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Update README.md

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README.md CHANGED
@@ -2384,7 +2384,7 @@ model-index:
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  - type: precision_at_10
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  value: 8.493
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  - type: precision_at_20
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- value: 5.0
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  - type: precision_at_100
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  value: 1.312
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  - type: precision_at_1000
@@ -4426,7 +4426,7 @@ model-index:
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  - type: recall_at_1000
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  value: 75.224
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  - type: precision_at_1
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- value: 72.0
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  - type: precision_at_3
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  value: 50.917
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  - type: precision_at_5
@@ -4440,7 +4440,7 @@ model-index:
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  - type: precision_at_1000
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  value: 2.0549999999999997
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  - type: mrr_at_1
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- value: 72.0
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  - type: mrr_at_3
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  value: 77.5417
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  - type: mrr_at_5
@@ -8225,7 +8225,7 @@ model-index:
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  type: mteb/trec-covid
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  metrics:
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  - type: ndcg_at_1
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- value: 86.0
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  - type: ndcg_at_3
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  value: 86.542
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  - type: ndcg_at_5
@@ -8267,11 +8267,11 @@ model-index:
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  - type: recall_at_1000
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  value: 54.290000000000006
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  - type: precision_at_1
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- value: 90.0
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  - type: precision_at_3
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- value: 92.0
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  - type: precision_at_5
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- value: 90.0
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  - type: precision_at_10
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  value: 88.6
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  - type: precision_at_20
@@ -8281,7 +8281,7 @@ model-index:
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  - type: precision_at_1000
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  value: 25.81
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  - type: mrr_at_1
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- value: 90.0
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  - type: mrr_at_3
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  value: 94.6667
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  - type: mrr_at_5
@@ -9036,16 +9036,13 @@ model-index:
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  type: PairClassification
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  pipeline_tag: sentence-similarity
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  tags:
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- - sentence-transformers
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  - feature-extraction
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  - sentence-similarity
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  - mteb
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  - arctic
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  - snowflake-arctic-embed
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- - transformers.js
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  - onnx
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  - teradata
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-
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  ---
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  # A Teradata Vantage compatible Embeddings Model
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@@ -9197,5 +9194,4 @@ print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(em
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  print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
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  ```
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- You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
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-
 
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  - type: precision_at_10
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  value: 8.493
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  - type: precision_at_20
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+ value: 5
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  - type: precision_at_100
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  value: 1.312
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  - type: precision_at_1000
 
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  - type: recall_at_1000
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  value: 75.224
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  - type: precision_at_1
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+ value: 72
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  - type: precision_at_3
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  value: 50.917
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  - type: precision_at_5
 
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  - type: precision_at_1000
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  value: 2.0549999999999997
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  - type: mrr_at_1
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+ value: 72
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  - type: mrr_at_3
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  value: 77.5417
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  - type: mrr_at_5
 
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  type: mteb/trec-covid
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  metrics:
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  - type: ndcg_at_1
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+ value: 86
8229
  - type: ndcg_at_3
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  value: 86.542
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  - type: ndcg_at_5
 
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  - type: recall_at_1000
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  value: 54.290000000000006
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  - type: precision_at_1
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+ value: 90
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  - type: precision_at_3
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+ value: 92
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  - type: precision_at_5
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+ value: 90
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  - type: precision_at_10
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  value: 88.6
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  - type: precision_at_20
 
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  - type: precision_at_1000
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  value: 25.81
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  - type: mrr_at_1
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+ value: 90
8285
  - type: mrr_at_3
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  value: 94.6667
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  - type: mrr_at_5
 
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  type: PairClassification
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  pipeline_tag: sentence-similarity
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  tags:
 
9039
  - feature-extraction
9040
  - sentence-similarity
9041
  - mteb
9042
  - arctic
9043
  - snowflake-arctic-embed
 
9044
  - onnx
9045
  - teradata
 
9046
  ---
9047
  # A Teradata Vantage compatible Embeddings Model
9048
 
 
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  print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
9195
  ```
9196
 
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+ You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)