update results and text

results.txt

@@ -42,3 +42,48 @@
     "test_seed": 42
   }
 }
+
+{
+  "username": "theterryzhang",
+  "space_url": "https://huggingface.co/spaces/theterryzhang/submission-template",
+  "submission_timestamp": "2025-01-31T20:05:49.060404",
+  "model_description": "Mixture of expert classifier with DistilBERT Embeddings",
+  "accuracy": 0.64,
+  "energy_consumed_wh": 0.5265790008821295,
+  "emissions_gco2eq": 0.19437841190095229,
+  "emissions_data": {
+    "run_id": "9d207fd5-6169-46d5-9e34-f575d7276e67",
+    "duration": 10.614267765000022,
+    "emissions": 0.0001943784119009523,
+    "emissions_rate": 0.000018313007308559322,
+    "cpu_power": 105,
+    "gpu_power": 67.91747982828848,
+    "ram_power": 5.74672794342041,
+    "cpu_energy": 0.0003094673678833363,
+    "gpu_energy": 0.00020017571569600108,
+    "ram_energy": 0.000016935917302792142,
+    "energy_consumed": 0.0005265790008821295,
+    "country_name": "United States",
+    "country_iso_code": "USA",
+    "region": "virginia",
+    "cloud_provider": "",
+    "cloud_region": "",
+    "os": "Linux-5.10.230-223.885.amzn2.x86_64-x86_64-with-glibc2.36",
+    "python_version": "3.9.21",
+    "codecarbon_version": "2.8.3",
+    "cpu_count": 4,
+    "cpu_model": "Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz",
+    "gpu_count": 1,
+    "gpu_model": "1 x Tesla T4",
+    "ram_total_size": 15.324607849121094,
+    "tracking_mode": "machine",
+    "on_cloud": "N",
+    "pue": 1
+  },
+  "api_route": "/text",
+  "dataset_config": {
+    "dataset_name": "QuotaClimat/frugalaichallenge-text-train",
+    "test_size": 0.2,
+    "test_seed": 42
+  }
+}

tasks/text.py

@@ -22,13 +22,13 @@ router = APIRouter()
 DESCRIPTION = "Random Baseline"
 ROUTE = "/text"
 
-models_descriptions = {
+MODEL_DESCRIPTIONS = {
     "baseline": "random baseline", # Baseline
     "tfidf_xgb": "TF-IDF vectorizer and XGBoost classifier", # Submitted
     "bert_base_pruned": "Pruned BERT base model", # Submitted
-    'climate_bert_pruned': "Fine-tuned and pruned DistilRoBERTa pre-trained on climate texts", # Not working
-    "sbert_distilroberta": "Fine-tuned sentence transformer DistilRoBERTa",
-    "embedding_moe": "Mixture of expert classifier with DistilBERT Embeddings"
+    # 'climate_bert_pruned': "Fine-tuned and pruned DistilRoBERTa pre-trained on climate texts", # Not working
+    "sbert_distilroberta": "Fine-tuned sentence transformer DistilRoBERTa", # working, not submitted
+    "embedding_moe": "Mixture of expert classifier with DistilBERT Embeddings" # working, not submitted
 }
 
 
@@ -93,7 +93,6 @@ def bert_classifier(test_dataset: dict, model: str):
     else:
         raise(ValueError)
 
-    # Use CUDA if available 
     device, _, _ = get_backend()
 
     model = model.to(device)
@@ -119,7 +118,6 @@ def bert_classifier(test_dataset: dict, model: str):
 def moe_classifier(test_dataset: dict, model: str):
     print("Starting MoE run")
 
-    # Use CUDA if available 
     device, _, _ = get_backend()
 
     texts = test_dataset["quote"]
@@ -228,7 +226,7 @@ async def evaluate_text(request: TextEvaluationRequest,
         "username": username,
         "space_url": space_url,
         "submission_timestamp": datetime.now().isoformat(),
-        "model_description": models_descriptions[model],
+        "model_description": MODEL_DESCRIPTIONS[model],
         "accuracy": float(accuracy),
         "energy_consumed_wh": emissions_data.energy_consumed * 1000,
         "emissions_gco2eq": emissions_data.emissions * 1000,