correct average values

app.py

@@ -12,7 +12,7 @@ INTRODUCTION_TEXT = """
 
 results = [
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[XLMR-base](https://huggingface.co/FacebookAI/xlm-roberta-base)',
         'Model Size (Million Parameters)': 279,
         'Embedding Dimensions': 768,
@@ -23,7 +23,7 @@ results = [
         'Retrieval (3 datasets)': 5.57,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[XLMR-large](https://huggingface.co/FacebookAI/xlm-roberta-large)',
         'Model Size (Million Parameters)': 561,
         'Embedding Dimensions': 1024,
@@ -34,7 +34,7 @@ results = [
         'Retrieval (3 datasets)': 11.80,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[WangchanBERTa](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased)',
         'Model Size (Million Parameters)': 106,
         'Embedding Dimensions': 768,
@@ -45,7 +45,7 @@ results = [
         'Retrieval (3 datasets)': 19.49,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[PhayaThaiBERT](https://huggingface.co/clicknext/phayathaibert)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -56,7 +56,7 @@ results = [
         'Retrieval (3 datasets)': 56.31,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[MPNet-multilingual](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -67,7 +67,7 @@ results = [
         'Retrieval (3 datasets)': 64.13,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[DistilUSE-multilingual](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)',
         'Model Size (Million Parameters)': 135,
         'Embedding Dimensions': 512,
@@ -78,7 +78,7 @@ results = [
         'Retrieval (3 datasets)': 42.72,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[BGE-M3](https://huggingface.co/BAAI/bge-m3)',
         'Model Size (Million Parameters)': 570,
         'Embedding Dimensions': 1024,
@@ -89,7 +89,7 @@ results = [
         'Retrieval (3 datasets)': 91.42,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SimCSE-XLMR-base](https://huggingface.co/kornwtp/simcse-model-XLMR)',
         'Model Size (Million Parameters)': 279,
         'Embedding Dimensions': 768,
@@ -100,7 +100,7 @@ results = [
         'Retrieval (3 datasets)': 54.17,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SimCSE-WangchanBERTa](https://huggingface.co/kornwtp/simcse-model-wangchanberta)',
         'Model Size (Million Parameters)': 106,
         'Embedding Dimensions': 768,
@@ -111,7 +111,7 @@ results = [
         'Retrieval (3 datasets)': 51.05,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SimCSE-PhayaThaiBERT](https://huggingface.co/kornwtp/simcse-model-phayathaibert)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -122,7 +122,7 @@ results = [
         'Retrieval (3 datasets)': 66.05,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-XLMR-base](https://huggingface.co/kornwtp/SCT-model-XLMR)',
         'Model Size (Million Parameters)': 279,
         'Embedding Dimensions': 768,
@@ -133,7 +133,7 @@ results = [
         'Retrieval (3 datasets)': 54.90,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-WangchanBERTa](https://huggingface.co/kornwtp/SCT-model-wangchanberta)',
         'Model Size (Million Parameters)': 106,
         'Embedding Dimensions': 768,
@@ -144,7 +144,7 @@ results = [
         'Retrieval (3 datasets)': 63.83,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-PhayaThaiBERT](https://huggingface.co/kornwtp/SCT-model-phayathaibert)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -155,7 +155,7 @@ results = [
         'Retrieval (3 datasets)': 66.20,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-KD-XLMR-base](https://huggingface.co/kornwtp/SCT-KD-model-XLMR)',
         'Model Size (Million Parameters)': 279,
         'Embedding Dimensions': 768,
@@ -166,7 +166,7 @@ results = [
         'Retrieval (3 datasets)': 65.02,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-KD-WangchanBERTa](https://huggingface.co/kornwtp/SCT-KD-model-wangchanberta)',
         'Model Size (Million Parameters)': 106,
         'Embedding Dimensions': 768,
@@ -177,7 +177,7 @@ results = [
         'Retrieval (3 datasets)': 62.38,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[SCT-KD-PhayaThaiBERT](https://huggingface.co/kornwtp/SCT-KD-model-phayathaibert)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -188,7 +188,7 @@ results = [
         'Retrieval (3 datasets)': 67.94,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[ConGen-XLMR-base](https://huggingface.co/kornwtp/ConGen-model-XLMR)',
         'Model Size (Million Parameters)': 279,
         'Embedding Dimensions': 768,
@@ -199,7 +199,7 @@ results = [
         'Retrieval (3 datasets)': 68.03,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[ConGen-WangchanBERTa](https://huggingface.co/kornwtp/ConGen-model-wangchanberta)',
         'Model Size (Million Parameters)': 106,
         'Embedding Dimensions': 768,
@@ -210,7 +210,7 @@ results = [
         'Retrieval (3 datasets)': 67.66,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[ConGen-PhayaThaiBERT](https://huggingface.co/kornwtp/ConGen-model-phayathaibert)',
         'Model Size (Million Parameters)': 278,
         'Embedding Dimensions': 768,
@@ -221,7 +221,7 @@ results = [
         'Retrieval (3 datasets)': 68.04,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[E5-Mistral-7B-Instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)',
         'Model Size (Million Parameters)': 7110,
         'Embedding Dimensions': 4096,
@@ -232,7 +232,7 @@ results = [
         'Retrieval (3 datasets)': 86.80,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[gte-Qwen2-7B-Instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)',
         'Model Size (Million Parameters)': 7610,
         'Embedding Dimensions': 3584,
@@ -243,7 +243,7 @@ results = [
         'Retrieval (3 datasets)': 38.31,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[GritLM-7B](https://huggingface.co/GritLM/GritLM-7B)',
         'Model Size (Million Parameters)': 7240,
         'Embedding Dimensions': 4096,
@@ -254,7 +254,7 @@ results = [
         'Retrieval (3 datasets)': 22.79,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)',
         'Model Size (Million Parameters)': 8030,
         'Embedding Dimensions': 4096,
@@ -265,7 +265,7 @@ results = [
         'Retrieval (3 datasets)': 47.93,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[Llama3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)',
         'Model Size (Million Parameters)': 8030,
         'Embedding Dimensions': 4096,
@@ -276,7 +276,7 @@ results = [
         'Retrieval (3 datasets)': 50.38,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[Llama3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)',
         'Model Size (Million Parameters)': 8030,
         'Embedding Dimensions': 4096,
@@ -287,7 +287,7 @@ results = [
         'Retrieval (3 datasets)': 43.64,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)',
         'Model Size (Million Parameters)': 8030,
         'Embedding Dimensions': 4096,
@@ -298,7 +298,7 @@ results = [
         'Retrieval (3 datasets)': 43.63,
     },
     {
-        'T': '🟢',
+        'Type': '🟢',
         'Model Name': '[Typhoon-8B-Instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct)',
         'Model Size (Million Parameters)': 8030,
         'Embedding Dimensions': 4096,
@@ -309,7 +309,7 @@ results = [
         'Retrieval (3 datasets)': 52.65,
     },
     {
-        'T': '📦',
+        'Type': '📦',
         'Model Name': 'Cohere-embed-multilingual-v2.0',
         'Model Size (Million Parameters)': "N/A",
         'Embedding Dimensions': 768,
@@ -320,7 +320,7 @@ results = [
         'Retrieval (3 datasets)': 85.23,
     },
     {
-        'T': '📦',
+        'Type': '📦',
         'Model Name': 'Cohere-embed-multilingual-v3.0',
         'Model Size (Million Parameters)': "N/A",
         'Embedding Dimensions': 1024,
@@ -331,7 +331,7 @@ results = [
         'Retrieval (3 datasets)': 91.43,
     },
     {
-        'T': '📦',
+        'Type': '📦',
         'Model Name': 'Openai-text-embedding-3-large',
         'Model Size (Million Parameters)': "N/A",
         'Embedding Dimensions': 3072,
@@ -343,6 +343,16 @@ results = [
     },
 ]
 
+# Calculate average
+results = [
+    {
+        **result,
+        'Average (8 datasets)': round(sum(
+            result.get(key, 0) for key in ['STS Average (1 datasets)', 'Classification (3 datasets)', 'PairClassification (1 datasets)', 'Retrieval (3 datasets)']
+        ) / 4, 2),
+    }
+    for result in results
+]
 # Sort by average
 results = sorted(results, key=lambda x: x['Average (8 datasets)'], reverse=True)