Training in progress, step 200

README.md

@@ -64,31 +64,31 @@ model-index:
       type: chess-ir
     metrics:
     - type: cosine_accuracy@1
-      value: 0.02
+      value: 0.03
       name: Cosine Accuracy@1
     - type: cosine_accuracy@10
-      value: 0.12
+      value: 0.16
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.02
+      value: 0.03
       name: Cosine Precision@1
     - type: cosine_precision@10
-      value: 0.0155
+      value: 0.02
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.006666666666666666
+      value: 0.009999999999999998
       name: Cosine Recall@1
     - type: cosine_recall@10
-      value: 0.051666666666666666
+      value: 0.06666666666666667
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.034539315152376744
+      value: 0.045080933582823335
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.04391468253968253
+      value: 0.05857142857142858
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.02851338765635309
+      value: 0.033163497941181515
       name: Cosine Map@100
 ---
 
@@ -155,7 +155,7 @@ print(query_embeddings.shape, document_embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(query_embeddings, document_embeddings)
 print(similarities)
-# tensor([[ 0.9140, -0.1872,  0.3933]])
+# tensor([[ 0.9284, -0.1828,  0.4001]])
 ```
 <!--
 ### Direct Usage (Transformers)
@@ -192,15 +192,15 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.02       |
-| cosine_accuracy@10  | 0.12       |
-| cosine_precision@1  | 0.02       |
-| cosine_precision@10 | 0.0155     |
-| cosine_recall@1     | 0.0067     |
-| cosine_recall@10    | 0.0517     |
-| **cosine_ndcg@10**  | **0.0345** |
-| cosine_mrr@10       | 0.0439     |
-| cosine_map@100      | 0.0285     |
+| cosine_accuracy@1   | 0.03       |
+| cosine_accuracy@10  | 0.16       |
+| cosine_precision@1  | 0.03       |
+| cosine_precision@10 | 0.02       |
+| cosine_recall@1     | 0.01       |
+| cosine_recall@10    | 0.0667     |
+| **cosine_ndcg@10**  | **0.0451** |
+| cosine_mrr@10       | 0.0586     |
+| cosine_map@100      | 0.0332     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -405,12 +405,22 @@ You can finetune this model on your own dataset.
 | 0.0492 | 140  | 1.3428        | -                       |
 | 0.0509 | 145  | 1.2886        | -                       |
 | 0.0527 | 150  | 1.2895        | 0.0345                  |
+| 0.0544 | 155  | 1.3418        | -                       |
+| 0.0562 | 160  | 1.3498        | -                       |
+| 0.0579 | 165  | 1.3033        | -                       |
+| 0.0597 | 170  | 1.2958        | -                       |
+| 0.0614 | 175  | 1.3081        | -                       |
+| 0.0632 | 180  | 1.3154        | -                       |
+| 0.0650 | 185  | 1.3129        | -                       |
+| 0.0667 | 190  | 1.3124        | -                       |
+| 0.0685 | 195  | 1.3237        | -                       |
+| 0.0702 | 200  | 1.3051        | 0.0451                  |
 
 
 ### Training Time
-- **Training**: 13.2 seconds
-- **Evaluation**: 0.1 seconds
-- **Total**: 13.3 seconds
+- **Training**: 17.1 seconds
+- **Evaluation**: 0.2 seconds
+- **Total**: 17.3 seconds
 
 ### Framework Versions
 - Python: 3.12.10

eval/Information-Retrieval_evaluation_chess-ir_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@10,cosine-Precision@1,cosine-Recal
 0.0175561797752809,50,0.015,0.115,0.015,0.005,0.013000000000000001,0.04333333333333333,0.03541269841269841,0.02770564804107805,0.021195015342589062
 0.0351123595505618,100,0.015,0.135,0.015,0.005,0.016,0.05333333333333333,0.04136111111111111,0.03352606053277749,0.025214543549657912
 0.05266853932584269,150,0.02,0.12,0.02,0.006666666666666666,0.0155,0.051666666666666666,0.04391468253968253,0.034539315152376744,0.02851338765635309
+0.0702247191011236,200,0.03,0.16,0.03,0.009999999999999998,0.02,0.06666666666666667,0.05857142857142858,0.045080933582823335,0.033163497941181515

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f5645fc31a78d305cc82c0d937c9ceecfd92d40fa95c3e3d81de94d475b2b109
+oid sha256:9bcc876f3677edb4cb0f11700734e06fa48fa3c849b976cbcd9ffe8419e6eb23
 size 4343904