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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
d2d59e9
·
verified ·
1 Parent(s): a32b369

Training in progress, step 100

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Files changed (3) hide show
  1. README.md +26 -16
  2. eval/Information-Retrieval_evaluation_chess-ir_results.csv +1 -0
  3. model.safetensors +1 -1
README.md CHANGED
@@ -67,28 +67,28 @@ model-index:
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  value: 0.015
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  name: Cosine Accuracy@1
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  - type: cosine_accuracy@10
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- value: 0.115
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  name: Cosine Accuracy@10
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  - type: cosine_precision@1
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  value: 0.015
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  name: Cosine Precision@1
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  - type: cosine_precision@10
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- value: 0.013000000000000001
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  name: Cosine Precision@10
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  - type: cosine_recall@1
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  value: 0.005
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  name: Cosine Recall@1
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  - type: cosine_recall@10
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- value: 0.04333333333333333
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  name: Cosine Recall@10
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  - type: cosine_ndcg@10
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- value: 0.02770564804107805
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  name: Cosine Ndcg@10
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  - type: cosine_mrr@10
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- value: 0.03541269841269841
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  name: Cosine Mrr@10
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  - type: cosine_map@100
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- value: 0.021195015342589062
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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([[ 0.7906, -0.0909, 0.3855]])
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  ```
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  <!--
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  ### Direct Usage (Transformers)
@@ -193,14 +193,14 @@ 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 | 0.015 |
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- | cosine_accuracy@10 | 0.115 |
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  | cosine_precision@1 | 0.015 |
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- | cosine_precision@10 | 0.013 |
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  | cosine_recall@1 | 0.005 |
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- | cosine_recall@10 | 0.0433 |
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- | **cosine_ndcg@10** | **0.0277** |
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- | cosine_mrr@10 | 0.0354 |
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- | cosine_map@100 | 0.0212 |
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  <!--
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  ## Bias, Risks and Limitations
@@ -385,12 +385,22 @@ You can finetune this model on your own dataset.
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  | 0.0140 | 40 | 1.4872 | - |
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  | 0.0158 | 45 | 1.4555 | - |
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  | 0.0176 | 50 | 1.4493 | 0.0277 |
 
 
 
 
 
 
 
 
 
 
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  ### Training Time
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- - **Training**: 5.1 seconds
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- - **Evaluation**: 0.0 seconds
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- - **Total**: 5.2 seconds
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  ### Framework Versions
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  - Python: 3.12.10
 
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  value: 0.015
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  name: Cosine Accuracy@1
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  - type: cosine_accuracy@10
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+ value: 0.135
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  name: Cosine Accuracy@10
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  - type: cosine_precision@1
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  value: 0.015
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  name: Cosine Precision@1
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  - type: cosine_precision@10
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+ value: 0.016
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  name: Cosine Precision@10
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  - type: cosine_recall@1
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  value: 0.005
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  name: Cosine Recall@1
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  - type: cosine_recall@10
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+ value: 0.05333333333333333
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  name: Cosine Recall@10
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  - type: cosine_ndcg@10
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+ value: 0.03352606053277749
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  name: Cosine Ndcg@10
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  - type: cosine_mrr@10
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+ value: 0.04136111111111111
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  name: Cosine Mrr@10
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  - type: cosine_map@100
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+ value: 0.025214543549657912
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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([[ 0.8804, -0.1477, 0.3899]])
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  ```
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  <!--
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  ### Direct Usage (Transformers)
 
193
  | Metric | Value |
194
  |:--------------------|:-----------|
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  | cosine_accuracy@1 | 0.015 |
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+ | cosine_accuracy@10 | 0.135 |
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  | cosine_precision@1 | 0.015 |
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+ | cosine_precision@10 | 0.016 |
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  | cosine_recall@1 | 0.005 |
200
+ | cosine_recall@10 | 0.0533 |
201
+ | **cosine_ndcg@10** | **0.0335** |
202
+ | cosine_mrr@10 | 0.0414 |
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+ | cosine_map@100 | 0.0252 |
204
 
205
  <!--
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  ## Bias, Risks and Limitations
 
385
  | 0.0140 | 40 | 1.4872 | - |
386
  | 0.0158 | 45 | 1.4555 | - |
387
  | 0.0176 | 50 | 1.4493 | 0.0277 |
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+ | 0.0193 | 55 | 1.4075 | - |
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+ | 0.0211 | 60 | 1.4012 | - |
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+ | 0.0228 | 65 | 1.4055 | - |
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+ | 0.0246 | 70 | 1.3977 | - |
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+ | 0.0263 | 75 | 1.3597 | - |
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+ | 0.0281 | 80 | 1.3765 | - |
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+ | 0.0298 | 85 | 1.3657 | - |
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+ | 0.0316 | 90 | 1.3138 | - |
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+ | 0.0334 | 95 | 1.3596 | - |
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+ | 0.0351 | 100 | 1.3428 | 0.0335 |
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  ### Training Time
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+ - **Training**: 9.2 seconds
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+ - **Evaluation**: 0.1 seconds
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+ - **Total**: 9.3 seconds
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  ### Framework Versions
406
  - Python: 3.12.10
eval/Information-Retrieval_evaluation_chess-ir_results.csv CHANGED
@@ -1,2 +1,3 @@
1
  epoch,steps,cosine-Accuracy@1,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
2
  0.0175561797752809,50,0.015,0.115,0.015,0.005,0.013000000000000001,0.04333333333333333,0.03541269841269841,0.02770564804107805,0.021195015342589062
 
 
1
  epoch,steps,cosine-Accuracy@1,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
2
  0.0175561797752809,50,0.015,0.115,0.015,0.005,0.013000000000000001,0.04333333333333333,0.03541269841269841,0.02770564804107805,0.021195015342589062
3
+ 0.0351123595505618,100,0.015,0.135,0.015,0.005,0.016,0.05333333333333333,0.04136111111111111,0.03352606053277749,0.025214543549657912
model.safetensors CHANGED
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  size 4343904
 
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