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oneryalcin
/
static-embedding-chess

Sentence Similarity
sentence-transformers
Safetensors
English
static-embedding
chess
retrieval
exploratory
Model card Files
xet
 Community
oneryalcin commited on
Commit
9b00e17
·
verified ·
1 Parent(s): 974505b

Training in progress, step 250

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Files changed (3) hide show
  1. README.md +31 -21
  2. eval/Information-Retrieval_evaluation_chess-ir_results.csv +1 -0
  3. model.safetensors +1 -1
README.md CHANGED
@@ -64,31 +64,31 @@ model-index:
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  type: chess-ir
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  metrics:
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  - type: cosine_accuracy@1
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- value: 0.03
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  name: Cosine Accuracy@1
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  - type: cosine_accuracy@10
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- value: 0.16
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  name: Cosine Accuracy@10
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  - type: cosine_precision@1
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- value: 0.03
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  name: Cosine Precision@1
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  - type: cosine_precision@10
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- value: 0.02
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  name: Cosine Precision@10
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  - type: cosine_recall@1
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- value: 0.009999999999999998
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  name: Cosine Recall@1
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  - type: cosine_recall@10
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- value: 0.06666666666666667
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  name: Cosine Recall@10
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  - type: cosine_ndcg@10
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- value: 0.045080933582823335
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  name: Cosine Ndcg@10
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  - type: cosine_mrr@10
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- value: 0.05857142857142858
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  name: Cosine Mrr@10
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  - type: cosine_map@100
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- value: 0.033163497941181515
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  name: Cosine Map@100
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  ---
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@@ -155,7 +155,7 @@ print(query_embeddings.shape, document_embeddings.shape)
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  # Get the similarity scores for the embeddings
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  similarities = model.similarity(query_embeddings, document_embeddings)
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  print(similarities)
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- # tensor([[ 0.9284, -0.1828, 0.4001]])
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  ```
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  <!--
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  ### Direct Usage (Transformers)
@@ -192,15 +192,15 @@ You can finetune this model on your own dataset.
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  | Metric | Value |
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  |:--------------------|:-----------|
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- | cosine_accuracy@1 | 0.03 |
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- | cosine_accuracy@10 | 0.16 |
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- | cosine_precision@1 | 0.03 |
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- | cosine_precision@10 | 0.02 |
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- | cosine_recall@1 | 0.01 |
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- | cosine_recall@10 | 0.0667 |
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- | **cosine_ndcg@10** | **0.0451** |
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- | cosine_mrr@10 | 0.0586 |
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- | cosine_map@100 | 0.0332 |
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  <!--
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  ## Bias, Risks and Limitations
@@ -415,12 +415,22 @@ You can finetune this model on your own dataset.
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  | 0.0667 | 190 | 1.3124 | - |
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  | 0.0685 | 195 | 1.3237 | - |
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  | 0.0702 | 200 | 1.3051 | 0.0451 |
 
 
 
 
 
 
 
 
 
 
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  ### Training Time
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- - **Training**: 17.1 seconds
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  - **Evaluation**: 0.2 seconds
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- - **Total**: 17.3 seconds
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  ### Framework Versions
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  - Python: 3.12.10
 
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  type: chess-ir
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  metrics:
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  - type: cosine_accuracy@1
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+ value: 0.025
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  name: Cosine Accuracy@1
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  - type: cosine_accuracy@10
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+ value: 0.14
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  name: Cosine Accuracy@10
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  - type: cosine_precision@1
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+ value: 0.025
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  name: Cosine Precision@1
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  - type: cosine_precision@10
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+ value: 0.017
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  name: Cosine Precision@10
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  - type: cosine_recall@1
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+ value: 0.008333333333333333
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  name: Cosine Recall@1
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  - type: cosine_recall@10
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+ value: 0.056666666666666664
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  name: Cosine Recall@10
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  - type: cosine_ndcg@10
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+ value: 0.037406426241984
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  name: Cosine Ndcg@10
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  - type: cosine_mrr@10
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+ value: 0.049240079365079355
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  name: Cosine Mrr@10
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  - type: cosine_map@100
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+ value: 0.02874627448743367
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  name: Cosine Map@100
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  ---
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  # Get the similarity scores for the embeddings
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  similarities = model.similarity(query_embeddings, document_embeddings)
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  print(similarities)
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+ # tensor([[ 0.9411, -0.1930, 0.3964]])
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  ```
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  <!--
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  ### Direct Usage (Transformers)
 
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  | Metric | Value |
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  |:--------------------|:-----------|
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+ | cosine_accuracy@1 | 0.025 |
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+ | cosine_accuracy@10 | 0.14 |
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+ | cosine_precision@1 | 0.025 |
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+ | cosine_precision@10 | 0.017 |
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+ | cosine_recall@1 | 0.0083 |
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+ | cosine_recall@10 | 0.0567 |
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+ | **cosine_ndcg@10** | **0.0374** |
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+ | cosine_mrr@10 | 0.0492 |
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+ | cosine_map@100 | 0.0287 |
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  <!--
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  ## Bias, Risks and Limitations
 
415
  | 0.0667 | 190 | 1.3124 | - |
416
  | 0.0685 | 195 | 1.3237 | - |
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  | 0.0702 | 200 | 1.3051 | 0.0451 |
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+ | 0.0720 | 205 | 1.2801 | - |
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+ | 0.0737 | 210 | 1.3404 | - |
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+ | 0.0755 | 215 | 1.2916 | - |
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+ | 0.0772 | 220 | 1.2981 | - |
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+ | 0.0790 | 225 | 1.3321 | - |
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+ | 0.0808 | 230 | 1.3369 | - |
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+ | 0.0825 | 235 | 1.3059 | - |
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+ | 0.0843 | 240 | 1.3213 | - |
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+ | 0.0860 | 245 | 1.3127 | - |
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+ | 0.0878 | 250 | 1.2801 | 0.0374 |
428
 
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  ### Training Time
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+ - **Training**: 21.2 seconds
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  - **Evaluation**: 0.2 seconds
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+ - **Total**: 21.5 seconds
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  ### Framework Versions
436
  - Python: 3.12.10
eval/Information-Retrieval_evaluation_chess-ir_results.csv CHANGED
@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@10,cosine-Precision@1,cosine-Recal
3
  0.0351123595505618,100,0.015,0.135,0.015,0.005,0.016,0.05333333333333333,0.04136111111111111,0.03352606053277749,0.025214543549657912
4
  0.05266853932584269,150,0.02,0.12,0.02,0.006666666666666666,0.0155,0.051666666666666666,0.04391468253968253,0.034539315152376744,0.02851338765635309
5
  0.0702247191011236,200,0.03,0.16,0.03,0.009999999999999998,0.02,0.06666666666666667,0.05857142857142858,0.045080933582823335,0.033163497941181515
 
 
3
  0.0351123595505618,100,0.015,0.135,0.015,0.005,0.016,0.05333333333333333,0.04136111111111111,0.03352606053277749,0.025214543549657912
4
  0.05266853932584269,150,0.02,0.12,0.02,0.006666666666666666,0.0155,0.051666666666666666,0.04391468253968253,0.034539315152376744,0.02851338765635309
5
  0.0702247191011236,200,0.03,0.16,0.03,0.009999999999999998,0.02,0.06666666666666667,0.05857142857142858,0.045080933582823335,0.033163497941181515
6
+ 0.0877808988764045,250,0.025,0.14,0.025,0.008333333333333333,0.017,0.056666666666666664,0.049240079365079355,0.037406426241984,0.02874627448743367
model.safetensors CHANGED
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