Upload from GitHub Actions: Evaluate Google Translate

.gitignore

@@ -3,6 +3,7 @@ fleurs
 spbleu
 .cache
 .env
+*_credentials.json
 
 # Python-generated files
 __pycache__/

evals/main.py

@@ -1,47 +1,32 @@
 import asyncio
 from time import time
 
-t0 = time()
-
 import pandas as pd
 from languages import languages
-
-print(f"loaded languages in {time() - t0:.2f}s")
-t0 = time()
-
 from models import models
-
-print(f"loaded models in {time() - t0:.2f}s")
-t0 = time()
-
 from tasks import tasks
-
-print(f"loaded tasks in {time() - t0:.2f}s")
-t0 = time()
-
 from tqdm.asyncio import tqdm_asyncio
 
 # ===== config =====
 
 n_sentences = 10
-n_models = 35
-
 
 # ===== run evaluation and aggregate results =====
 
 
 async def evaluate():
-    # FIXME we should not need this for loop, but it helps
-    for n_languages in range(0, 200):
+    # FIXME we should not need this for-loop, but it helps
+    for n_languages in range(100, 101):
         print(f"running evaluations for {n_languages} languages")
         old_results = pd.read_json("results.json")
         old_models = pd.read_json("models.json")
         # get all combinations of model, language and task
         combis = [
             (model, lang.bcp_47, task_name)
-            for task_name, task in tasks.items()
+            for model in models["id"]
             for lang in languages.iloc[:n_languages].itertuples()
-            for model in models["id"].iloc[:n_models]
+            for task_name, task in tasks.items()
+            if task_name in models[models["id"] == model]["tasks"].iloc[0]
         ]
         # filter out combinations that have already been evaluated
         combis = pd.DataFrame(combis, columns=["model", "bcp_47", "task"])
@@ -69,7 +54,7 @@ async def evaluate():
             results = results.sort_values(by=["model", "bcp_47", "task", "metric"])
             results.to_json("results.json", **args)
         # save up-to-date info on models and languages
-        all_models = pd.concat([old_models, pd.DataFrame(models)])
+        all_models = pd.concat([pd.DataFrame(models), old_models])
         all_models = all_models.drop_duplicates(subset=["id"]).sort_values(by=["id"])
         all_models.to_json("models.json", **args)
         pd.DataFrame(languages).to_json("languages.json", **args)

evals/models.py

@@ -8,8 +8,10 @@ import pandas as pd
 from aiolimiter import AsyncLimiter
 from dotenv import load_dotenv
 from elevenlabs import AsyncElevenLabs
+from google.cloud import translate_v2 as translate
 from huggingface_hub import AsyncInferenceClient, HfApi
 from joblib.memory import Memory
+from langcodes import closest_supported_match
 from openai import AsyncOpenAI, PermissionDeniedError
 from requests import HTTPError, get
 
@@ -47,7 +49,9 @@ important_models = [
 ]
 
 blocklist = [
-    "microsoft/wizardlm-2-8x22b"  # temporarily rate-limited
+    "microsoft/wizardlm-2-8x22b",  # temporarily rate-limited
+    "google/gemini-2.5-pro",  # something wrong FIXME
+    "google/gemini-2.5-pro-preview",  # something wrong FIXME
 ]
 
 transcription_models = [
@@ -106,6 +110,23 @@ def get_current_popular_models(date: date):
     return [m for m in models if m]
 
 
+def get_translation_models():
+    return pd.DataFrame(
+        [
+            {
+                "id": "google/translate-v2",
+                "name": "Google Translate",
+                "provider_name": "Google",
+                "cost": 20.0,
+                "size": None,
+                "type": "closed-source",
+                "license": None,
+                "tasks": ["translation_from", "translation_to"],
+            }
+        ]
+    )
+
+
 load_dotenv()
 client = AsyncOpenAI(
     base_url="https://openrouter.ai/api/v1",
@@ -115,6 +136,7 @@ client = AsyncOpenAI(
 openrouter_rate_limit = AsyncLimiter(max_rate=20, time_period=1)
 elevenlabs_rate_limit = AsyncLimiter(max_rate=2, time_period=1)
 huggingface_rate_limit = AsyncLimiter(max_rate=5, time_period=1)
+google_rate_limit = AsyncLimiter(max_rate=10, time_period=1)
 
 
 @cache
@@ -130,6 +152,25 @@ async def complete(**kwargs) -> str | None:
     return response.choices[0].message.content.strip()
 
 
+translate_client = translate.Client()
+supported_languages = [l["language"] for l in translate_client.get_languages()]
+
+
+@cache
+async def translate_google(text, source_language, target_language):
+    source_language = closest_supported_match(source_language, supported_languages)
+    target_language = closest_supported_match(target_language, supported_languages)
+    if source_language == target_language:
+        return text
+    if source_language is None or target_language is None:
+        return None
+    async with google_rate_limit:
+        response = translate_client.translate(
+            text, source_language=source_language, target_language=target_language
+        )
+    return response["translatedText"]
+
+
 @cache
 async def transcribe_elevenlabs(path, model):
     modelname = model.split("/")[-1]
@@ -239,6 +280,14 @@ def load_models(date: date):
         creation_date=creation_date_hf.combine_first(creation_date_or),
     )
     # models = models[models["cost"] <= 2.0].reset_index(drop=True)
+    models["tasks"] = [
+        ["translation_from", "translation_to", "classification", "mmlu", "mgsm"]
+    ] * len(models)
+    models = pd.concat([models, get_translation_models()])
+    models = models[ # temporary fix FIXME
+        (models["id"] != "google/gemini-2.5-pro")
+        & (models["id"] != "google/gemini-2.5-pro-preview")
+    ]
     return models
 
 

evals/tasks.py

@@ -1,7 +1,6 @@
 import random
 from functools import partial
 from textwrap import dedent
-
 import evaluate
 import pandas as pd
 import sentencepiece as spm
@@ -9,7 +8,7 @@ from datasets_.flores import flores_sentences
 from datasets_.mgsm import load_mgsm, parse_number
 from datasets_.mmlu import load_mmlu
 from languages import languages, script_name
-from models import complete, transcribe
+from models import complete, transcribe, translate_google
 
 bleu = evaluate.load("bleu")
 chrf = evaluate.load("chrf")
@@ -40,24 +39,31 @@ async def translate_and_evaluate(model, bcp_47, sentence_nr, mode="from"):
     original_sentence = flores_sentences(original_language)["text"][sentence_nr].strip()
     target_sentence = flores_sentences(target_language)["text"][sentence_nr].strip()
     script = script_name(target_language.flores_path.split("_")[1])
-    prediction = await complete(
-        model=model,
-        messages=[
-            {
-                "role": "user",
-                "content": f"Translate the following text to the {target_language.language_name} language; use the {script} script; reply only with the translation:\n\n{original_sentence}",
-            }
-        ],
-        temperature=0,
-        max_tokens=1024,
-    )
+    if model == "google/translate-v2":
+        prediction = await translate_google(
+            original_sentence, original_language.bcp_47, target_language.bcp_47
+        )
+    else:
+        prediction = await complete(
+            model=model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Translate the following text to the {target_language.language_name} language; use the {script} script; reply only with the translation:\n\n{original_sentence}",
+                }
+            ],
+            temperature=0,
+            max_tokens=1024,
+        )
     if prediction:
         bleu_score = bleu.compute(
             predictions=[prediction],
             references=[target_sentence],
             tokenizer=tokenizer.tokenize,
         )
-        chrf_score = chrf.compute(predictions=[prediction], references=[target_sentence])
+        chrf_score = chrf.compute(
+            predictions=[prediction], references=[target_sentence]
+        )
     else:
         bleu_score = {"bleu": 0}
         chrf_score = {"score": 0}

languages.json

@@ -7,7 +7,7 @@
     "family":"Indo-European",
     "flores_path":"eng_Latn",
     "fleurs_tag":"en_us",
-    "commonvoice_hours":2670.0,
+    "commonvoice_hours":2673.0,
     "commonvoice_locale":"en",
     "in_benchmark":true
   },
@@ -19,7 +19,7 @@
     "family":"Sino-Tibetan",
     "flores_path":"cmn_Hans",
     "fleurs_tag":"cmn_hans_cn",
-    "commonvoice_hours":422.0,
+    "commonvoice_hours":423.0,
     "commonvoice_locale":"zh-TW",
     "in_benchmark":true
   },
@@ -43,7 +43,7 @@
     "family":"Indo-European",
     "flores_path":"spa_Latn",
     "fleurs_tag":"es_419",
-    "commonvoice_hours":447.0,
+    "commonvoice_hours":448.0,
     "commonvoice_locale":"es",
     "in_benchmark":true
   },
@@ -79,7 +79,7 @@
     "family":"Indo-European",
     "flores_path":"fra_Latn",
     "fleurs_tag":"fr_fr",
-    "commonvoice_hours":1063.0,
+    "commonvoice_hours":1064.0,
     "commonvoice_locale":"fr",
     "in_benchmark":true
   },
@@ -103,7 +103,7 @@
     "family":"Indo-European",
     "flores_path":"por_Latn",
     "fleurs_tag":"pt_br",
-    "commonvoice_hours":179.0,
+    "commonvoice_hours":180.0,
     "commonvoice_locale":"pt",
     "in_benchmark":true
   },
@@ -127,7 +127,7 @@
     "family":"Indo-European",
     "flores_path":"rus_Cyrl",
     "fleurs_tag":"ru_ru",
-    "commonvoice_hours":244.0,
+    "commonvoice_hours":245.0,
     "commonvoice_locale":"ru",
     "in_benchmark":true
   },
@@ -163,7 +163,7 @@
     "family":"Indo-European",
     "flores_path":"deu_Latn",
     "fleurs_tag":"de_de",
-    "commonvoice_hours":1368.0,
+    "commonvoice_hours":1369.0,
     "commonvoice_locale":"de",
     "in_benchmark":true
   },
@@ -1183,7 +1183,7 @@
     "family":"Indo-European",
     "flores_path":"bel_Cyrl",
     "fleurs_tag":"be_by",
-    "commonvoice_hours":1808.0,
+    "commonvoice_hours":1809.0,
     "commonvoice_locale":"be",
     "in_benchmark":true
   },
@@ -1207,7 +1207,7 @@
     "family":"Indo-European",
     "flores_path":"tgk_Cyrl",
     "fleurs_tag":"tg_tj",
-    "commonvoice_hours":0.2,
+    "commonvoice_hours":0.4,
     "commonvoice_locale":"tg",
     "in_benchmark":true
   },
@@ -1291,7 +1291,7 @@
     "family":"Indo-European",
     "flores_path":"cat_Latn",
     "fleurs_tag":"ca_es",
-    "commonvoice_hours":2857.0,
+    "commonvoice_hours":2862.0,
     "commonvoice_locale":"ca",
     "in_benchmark":true
   },
@@ -1375,7 +1375,7 @@
     "family":"Turkic",
     "flores_path":"uig_Arab",
     "fleurs_tag":null,
-    "commonvoice_hours":379.0,
+    "commonvoice_hours":410.0,
     "commonvoice_locale":"ug",
     "in_benchmark":true
   },
@@ -1555,7 +1555,7 @@
     "family":"Indo-European",
     "flores_path":"slk_Latn",
     "fleurs_tag":"sk_sk",
-    "commonvoice_hours":49.0,
+    "commonvoice_hours":51.0,
     "commonvoice_locale":"sk",
     "in_benchmark":true
   },
@@ -1675,7 +1675,7 @@
     "family":"Tupian",
     "flores_path":"gug_Latn",
     "fleurs_tag":null,
-    "commonvoice_hours":3.8,
+    "commonvoice_hours":4.0,
     "commonvoice_locale":"gn",
     "in_benchmark":true
   },
@@ -1747,7 +1747,7 @@
     "family":"Indo-European",
     "flores_path":"nob_Latn",
     "fleurs_tag":"nb_no",
-    "commonvoice_hours":0.3,
+    "commonvoice_hours":0.4,
     "commonvoice_locale":"nb-NO",
     "in_benchmark":true
   },
@@ -2167,7 +2167,7 @@
     "family":"Indo-European",
     "flores_path":"glg_Latn",
     "fleurs_tag":"gl_es",
-    "commonvoice_hours":116.0,
+    "commonvoice_hours":117.0,
     "commonvoice_locale":"gl",
     "in_benchmark":true
   },
@@ -2827,8 +2827,8 @@
     "family":"Sino-Tibetan",
     "flores_path":"brx_Deva",
     "fleurs_tag":null,
-    "commonvoice_hours":null,
-    "commonvoice_locale":null,
+    "commonvoice_hours":0.0,
+    "commonvoice_locale":"brx",
     "in_benchmark":true
   },
   {
@@ -3019,7 +3019,7 @@
     "family":"Indo-European",
     "flores_path":"mkd_Cyrl",
     "fleurs_tag":"mk_mk",
-    "commonvoice_hours":19.0,
+    "commonvoice_hours":20.0,
     "commonvoice_locale":"mk",
     "in_benchmark":true
   },
@@ -3331,7 +3331,7 @@
     "family":"Indo-European",
     "flores_path":"gle_Latn",
     "fleurs_tag":"ga_ie",
-    "commonvoice_hours":7.0,
+    "commonvoice_hours":8.2,
     "commonvoice_locale":"ga-IE",
     "in_benchmark":true
   },
@@ -3535,7 +3535,7 @@
     "family":null,
     "flores_path":"eus_Latn",
     "fleurs_tag":null,
-    "commonvoice_hours":412.0,
+    "commonvoice_hours":438.0,
     "commonvoice_locale":"eu",
     "in_benchmark":true
   },
@@ -3559,7 +3559,7 @@
     "family":"Abkhaz-Adyge",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":69.0,
+    "commonvoice_hours":82.0,
     "commonvoice_locale":"kbd",
     "in_benchmark":false
   },
@@ -4411,7 +4411,7 @@
     "family":"Indo-European",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":1.0,
+    "commonvoice_hours":1.4,
     "commonvoice_locale":"os",
     "in_benchmark":false
   },
@@ -4615,7 +4615,7 @@
     "family":"Turkic",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":15.0,
+    "commonvoice_hours":16.0,
     "commonvoice_locale":"sah",
     "in_benchmark":false
   },
@@ -4651,7 +4651,7 @@
     "family":"Abkhaz-Adyge",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":23.0,
+    "commonvoice_hours":29.0,
     "commonvoice_locale":"ady",
     "in_benchmark":false
   },
@@ -7051,7 +7051,7 @@
     "family":"Indo-European",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":2.9,
+    "commonvoice_hours":3.0,
     "commonvoice_locale":"hsb",
     "in_benchmark":false
   },
@@ -7219,7 +7219,7 @@
     "family":"Atlantic-Congo",
     "flores_path":null,
     "fleurs_tag":null,
-    "commonvoice_hours":0.0,
+    "commonvoice_hours":11.0,
     "commonvoice_locale":"nmg",
     "in_benchmark":false
   },

models.json

@@ -8,7 +8,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1733356800000
+    "creation_date":1733356800000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"anthropic\/claude-3.5-sonnet",
@@ -19,7 +26,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1729555200000
+    "creation_date":1729555200000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"anthropic\/claude-3.7-sonnet",
@@ -30,7 +44,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1740355200000
+    "creation_date":1740355200000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"anthropic\/claude-sonnet-4",
@@ -41,7 +62,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1747872000000
+    "creation_date":1747872000000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"deepseek\/deepseek-chat",
@@ -52,7 +80,14 @@
     "size":684531386000.0,
     "type":"open-source",
     "license":"",
-    "creation_date":1735084800000
+    "creation_date":1735084800000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"deepseek\/deepseek-chat-v3-0324",
@@ -63,7 +98,14 @@
     "size":684531386000.0,
     "type":"open-source",
     "license":"Mit",
-    "creation_date":1742774400000
+    "creation_date":1742774400000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"deepseek\/deepseek-r1",
@@ -74,7 +116,14 @@
     "size":684531386000.0,
     "type":"open-source",
     "license":"Mit",
-    "creation_date":1737331200000
+    "creation_date":1737331200000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"deepseek\/deepseek-r1-0528",
@@ -85,7 +134,8 @@
     "size":684531386000.0,
     "type":"open-source",
     "license":"Mit",
-    "creation_date":1748390400000
+    "creation_date":1748390400000.0,
+    "tasks":null
   },
   {
     "id":"google\/gemini-2.0-flash-001",
@@ -96,7 +146,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1738713600000
+    "creation_date":1738713600000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-2.0-flash-lite-001",
@@ -107,7 +164,50 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1740441600000
+    "creation_date":1740441600000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
+  },
+  {
+    "id":"google\/gemini-2.5-flash",
+    "name":"Gemini 2.5 Flash",
+    "provider_name":"Google",
+    "cost":2.5,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1750118400000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
+  },
+  {
+    "id":"google\/gemini-2.5-flash-lite-preview-06-17",
+    "name":"Gemini 2.5 Flash Lite Preview 06-17",
+    "provider_name":"Google",
+    "cost":0.4,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1750118400000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-2.5-flash-preview",
@@ -118,7 +218,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1744848000000
+    "creation_date":1744848000000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-2.5-flash-preview-05-20",
@@ -129,7 +236,50 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1747699200000
+    "creation_date":1747699200000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
+  },
+  {
+    "id":"google\/gemini-2.5-pro",
+    "name":"Gemini 2.5 Pro",
+    "provider_name":"Google",
+    "cost":10.0,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1750118400000.0,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
+  },
+  {
+    "id":"google\/gemini-2.5-pro-preview",
+    "name":"Gemini 2.5 Pro Preview 06-05",
+    "provider_name":"Google",
+    "cost":10.0,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1749081600000.0,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-2.5-pro-preview-05-06",
@@ -140,7 +290,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1746576000000
+    "creation_date":1746576000000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-flash-1.5",
@@ -151,7 +308,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1715644800000
+    "creation_date":1715644800000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
   {
     "id":"google\/gemini-flash-1.5-8b",
@@ -162,7 +326,14 @@
     "size":null,
     "type":"closed-source",
     "license":null,
-    "creation_date":1727913600000
+    "creation_date":1727913600000,
+    "tasks":[
+      "translation_from",
+      "translation_to",
+      "classification",
+      "mmlu",
+      "mgsm"
+    ]
   },
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pyproject.toml

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