of AI models for every language</p>
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-
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<div className=
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<PrimeReactProvider>
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{loading && <p>...</p>}
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{error && <p>Error: {error}</p>}
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-
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</PrimeReactProvider>
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</div>
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</header>
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</div>
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-
)
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}
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-
export default App
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import './App.css'
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import { useState, useEffect } from 'react'
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import { PrimeReactProvider } from 'primereact/api'
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+
import 'primereact/resources/themes/lara-light-cyan/theme.css'
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import ModelTable from './components/ModelTable'
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import LanguageTable from './components/LanguageTable'
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function App () {
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const [data, setData] = useState(null)
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const [loading, setLoading] = useState(true)
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const [error, setError] = useState(null)
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useEffect(() => {
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fetch('/results.json')
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.then(response => {
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if (!response.ok) {
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throw new Error('Network response was not ok')
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}
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return response.json()
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})
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.then(jsonData => {
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setData(jsonData)
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setLoading(false)
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})
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.catch(err => {
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setError(err.message)
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setLoading(false)
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})
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}, [])
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return (
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<div className='App'>
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+
<header className='App-header'>
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+
<div className='emoji-container'>
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<span
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role='img'
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aria-label='Hugging Face Emoji'
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className='header-emoji'
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>
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🌍
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</span>
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</div>
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<h1>Global AI Language Monitor</h1>
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<p>Tracking language proficiency of AI models for every language</p>
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+
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<div className='data-container' style={{ width: '100%' }}>
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<PrimeReactProvider>
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{loading && <p>...</p>}
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{error && <p>Error: {error}</p>}
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+
{data && (
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<div style={{ display: 'flex', flexDirection: 'row', gap: '2rem' }}>
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<ModelTable data={data} />
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<LanguageTable data={data} />
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</div>
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)}
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</PrimeReactProvider>
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</div>
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</header>
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</div>
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+
)
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}
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+
export default App
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frontend/src/components/LanguageTable.js
ADDED
@@ -0,0 +1,197 @@
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1 |
+
import { DataTable } from 'primereact/datatable'
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import { Column } from 'primereact/column'
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3 |
+
import { FilterMatchMode } from 'primereact/api'
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4 |
+
import { MultiSelect } from 'primereact/multiselect'
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5 |
+
import { useState, useEffect } from 'react'
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6 |
+
import { Slider } from 'primereact/slider'
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7 |
+
import ScoreField from './ScoreField'
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8 |
+
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const LanguageTable = ({ data }) => {
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const [filters, setFilters] = useState({
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language_name: { value: null, matchMode: FilterMatchMode.CONTAINS },
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family: { value: null, matchMode: FilterMatchMode.IN },
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speakers: { value: null, matchMode: FilterMatchMode.BETWEEN },
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14 |
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})
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15 |
+
const table = data.language_table
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+
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17 |
+
const families = [...new Set(table.map(item => item.family))]
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18 |
+
const familyRowFilterTemplate = options => {
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19 |
+
return (
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20 |
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<MultiSelect
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21 |
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value={options.value}
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22 |
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options={families}
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23 |
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onChange={e => {
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24 |
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options.filterApplyCallback(e.value)
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25 |
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setFilters(prevFilters => ({
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...prevFilters,
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family: { value: e.value, matchMode: FilterMatchMode.IN }
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}))
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29 |
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}}
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30 |
+
placeholder='All families'
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/>
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+
)
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33 |
+
}
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34 |
+
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35 |
+
const formatPopulation = population => {
|
36 |
+
if (population === null) {
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37 |
+
return ''
|
38 |
+
} else if (population < 1000) {
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39 |
+
return population.toFixed(0) + ''
|
40 |
+
} else if (population < 1000 * 1000) {
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41 |
+
return (population / 1000).toFixed(1) + 'K'
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42 |
+
} else if (population < 1000 * 1000 * 1000) {
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43 |
+
return (population / 1000 / 1000).toFixed(1) + 'M'
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44 |
+
} else {
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45 |
+
return (population / 1000 / 1000 / 1000).toFixed(1) + 'B'
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46 |
+
}
|
47 |
+
}
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48 |
+
|
49 |
+
const SliderWithLabel = ({ value, onChange }) => {
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50 |
+
const p = 10
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51 |
+
const min = 2
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52 |
+
const max = 12
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53 |
+
const start = value === null ? min : Math.log(value[0]) / Math.log(p)
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54 |
+
const stop = value === null ? max : Math.log(value[1]) / Math.log(p)
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55 |
+
const [_value, _setValue] = useState([start, stop])
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56 |
+
useEffect(() => {
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57 |
+
const timer = setTimeout(() => {
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58 |
+
onChange({
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59 |
+
value:
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60 |
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_value[0] <= min + 0.1 && _value[1] >= max - 0.1
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61 |
+
? null
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62 |
+
: [p ** _value[0], p ** _value[1]]
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63 |
+
})
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64 |
+
}, 1000)
|
65 |
+
return () => clearTimeout(timer)
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66 |
+
}, [_value, onChange])
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67 |
+
return (
|
68 |
+
<div style={{ minWidth: '20rem' }}>
|
69 |
+
<div>{formatPopulation(p ** _value[0])}</div>
|
70 |
+
<div>{formatPopulation(p ** _value[1])}</div>
|
71 |
+
<Slider
|
72 |
+
value={_value}
|
73 |
+
onChange={e => _setValue(e.value)}
|
74 |
+
placeholder='All sizes'
|
75 |
+
min={min}
|
76 |
+
max={max}
|
77 |
+
step={0.01}
|
78 |
+
range
|
79 |
+
style={{ marginTop: '5rem' }}
|
80 |
+
/>
|
81 |
+
</div>
|
82 |
+
)
|
83 |
+
}
|
84 |
+
|
85 |
+
const speakerFilterTemplate = options => {
|
86 |
+
return (
|
87 |
+
<SliderWithLabel
|
88 |
+
value={options.value}
|
89 |
+
onChange={e => {
|
90 |
+
options.filterApplyCallback(e.value)
|
91 |
+
setFilters(prevFilters => ({
|
92 |
+
...prevFilters,
|
93 |
+
speakers: { value: e.value, matchMode: FilterMatchMode.BETWEEN }
|
94 |
+
}))
|
95 |
+
}}
|
96 |
+
/>
|
97 |
+
)
|
98 |
+
}
|
99 |
+
|
100 |
+
const speakerBodyTemplate = rowData => {
|
101 |
+
const populationStr = formatPopulation(rowData.speakers)
|
102 |
+
return <div>{populationStr}</div>
|
103 |
+
}
|
104 |
+
|
105 |
+
const languageBodyTemplate = rowData => {
|
106 |
+
return <div style={{ fontWeight: 'bold' }}>{rowData.language_name}</div>
|
107 |
+
}
|
108 |
+
|
109 |
+
const scoreBodyTemplate = (field, options = {}) => {
|
110 |
+
const { minScore = 0, maxScore = 1 } = options
|
111 |
+
|
112 |
+
return rowData => {
|
113 |
+
const score = rowData[field]
|
114 |
+
return ScoreField(score, minScore, maxScore)
|
115 |
+
}
|
116 |
+
}
|
117 |
+
|
118 |
+
return (
|
119 |
+
<DataTable
|
120 |
+
value={table}
|
121 |
+
header={<>Languages</>}
|
122 |
+
sortField='speakers'
|
123 |
+
removableSort
|
124 |
+
filters={filters}
|
125 |
+
filterDisplay='menu'
|
126 |
+
scrollable
|
127 |
+
scrollHeight='500px'
|
128 |
+
style={{ minWidth: '200px' }}
|
129 |
+
>
|
130 |
+
<Column
|
131 |
+
field='language_name'
|
132 |
+
header='Language'
|
133 |
+
body={languageBodyTemplate}
|
134 |
+
filter
|
135 |
+
showFilterMatchModes={false}
|
136 |
+
style={{ minWidth: '5rem' }}
|
137 |
+
frozen
|
138 |
+
/>
|
139 |
+
<Column
|
140 |
+
field='speakers'
|
141 |
+
header='Speakers'
|
142 |
+
body={speakerBodyTemplate}
|
143 |
+
filter
|
144 |
+
filterElement={speakerFilterTemplate}
|
145 |
+
showFilterMatchModes={false}
|
146 |
+
style={{ minWidth: '5rem' }}
|
147 |
+
/>
|
148 |
+
<Column
|
149 |
+
field='family'
|
150 |
+
header='Family'
|
151 |
+
filter
|
152 |
+
showFilterMatchModes={false}
|
153 |
+
filterElement={familyRowFilterTemplate}
|
154 |
+
style={{ minWidth: '10rem' }}
|
155 |
+
/>
|
156 |
+
<Column
|
157 |
+
field='average'
|
158 |
+
header='Average'
|
159 |
+
sortable
|
160 |
+
body={scoreBodyTemplate('average', { minScore: 0.4, maxScore: 0.8 })}
|
161 |
+
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
162 |
+
/>
|
163 |
+
<Column
|
164 |
+
field='translation_chrf'
|
165 |
+
header='Translation'
|
166 |
+
sortable
|
167 |
+
body={scoreBodyTemplate('translation_chrf', {
|
168 |
+
minScore: 0.4,
|
169 |
+
maxScore: 0.7
|
170 |
+
})}
|
171 |
+
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
172 |
+
/>
|
173 |
+
<Column
|
174 |
+
field='classification_accuracy'
|
175 |
+
header='Classification'
|
176 |
+
sortable
|
177 |
+
body={scoreBodyTemplate('classification_accuracy', {
|
178 |
+
minScore: 0.4,
|
179 |
+
maxScore: 1
|
180 |
+
})}
|
181 |
+
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
182 |
+
/>
|
183 |
+
<Column
|
184 |
+
field='language_modeling_chrf'
|
185 |
+
header='Language Modeling'
|
186 |
+
sortable
|
187 |
+
body={scoreBodyTemplate('language_modeling_chrf', {
|
188 |
+
minScore: 0.8,
|
189 |
+
maxScore: 1
|
190 |
+
})}
|
191 |
+
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
192 |
+
/>
|
193 |
+
</DataTable>
|
194 |
+
)
|
195 |
+
}
|
196 |
+
|
197 |
+
export default LanguageTable
|
frontend/src/components/ModelTable.js
CHANGED
@@ -124,7 +124,7 @@ const ModelTable = ({ data }) => {
|
|
124 |
}
|
125 |
|
126 |
const modelBodyTemplate = rowData => {
|
127 |
-
return <div style={{ fontWeight: 'bold' }}>{rowData.model}</div>
|
128 |
}
|
129 |
|
130 |
const scoreBodyTemplate = (field, options = {}) => {
|
@@ -162,7 +162,7 @@ const ModelTable = ({ data }) => {
|
|
162 |
header='Model'
|
163 |
filter
|
164 |
showFilterMatchModes={false}
|
165 |
-
style={{ minWidth: '
|
166 |
body={modelBodyTemplate}
|
167 |
frozen
|
168 |
/>
|
@@ -188,7 +188,7 @@ const ModelTable = ({ data }) => {
|
|
188 |
field='average'
|
189 |
header='Average'
|
190 |
sortable
|
191 |
-
body={scoreBodyTemplate('average', { minScore: 0.
|
192 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
193 |
/>
|
194 |
<Column
|
@@ -196,7 +196,7 @@ const ModelTable = ({ data }) => {
|
|
196 |
header='Translation'
|
197 |
sortable
|
198 |
body={scoreBodyTemplate('translation_chrf', {
|
199 |
-
minScore: 0.
|
200 |
maxScore: 0.7
|
201 |
})}
|
202 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
@@ -206,8 +206,8 @@ const ModelTable = ({ data }) => {
|
|
206 |
header='Classification'
|
207 |
sortable
|
208 |
body={scoreBodyTemplate('classification_accuracy', {
|
209 |
-
minScore: 0.
|
210 |
-
maxScore:
|
211 |
})}
|
212 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
213 |
/>
|
|
|
124 |
}
|
125 |
|
126 |
const modelBodyTemplate = rowData => {
|
127 |
+
return <div style={{ fontWeight: 'bold', height: '100%' }}>{rowData.model}</div>
|
128 |
}
|
129 |
|
130 |
const scoreBodyTemplate = (field, options = {}) => {
|
|
|
162 |
header='Model'
|
163 |
filter
|
164 |
showFilterMatchModes={false}
|
165 |
+
style={{ minWidth: '10rem' }}
|
166 |
body={modelBodyTemplate}
|
167 |
frozen
|
168 |
/>
|
|
|
188 |
field='average'
|
189 |
header='Average'
|
190 |
sortable
|
191 |
+
body={scoreBodyTemplate('average', { minScore: 0.3, maxScore: 0.6 })}
|
192 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
193 |
/>
|
194 |
<Column
|
|
|
196 |
header='Translation'
|
197 |
sortable
|
198 |
body={scoreBodyTemplate('translation_chrf', {
|
199 |
+
minScore: 0.3,
|
200 |
maxScore: 0.7
|
201 |
})}
|
202 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
|
|
206 |
header='Classification'
|
207 |
sortable
|
208 |
body={scoreBodyTemplate('classification_accuracy', {
|
209 |
+
minScore: 0.3,
|
210 |
+
maxScore: 0.8
|
211 |
})}
|
212 |
style={{ minWidth: '5rem', maxWidth: '10rem' }}
|
213 |
/>
|
results.json
CHANGED
The diff for this file is too large to render.
See raw diff
|
|