Upload from GitHub Actions: ran full evaluation locally

.gitattributes

@@ -1,2 +1,3 @@
 evals/data_flow_architecture.png filter=lfs diff=lfs merge=lfs -text
 results.json filter=lfs diff=lfs merge=lfs -text
+errors.log filter=lfs diff=lfs merge=lfs -text

errors.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bbd7bf12d062c48650b24bb1c138d565f8129cb9d4ecabeedc3d165edcd57cb
+size 11143443

evals/main.py

@@ -14,7 +14,7 @@ async def evaluate():
     single_model = os.environ.get("SINGLE_MODEL")            # Optional: run only one specific model
     test_mode = os.environ.get("TEST", "").lower() in ("1", "true", "yes")  # Optional: skip results loading/saving
     
-    # Keep original DataFrames for saving metadata
+    # Keep original DataFrames for saving metadata - distinction added for single model test runs.
     original_models_df = pd.DataFrame(models)
     original_languages_df = pd.DataFrame(languages)
     
@@ -59,8 +59,11 @@ async def evaluate():
     
     # Filter out already evaluated combinations
     combis = pd.DataFrame(combis, columns=["model", "bcp_47", "task"])
-    combis = combis.merge(old_results, on=["model", "bcp_47", "task"], how="left")
-    combis = combis[combis["metric"].isna()][["model", "bcp_47", "task"]]
+    if not old_results.empty:
+        completed = set(old_results[["model", "bcp_47", "task"]].apply(tuple, axis=1))
+        # set + combis is faster than merge (locally it made a difference for me when loading all data/tasks into memory)
+        mask = ~combis.apply(lambda row: (row["model"], row["bcp_47"], row["task"]) in completed, axis=1)
+        combis = combis[mask]
     
     # Create all evaluation tasks
     all_tasks = []
@@ -70,64 +73,101 @@ async def evaluate():
     
     print(f"Running {len(all_tasks)} evaluation tasks...")
     
-    # Run all tasks with simple asyncio.gather, but stop on first error
+    # For single model runs, we stop immediately on first API error to inspect.
+    # For full evaluations, we continue despite errors to get maximum coverage.
+    stop_on_error = single_model is not None
+    
+    # Process tasks in batches to avoid memory issues (for full evaluation locally that helped a lot)
+    batch_size = 1000
+    all_results = []
+    
     try:
-        results = await asyncio.gather(
-            *[task_func(model, bcp_47, sentence_nr) for task_func, model, bcp_47, sentence_nr in all_tasks],
-            return_exceptions=False  # This will raise on first exception
-        )
+        for i in range(0, len(all_tasks), batch_size):
+            batch = all_tasks[i:i + batch_size]
+            batch_results = await asyncio.gather(
+                *[task_func(model, bcp_47, sentence_nr) for task_func, model, bcp_47, sentence_nr in batch],
+                return_exceptions=not stop_on_error
+            )
+            all_results.extend(batch_results)
+        
+        results = all_results
         
-        # Process results - no exceptions should reach here
+        # Process results and logging API errors separately to understand what are the main issues.
         valid_results = []
-        for r in results:
-            if isinstance(r, list):
+        errors = []
+        
+        for i, r in enumerate(results):
+            if isinstance(r, Exception):
+                if i < len(all_tasks):
+                    task_info = all_tasks[i]
+                    errors.append(f"{task_info[1]},{task_info[2]},{str(r)}")
+            elif isinstance(r, list):
                 valid_results.extend(r)
-            else:
+            elif r is not None:
                 valid_results.append(r)
-
-        print(f"Completed: {len(valid_results)} valid results")
         
+        # log errors and store 
+        if errors:
+            with open("errors.log", "w") as f:
+                f.write("model,task,error\n")
+                for error in errors:
+                    f.write(error + "\n")
+        
+        # Track model completion (TO BE DELETED - was for local run only)
+        if valid_results:
+            completed_models = set()
+            for result in valid_results:
+                if isinstance(result, dict) and "model" in result:
+                    model = result["model"]
+                    if model not in completed_models:
+                        completed_models.add(model)
+                        print(f"Completed: {model}")
+
+        print(f"Completed: {len(valid_results)} valid results, {len(errors)} errors")
+    
+    # this is for local single model runs - for testing and development
     except Exception as e:
         print(f"EVALUATION STOPPED - API Error occurred:")
         print(f"Error type: {type(e).__name__}")
         print(f"Error message: {str(e)}")
         return pd.DataFrame()
     
-        # Save results (skip in test mode)
-        if valid_results:
-            results_df = pd.DataFrame(valid_results)
-            
-            # Aggregate results
-            results_df = (
-                results_df.groupby(["model", "bcp_47", "task", "metric", "origin"])
-                .agg({"score": "mean"})
-                .reset_index()
-            )
+    # Save results (skipped in test mode as we do not want to overwrite existing results)
+    if valid_results:
+        results_df = pd.DataFrame(valid_results)
+        
+        # Aggregate results
+        results_df = (
+            results_df.groupby(["model", "bcp_47", "task", "metric", "origin"])
+            .agg({"score": "mean"})
+            .reset_index()
+        )
+        
+        if not test_mode:
+            args = dict(orient="records", indent=2, force_ascii=False)
             
-            if not test_mode:
-                args = dict(orient="records", indent=2, force_ascii=False)
-                
-                # Merge with existing results
-                if not old_results.empty:
-                    results_df = pd.concat([old_results, results_df])
-                    results_df = results_df.drop_duplicates(subset=["model", "bcp_47", "task", "metric", "origin"])
-                
-                results_df = results_df.sort_values(by=["model", "bcp_47", "task", "metric"])
-                results_df.to_json("results.json", **args)
-                
-                # Save model and language info (always save complete metadata, not filtered)
-                original_models_df.to_json("models.json", **args)
-                original_languages_df.to_json("languages.json", **args)
-            else:
-                print("TEST MODE: Skipping results saving")
+            # Merge with existing results
+            if not old_results.empty:
+                results_df = pd.concat([old_results, results_df])
+                results_df = results_df.drop_duplicates(subset=["model", "bcp_47", "task", "metric", "origin"])
             
-            elapsed = time.time() - start_time
-            print(f"Evaluation completed in {str(timedelta(seconds=int(elapsed)))}")
+            results_df = results_df.sort_values(by=["model", "bcp_47", "task", "metric"])
+            results_df.to_json("results.json", **args)
             
-            return results_df
+            # Save model and language info (always save complete metadata, not filtered)
+            original_models_df.to_json("models.json", **args)
+            original_languages_df.to_json("languages.json", **args)
+        else:
+            print("TEST MODE: Skipping results saving")
+        
+        elapsed = time.time() - start_time
+        print(f"Evaluation completed in {str(timedelta(seconds=int(elapsed)))}")
+        
+        return results_df
     
     return pd.DataFrame()
 
 
+
 if __name__ == "__main__":
     results = asyncio.run(evaluate())

languages.json

@@ -979,7 +979,7 @@
     "family":"Turkic",
     "flores_path":"kaz_Cyrl",
     "fleurs_tag":"kk_kz",
-    "commonvoice_hours":2.2,
+    "commonvoice_hours":2.3,
     "commonvoice_locale":"kk",
     "in_benchmark":true
   },
@@ -2167,7 +2167,7 @@
     "family":"Indo-European",
     "flores_path":"glg_Latn",
     "fleurs_tag":"gl_es",
-    "commonvoice_hours":162.0,
+    "commonvoice_hours":163.0,
     "commonvoice_locale":"gl",
     "in_benchmark":true
   },
@@ -3679,7 +3679,7 @@
     "family":"Indo-European",
     "flores_path":"ydd_Hebr",
     "fleurs_tag":null,
-    "commonvoice_hours":1.7,
+    "commonvoice_hours":1.8,
     "commonvoice_locale":"yi",
     "in_benchmark":true
   },

models.json

@@ -1,15 +1,15 @@
 [
   {
-    "id": "amazon/nova-micro-v1",
-    "name": "Nova Micro 1.0",
-    "provider_name": "Amazon",
-    "cost": 0.14,
-    "hf_id": null,
-    "size": null,
-    "type": "closed-source",
-    "license": null,
-    "creation_date": 1733356800000,
-    "tasks": [
+    "id":"ai21\/jamba-large-1.7",
+    "name":"Jamba Large 1.7",
+    "provider_name":"AI21",
+    "cost":8.0,
+    "hf_id":"ai21labs\/AI21-Jamba-Large-1.7",
+    "size":398555145696.0,
+    "type":"open-source",
+    "license":"Other",
+    "creation_date":1751414400000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -20,16 +20,16 @@
     ]
   },
   {
-    "id": "anthracite-org/magnum-v4-72b",
-    "name": "Magnum v4 72B",
-    "provider_name": "Magnum v4 72B",
-    "cost": 3.0,
-    "hf_id": "anthracite-org/magnum-v4-72b",
-    "size": 72706203648.0,
-    "type": "open-source",
-    "license": "Apache 2.0",
-    "creation_date": 1726790400000,
-    "tasks": [
+    "id":"amazon\/nova-micro-v1",
+    "name":"Nova Micro 1.0",
+    "provider_name":"Amazon",
+    "cost":0.14,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1733356800000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -40,16 +40,16 @@
     ]
   },
   {
-    "id": "anthropic/claude-sonnet-4",
-    "name": "Claude Sonnet 4",
-    "provider_name": "Anthropic",
-    "cost": 15.0,
-    "hf_id": null,
-    "size": null,
-    "type": "closed-source",
-    "license": null,
-    "creation_date": 1747872000000,
-    "tasks": [
+    "id":"anthracite-org\/magnum-v4-72b",
+    "name":"Magnum v4 72B",
+    "provider_name":"Magnum v4 72B",
+    "cost":5.0,
+    "hf_id":"anthracite-org\/magnum-v4-72b",
+    "size":72706203648.0,
+    "type":"open-source",
+    "license":"Apache 2.0",
+    "creation_date":1726790400000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -60,16 +60,16 @@
     ]
   },
   {
-    "id": "deepseek/deepseek-chat",
-    "name": "DeepSeek V3",
-    "provider_name": "DeepSeek",
-    "cost": 0.72,
-    "hf_id": "deepseek-ai/DeepSeek-V3",
-    "size": 684531386000.0,
-    "type": "open-source",
-    "license": "",
-    "creation_date": 1735084800000,
-    "tasks": [
+    "id":"anthropic\/claude-3.7-sonnet",
+    "name":"Claude 3.7 Sonnet",
+    "provider_name":"Anthropic",
+    "cost":15.0,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1740355200000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -80,16 +80,16 @@
     ]
   },
   {
-    "id": "deepseek/deepseek-chat-v3-0324",
-    "name": "DeepSeek V3 0324",
-    "provider_name": "DeepSeek",
-    "cost": 0.0,
-    "hf_id": "deepseek-ai/DeepSeek-V3-0324",
-    "size": 684531386000.0,
-    "type": "open-source",
-    "license": "Mit",
-    "creation_date": 1742774400000,
-    "tasks": [
+    "id":"anthropic\/claude-sonnet-4",
+    "name":"Claude Sonnet 4",
+    "provider_name":"Anthropic",
+    "cost":15.0,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1747872000000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -100,16 +100,16 @@
     ]
   },
   {
-    "id": "deepseek/deepseek-r1-0528",
-    "name": "R1 0528",
-    "provider_name": "DeepSeek",
-    "cost": 0.0,
-    "hf_id": "deepseek-ai/DeepSeek-R1-0528",
-    "size": 684531386000.0,
-    "type": "open-source",
-    "license": "Mit",
-    "creation_date": 1748390400000,
-    "tasks": [
+    "id":"arcee-ai\/virtuoso-large",
+    "name":"Virtuoso Large",
+    "provider_name":"Arcee AI",
+    "cost":1.2,
+    "hf_id":"arcee-ai\/Virtuoso-Large",
+    "size":72706203648.0,
+    "type":"open-source",
+    "license":"Other",
+    "creation_date":1749427200000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -120,16 +120,16 @@
     ]
   },
   {
-    "id": "google/gemini-2.0-flash-lite-001",
-    "name": "Gemini 2.0 Flash Lite",
-    "provider_name": "Google",
-    "cost": 0.3,
-    "hf_id": null,
-    "size": null,
-    "type": "closed-source",
-    "license": null,
-    "creation_date": 1740441600000,
-    "tasks": [
+    "id":"arliai\/qwq-32b-arliai-rpr-v1",
+    "name":"QwQ 32B RpR v1",
+    "provider_name":"ArliAI",
+    "cost":0.0,
+    "hf_id":"ArliAI\/QwQ-32B-ArliAI-RpR-v1",
+    "size":32763876352.0,
+    "type":"open-source",
+    "license":"Apache 2.0",
+    "creation_date":1743984000000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -140,16 +140,16 @@
     ]
   },
   {
-    "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": [
+    "id":"baidu\/ernie-4.5-300b-a47b",
+    "name":"ERNIE 4.5 300B A47B ",
+    "provider_name":"Baidu",
+    "cost":1.1,
+    "hf_id":"baidu\/ERNIE-4.5-300B-A47B-PT",
+    "size":300474051776.0,
+    "type":"open-source",
+    "license":"Apache 2.0",
+    "creation_date":1751068800000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -160,16 +160,16 @@
     ]
   },
   {
-    "id": "google/gemma-2-9b-it",
-    "name": "Gemma 2 9B",
-    "provider_name": "Google",
-    "cost": 0.0,
-    "hf_id": "google/gemma-2-9b-it",
-    "size": 9241705984.0,
-    "type": "open-source",
-    "license": "Gemma",
-    "creation_date": 1719187200000,
-    "tasks": [
+    "id":"deepseek\/deepseek-chat",
+    "name":"DeepSeek V3",
+    "provider_name":"DeepSeek",
+    "cost":0.8,
+    "hf_id":"deepseek-ai\/DeepSeek-V3",
+    "size":684531386000.0,
+    "type":"open-source",
+    "license":"",
+    "creation_date":1735084800000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -180,16 +180,16 @@
     ]
   },
   {
-    "id": "google/gemma-3-27b-it",
-    "name": "Gemma 3 27B",
-    "provider_name": "Google",
-    "cost": 0.0,
-    "hf_id": "google/gemma-3-27b-it",
-    "size": 27432406640.0,
-    "type": "open-source",
-    "license": "Gemma",
-    "creation_date": 1740787200000,
-    "tasks": [
+    "id":"deepseek\/deepseek-chat-v3-0324",
+    "name":"DeepSeek V3 0324",
+    "provider_name":"DeepSeek",
+    "cost":0.0,
+    "hf_id":"deepseek-ai\/DeepSeek-V3-0324",
+    "size":684531386000.0,
+    "type":"open-source",
+    "license":"Mit",
+    "creation_date":1742774400000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -200,16 +200,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-3-70b-instruct",
-    "name": "Llama 3 70B Instruct",
-    "provider_name": "Meta",
-    "cost": 0.4,
-    "hf_id": "meta-llama/Meta-Llama-3-70B-Instruct",
-    "size": 70553706496.0,
-    "type": "open-source",
-    "license": "Llama3",
-    "creation_date": 1713312000000,
-    "tasks": [
+    "id":"deepseek\/deepseek-chat-v3.1",
+    "name":"DeepSeek V3.1",
+    "provider_name":"DeepSeek",
+    "cost":0.0,
+    "hf_id":"deepseek-ai\/DeepSeek-V3.1",
+    "size":684531386000.0,
+    "type":"open-source",
+    "license":"Mit",
+    "creation_date":1755734400000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -220,16 +220,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-3.1-70b-instruct",
-    "name": "Llama 3.1 70B Instruct",
-    "provider_name": "Meta",
-    "cost": 0.28,
-    "hf_id": "meta-llama/Llama-3.1-70B-Instruct",
-    "size": 70553706496.0,
-    "type": "open-source",
-    "license": "Llama3.1",
-    "creation_date": 1721088000000,
-    "tasks": [
+    "id":"eleutherai\/llemma_7b",
+    "name":"Llemma 7b",
+    "provider_name":"EleutherAI",
+    "cost":1.2,
+    "hf_id":"EleutherAI\/llemma_7b",
+    "size":null,
+    "type":"open-source",
+    "license":"Llama2",
+    "creation_date":1694476800000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -240,16 +240,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-3.2-3b-instruct",
-    "name": "Llama 3.2 3B Instruct",
-    "provider_name": "Meta",
-    "cost": 0.0,
-    "hf_id": "meta-llama/Llama-3.2-3B-Instruct",
-    "size": 3212749824.0,
-    "type": "open-source",
-    "license": "Llama3.2",
-    "creation_date": 1726617600000,
-    "tasks": [
+    "id":"google\/gemini-2.0-flash-001",
+    "name":"Gemini 2.0 Flash",
+    "provider_name":"Google",
+    "cost":0.4,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1738713600000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -260,16 +260,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-3.3-70b-instruct",
-    "name": "Llama 3.3 70B Instruct",
-    "provider_name": "Meta",
-    "cost": 0.0,
-    "hf_id": "meta-llama/Llama-3.3-70B-Instruct",
-    "size": 70553706496.0,
-    "type": "open-source",
-    "license": "Llama3.3",
-    "creation_date": 1732579200000,
-    "tasks": [
+    "id":"google\/gemini-2.0-flash-lite-001",
+    "name":"Gemini 2.0 Flash Lite",
+    "provider_name":"Google",
+    "cost":0.3,
+    "hf_id":null,
+    "size":null,
+    "type":"closed-source",
+    "license":null,
+    "creation_date":1740441600000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -280,16 +280,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-4-maverick",
-    "name": "Llama 4 Maverick",
-    "provider_name": "Meta",
-    "cost": 0.6,
-    "hf_id": "meta-llama/Llama-4-Maverick-17B-128E-Instruct",
-    "size": 401583781376.0,
-    "type": "open-source",
-    "license": "Other",
-    "creation_date": 1743465600000,
-    "tasks": [
+    "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",
@@ -300,16 +300,16 @@
     ]
   },
   {
-    "id": "meta-llama/llama-guard-4-12b",
-    "name": "Llama Guard 4 12B",
-    "provider_name": "Meta",
-    "cost": 0.18,
-    "hf_id": "meta-llama/Llama-Guard-4-12B",
-    "size": 12001097216.0,
-    "type": "open-source",
-    "license": "Other",
-    "creation_date": 1745366400000,
-    "tasks": [
+    "id":"google\/gemma-2-9b-it",
+    "name":"Gemma 2 9B",
+    "provider_name":"Google",
+    "cost":0.0,
+    "hf_id":"google\/gemma-2-9b-it",
+    "size":9241705984.0,
+    "type":"open-source",
+    "license":"Gemma",
+    "creation_date":1719187200000,
+    "tasks":[
       "translation_from",
       "translation_to",
       "classification",
@@ -320,16 +320,16 @@
     ]
   },
   {
-    "id": "microsoft/phi-3-medium-128k-instruct",
-    "name": "Phi-3 Medium 128K Instruct",
-    "provider_name": "Microsoft",
-    "cost": 1.0,
-    "hf_id": "microsoft/Phi-3-medium-128k-instruct",
-    "size": 13960238080.0,
-    "type": "open-source",
-    "license": "Mit",
-    "creation_date": 1715040000000,
-    "tasks": [
+    "id":"google\/gemma-3-12b-it",
+    "name":"Gemma 3 12B",
+    "provider_name":"Google",
+    "cost":0.0,
+    "hf_id":"google\/gemma-3-12b-it",
+    "size":12187325040.0,
+    "type":"open-source",
+    "license":"Gemma",
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