Training in progress, step 855

README.md

@@ -77,28 +77,28 @@ model-index:
       value: 0.01
       name: Cosine Accuracy@1
     - type: cosine_accuracy@10
-      value: 0.06
+      value: 0.055
       name: Cosine Accuracy@10
     - type: cosine_precision@1
       value: 0.01
       name: Cosine Precision@1
     - type: cosine_precision@10
-      value: 0.006999999999999999
+      value: 0.006
       name: Cosine Precision@10
     - type: cosine_recall@1
       value: 0.003333333333333333
       name: Cosine Recall@1
     - type: cosine_recall@10
-      value: 0.02333333333333333
+      value: 0.019999999999999997
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.0165414546823231
+      value: 0.014141653573050736
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.021797619047619052
+      value: 0.02086111111111111
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.01826039464782554
+      value: 0.012561680163147302
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -108,31 +108,31 @@ model-index:
       type: chess-ir-tokens
     metrics:
     - type: cosine_accuracy@1
-      value: 0.05291005291005291
+      value: 0.037037037037037035
       name: Cosine Accuracy@1
     - type: cosine_accuracy@10
       value: 0.21164021164021163
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.05291005291005291
+      value: 0.037037037037037035
       name: Cosine Precision@1
     - type: cosine_precision@10
-      value: 0.056613756613756616
+      value: 0.047619047619047616
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.0032049522325313766
+      value: 0.0025144161912381744
       name: Cosine Recall@1
     - type: cosine_recall@10
-      value: 0.023108435943979263
+      value: 0.02212990521949281
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.062386658509055025
+      value: 0.0517090496324674
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.09312379272696733
+      value: 0.08710842361636012
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.0369514194632888
+      value: 0.028156284478181654
       name: Cosine Map@100
 ---
 
@@ -199,7 +199,7 @@ print(query_embeddings.shape, document_embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(query_embeddings, document_embeddings)
 print(similarities)
-# tensor([[ 0.9564, -0.1107,  0.0607]])
+# tensor([[ 0.9826, -0.1530,  0.0366]])
 ```
 <!--
 ### Direct Usage (Transformers)
@@ -236,15 +236,15 @@ You can finetune this model on your own dataset.
 
 | Metric              | chess-ir   | chess-ir-tokens |
 |:--------------------|:-----------|:----------------|
-| cosine_accuracy@1   | 0.01       | 0.0529          |
-| cosine_accuracy@10  | 0.06       | 0.2116          |
-| cosine_precision@1  | 0.01       | 0.0529          |
-| cosine_precision@10 | 0.007      | 0.0566          |
-| cosine_recall@1     | 0.0033     | 0.0032          |
-| cosine_recall@10    | 0.0233     | 0.0231          |
-| **cosine_ndcg@10**  | **0.0165** | **0.0624**      |
-| cosine_mrr@10       | 0.0218     | 0.0931          |
-| cosine_map@100      | 0.0183     | 0.037           |
+| cosine_accuracy@1   | 0.01       | 0.037           |
+| cosine_accuracy@10  | 0.055      | 0.2116          |
+| cosine_precision@1  | 0.01       | 0.037           |
+| cosine_precision@10 | 0.006      | 0.0476          |
+| cosine_recall@1     | 0.0033     | 0.0025          |
+| cosine_recall@10    | 0.02       | 0.0221          |
+| **cosine_ndcg@10**  | **0.0141** | **0.0517**      |
+| cosine_mrr@10       | 0.0209     | 0.0871          |
+| cosine_map@100      | 0.0126     | 0.0282          |
 
 <!--
 ## Bias, Risks and Limitations
@@ -445,12 +445,23 @@ You can finetune this model on your own dataset.
 | 0.1833 | 522  | 6.5016        | -                       | -                              |
 | 0.1935 | 551  | 6.4405        | -                       | -                              |
 | 0.2001 | 570  | -             | 0.0165                  | 0.0624                         |
+| 0.2037 | 580  | 6.5354        | -                       | -                              |
+| 0.2138 | 609  | 6.4492        | -                       | -                              |
+| 0.2240 | 638  | 6.4807        | -                       | -                              |
+| 0.2342 | 667  | 6.4568        | -                       | -                              |
+| 0.2444 | 696  | 6.4335        | -                       | -                              |
+| 0.2546 | 725  | 6.4693        | -                       | -                              |
+| 0.2647 | 754  | 6.4870        | -                       | -                              |
+| 0.2749 | 783  | 6.4468        | -                       | -                              |
+| 0.2851 | 812  | 6.4680        | -                       | -                              |
+| 0.2953 | 841  | 6.3538        | -                       | -                              |
+| 0.3002 | 855  | -             | 0.0141                  | 0.0517                         |
 
 
 ### Training Time
-- **Training**: 33.1 seconds
+- **Training**: 49.8 seconds
 - **Evaluation**: 0.1 seconds
-- **Total**: 33.2 seconds
+- **Total**: 49.9 seconds
 
 ### Framework Versions
 - Python: 3.12.10

eval/Information-Retrieval_evaluation_chess-ir-tokens_results.csv

@@ -1,3 +1,4 @@
 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
 0.10007022471910113,285,0.1111111111111111,0.30158730158730157,0.1111111111111111,0.008191309640952804,0.0835978835978836,0.03797928598263959,0.16048962794994542,0.0963937043281825,0.05480807151213741
 0.20014044943820225,570,0.05291005291005291,0.21164021164021163,0.05291005291005291,0.0032049522325313766,0.056613756613756616,0.023108435943979263,0.09312379272696733,0.062386658509055025,0.0369514194632888
+0.30021067415730335,855,0.037037037037037035,0.21164021164021163,0.037037037037037035,0.0025144161912381744,0.047619047619047616,0.02212990521949281,0.08710842361636012,0.0517090496324674,0.028156284478181654

eval/Information-Retrieval_evaluation_chess-ir_results.csv

@@ -1,3 +1,4 @@
 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
 0.10007022471910113,285,0.02,0.135,0.02,0.006666666666666666,0.0175,0.05833333333333333,0.05090277777777777,0.040260232965004236,0.03468285594907049
 0.20014044943820225,570,0.01,0.06,0.01,0.003333333333333333,0.006999999999999999,0.02333333333333333,0.021797619047619052,0.0165414546823231,0.01826039464782554
+0.30021067415730335,855,0.01,0.055,0.01,0.003333333333333333,0.006,0.019999999999999997,0.02086111111111111,0.014141653573050736,0.012561680163147302

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7dc6e93a2832d52718e604d16e60ed4d6898b4984a79af56244b878f9f9eba05
+oid sha256:a585943d76bec5e52fc185f5414dba80093b3693261e4921542d31ea01c10fb8
 size 8880224