Training in progress, step 1188

README.md

@@ -88,31 +88,31 @@ model-index:
       type: chess-ir
     metrics:
     - type: cosine_accuracy@1
-      value: 0.035
+      value: 0.005
       name: Cosine Accuracy@1
     - type: cosine_accuracy@10
-      value: 0.17
+      value: 0.12
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.035
+      value: 0.005
       name: Cosine Precision@1
     - type: cosine_precision@10
-      value: 0.020999999999999998
+      value: 0.013500000000000002
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.011666666666666665
+      value: 0.0016666666666666666
       name: Cosine Recall@1
     - type: cosine_recall@10
-      value: 0.07
+      value: 0.045
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.05046230511277317
+      value: 0.025055316706879063
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.06641468253968254
+      value: 0.027448412698412694
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.04510173764984732
+      value: 0.01920011746106999
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -122,31 +122,31 @@ model-index:
       type: chess-ir-tokens
     metrics:
     - type: cosine_accuracy@1
-      value: 0.10582010582010581
+      value: 0.07407407407407407
       name: Cosine Accuracy@1
     - type: cosine_accuracy@10
-      value: 0.2857142857142857
+      value: 0.2751322751322751
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.10582010582010581
+      value: 0.07407407407407407
       name: Cosine Precision@1
     - type: cosine_precision@10
-      value: 0.07671957671957672
+      value: 0.07301587301587303
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.009819076674514655
+      value: 0.007335022553504582
       name: Cosine Recall@1
     - type: cosine_recall@10
-      value: 0.04177603773249901
+      value: 0.03736997784436353
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.0938286742384803
+      value: 0.08304277519172808
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.15736751490719747
+      value: 0.12320483749055179
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.06197118360268986
+      value: 0.048955611576045346
       name: Cosine Map@100
 ---
 
@@ -213,7 +213,7 @@ print(query_embeddings.shape, document_embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(query_embeddings, document_embeddings)
 print(similarities)
-# tensor([[0.8276, 0.5204, 0.2031]])
+# tensor([[0.8485, 0.5020, 0.2100]])
 ```
 <!--
 ### Direct Usage (Transformers)
@@ -250,15 +250,15 @@ You can finetune this model on your own dataset.
 
 | Metric              | chess-ir   | chess-ir-tokens |
 |:--------------------|:-----------|:----------------|
-| cosine_accuracy@1   | 0.035      | 0.1058          |
-| cosine_accuracy@10  | 0.17       | 0.2857          |
-| cosine_precision@1  | 0.035      | 0.1058          |
-| cosine_precision@10 | 0.021      | 0.0767          |
-| cosine_recall@1     | 0.0117     | 0.0098          |
-| cosine_recall@10    | 0.07       | 0.0418          |
-| **cosine_ndcg@10**  | **0.0505** | **0.0938**      |
-| cosine_mrr@10       | 0.0664     | 0.1574          |
-| cosine_map@100      | 0.0451     | 0.062           |
+| cosine_accuracy@1   | 0.005      | 0.0741          |
+| cosine_accuracy@10  | 0.12       | 0.2751          |
+| cosine_precision@1  | 0.005      | 0.0741          |
+| cosine_precision@10 | 0.0135     | 0.073           |
+| cosine_recall@1     | 0.0017     | 0.0073          |
+| cosine_recall@10    | 0.045      | 0.0374          |
+| **cosine_ndcg@10**  | **0.0251** | **0.083**       |
+| cosine_mrr@10       | 0.0274     | 0.1232          |
+| cosine_map@100      | 0.0192     | 0.049           |
 
 <!--
 ## Bias, Risks and Limitations
@@ -449,12 +449,18 @@ You can finetune this model on your own dataset.
 | 1.6162 | 640  | 3.9819        | -                       | -                              |
 | 1.8182 | 720  | 3.4584        | -                       | -                              |
 | 2.0    | 792  | -             | 0.0505                  | 0.0938                         |
+| 2.0202 | 800  | 3.1303        | -                       | -                              |
+| 2.2222 | 880  | 2.9652        | -                       | -                              |
+| 2.4242 | 960  | 2.8584        | -                       | -                              |
+| 2.6263 | 1040 | 2.7907        | -                       | -                              |
+| 2.8283 | 1120 | 2.7475        | -                       | -                              |
+| 3.0    | 1188 | -             | 0.0251                  | 0.0830                         |
 
 
 ### Training Time
-- **Training**: 2.0 minutes
-- **Evaluation**: 0.1 seconds
-- **Total**: 2.0 minutes
+- **Training**: 3.1 minutes
+- **Evaluation**: 0.2 seconds
+- **Total**: 3.1 minutes
 
 ### 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
 1.0,396,0.12698412698412698,0.3544973544973545,0.12698412698412698,0.0066613186633905,0.10476190476190476,0.0462228099305809,0.18598303518938442,0.11807198905104373,0.06497812950052975
 2.0,792,0.10582010582010581,0.2857142857142857,0.10582010582010581,0.009819076674514655,0.07671957671957672,0.04177603773249901,0.15736751490719747,0.0938286742384803,0.06197118360268986
+3.0,1188,0.07407407407407407,0.2751322751322751,0.07407407407407407,0.007335022553504582,0.07301587301587303,0.03736997784436353,0.12320483749055179,0.08304277519172808,0.048955611576045346

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
 1.0,396,0.06,0.255,0.06,0.02,0.032,0.10666666666666665,0.11224206349206348,0.07998649265394674,0.06593273410392075
 2.0,792,0.035,0.17,0.035,0.011666666666666665,0.020999999999999998,0.07,0.06641468253968254,0.05046230511277317,0.04510173764984732
+3.0,1188,0.005,0.12,0.005,0.0016666666666666666,0.013500000000000002,0.045,0.027448412698412694,0.025055316706879063,0.01920011746106999

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1a057899bd56db42985aa63750bdfa630df92ff7141e3ecaeb8fa6b487e31208
+oid sha256:c672528037a4c5bc695142e31736c561db8222fd68bb52444b2866e0f5babfd6
 size 8880224