Add new SentenceTransformer model

README.md

@@ -7,7 +7,7 @@ tags:
 - generated_from_trainer
 - dataset_size:90000
 - loss:MultipleNegativesRankingLoss
-base_model: sentence-transformers/all-MiniLM-L6-v2
+base_model: sentence-transformers/all-MiniLM-L12-v2
 widget:
 - source_sentence: what is the maximum i can contribute to a traditional ira
   sentences:
@@ -121,7 +121,7 @@ metrics:
 - cosine_mrr@10
 - cosine_map@100
 model-index:
-- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
   results:
   - task:
       type: information-retrieval
@@ -131,49 +131,49 @@ model-index:
       type: NanoMSMARCO
     metrics:
     - type: cosine_accuracy@1
-      value: 0.32
+      value: 0.36
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.54
+      value: 0.58
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.62
+      value: 0.64
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.72
+      value: 0.76
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.32
+      value: 0.36
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.18
+      value: 0.19333333333333333
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.124
+      value: 0.128
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07200000000000001
+      value: 0.07600000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.32
+      value: 0.36
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.54
+      value: 0.58
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.62
+      value: 0.64
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.72
+      value: 0.76
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.5113600890173079
+      value: 0.5502773798420649
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.44588095238095227
+      value: 0.4841904761904761
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.46015815095025
+      value: 0.49554545654198856
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -183,49 +183,49 @@ model-index:
       type: NanoNQ
     metrics:
     - type: cosine_accuracy@1
-      value: 0.34
+      value: 0.38
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5
+      value: 0.56
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.58
+      value: 0.6
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.62
+      value: 0.66
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.34
+      value: 0.38
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.18
+      value: 0.19333333333333333
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12400000000000003
+      value: 0.128
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.068
+      value: 0.07
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.31
+      value: 0.37
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.49
+      value: 0.53
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.56
+      value: 0.58
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.61
+      value: 0.63
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.47606860320855016
+      value: 0.50866692066392
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.44116666666666665
+      value: 0.4758571428571428
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4435242837774235
+      value: 0.47823183905498623
       name: Cosine Map@100
   - task:
       type: nano-beir
@@ -235,61 +235,61 @@ model-index:
       type: NanoBEIR_mean
     metrics:
     - type: cosine_accuracy@1
-      value: 0.33
+      value: 0.37
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.52
+      value: 0.5700000000000001
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6
+      value: 0.62
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.6699999999999999
+      value: 0.71
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.33
+      value: 0.37
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.18
+      value: 0.19333333333333333
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12400000000000001
+      value: 0.128
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07
+      value: 0.07300000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.315
+      value: 0.365
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.515
+      value: 0.5549999999999999
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5900000000000001
+      value: 0.61
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.665
+      value: 0.6950000000000001
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.493714346112929
+      value: 0.5294721502529924
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.44352380952380943
+      value: 0.48002380952380946
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.45184121736383676
+      value: 0.4868886477984874
       name: Cosine Map@100
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Base model:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 936af83a2ecce5fe87a09109ff5cbcefe073173a -->
 - **Maximum Sequence Length:** 128 tokens
 - **Output Dimensionality:** 384 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -342,9 +342,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.5834, 0.4906],
-#         [0.5834, 1.0000, 0.2633],
-#         [0.4906, 0.2633, 1.0000]])
+# tensor([[1.0001, 0.5920, 0.3852],
+#         [0.5920, 1.0000, 0.0748],
+#         [0.3852, 0.0748, 1.0001]])
 ```
 
 <!--
@@ -382,21 +382,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | NanoMSMARCO | NanoNQ     |
 |:--------------------|:------------|:-----------|
-| cosine_accuracy@1   | 0.32        | 0.34       |
-| cosine_accuracy@3   | 0.54        | 0.5        |
-| cosine_accuracy@5   | 0.62        | 0.58       |
-| cosine_accuracy@10  | 0.72        | 0.62       |
-| cosine_precision@1  | 0.32        | 0.34       |
-| cosine_precision@3  | 0.18        | 0.18       |
-| cosine_precision@5  | 0.124       | 0.124      |
-| cosine_precision@10 | 0.072       | 0.068      |
-| cosine_recall@1     | 0.32        | 0.31       |
-| cosine_recall@3     | 0.54        | 0.49       |
-| cosine_recall@5     | 0.62        | 0.56       |
-| cosine_recall@10    | 0.72        | 0.61       |
-| **cosine_ndcg@10**  | **0.5114**  | **0.4761** |
-| cosine_mrr@10       | 0.4459      | 0.4412     |
-| cosine_map@100      | 0.4602      | 0.4435     |
+| cosine_accuracy@1   | 0.36        | 0.38       |
+| cosine_accuracy@3   | 0.58        | 0.56       |
+| cosine_accuracy@5   | 0.64        | 0.6        |
+| cosine_accuracy@10  | 0.76        | 0.66       |
+| cosine_precision@1  | 0.36        | 0.38       |
+| cosine_precision@3  | 0.1933      | 0.1933     |
+| cosine_precision@5  | 0.128       | 0.128      |
+| cosine_precision@10 | 0.076       | 0.07       |
+| cosine_recall@1     | 0.36        | 0.37       |
+| cosine_recall@3     | 0.58        | 0.53       |
+| cosine_recall@5     | 0.64        | 0.58       |
+| cosine_recall@10    | 0.76        | 0.63       |
+| **cosine_ndcg@10**  | **0.5503**  | **0.5087** |
+| cosine_mrr@10       | 0.4842      | 0.4759     |
+| cosine_map@100      | 0.4955      | 0.4782     |
 
 #### Nano BEIR
 
@@ -414,21 +414,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.33       |
-| cosine_accuracy@3   | 0.52       |
-| cosine_accuracy@5   | 0.6        |
-| cosine_accuracy@10  | 0.67       |
-| cosine_precision@1  | 0.33       |
-| cosine_precision@3  | 0.18       |
-| cosine_precision@5  | 0.124      |
-| cosine_precision@10 | 0.07       |
-| cosine_recall@1     | 0.315      |
-| cosine_recall@3     | 0.515      |
-| cosine_recall@5     | 0.59       |
-| cosine_recall@10    | 0.665      |
-| **cosine_ndcg@10**  | **0.4937** |
-| cosine_mrr@10       | 0.4435     |
-| cosine_map@100      | 0.4518     |
+| cosine_accuracy@1   | 0.37       |
+| cosine_accuracy@3   | 0.57       |
+| cosine_accuracy@5   | 0.62       |
+| cosine_accuracy@10  | 0.71       |
+| cosine_precision@1  | 0.37       |
+| cosine_precision@3  | 0.1933     |
+| cosine_precision@5  | 0.128      |
+| cosine_precision@10 | 0.073      |
+| cosine_recall@1     | 0.365      |
+| cosine_recall@3     | 0.555      |
+| cosine_recall@5     | 0.61       |
+| cosine_recall@10    | 0.695      |
+| **cosine_ndcg@10**  | **0.5295** |
+| cosine_mrr@10       | 0.48       |
+| cosine_map@100      | 0.4869     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -645,19 +645,19 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
 |:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
-| 0      | 0    | -             | 1.2542          | 0.5540                     | 0.5931                | 0.5735                       |
-| 0.3556 | 250  | 1.2668        | 0.9745          | 0.5174                     | 0.5317                | 0.5246                       |
-| 0.7112 | 500  | 1.1283        | 0.9461          | 0.5013                     | 0.5121                | 0.5067                       |
-| 1.0669 | 750  | 1.1038        | 0.9351          | 0.5139                     | 0.5015                | 0.5077                       |
-| 1.4225 | 1000 | 1.0775        | 0.9264          | 0.5211                     | 0.5268                | 0.5239                       |
-| 1.7781 | 1250 | 1.0688        | 0.9222          | 0.5290                     | 0.4973                | 0.5132                       |
-| 2.1337 | 1500 | 1.0564        | 0.9181          | 0.5224                     | 0.4688                | 0.4956                       |
-| 2.4893 | 1750 | 1.0411        | 0.9167          | 0.5210                     | 0.5014                | 0.5112                       |
-| 2.8450 | 2000 | 1.0432        | 0.9141          | 0.5257                     | 0.4661                | 0.4959                       |
-| 3.2006 | 2250 | 1.0299        | 0.9124          | 0.5122                     | 0.4895                | 0.5008                       |
-| 3.5562 | 2500 | 1.0308        | 0.9112          | 0.5131                     | 0.4848                | 0.4989                       |
-| 3.9118 | 2750 | 1.0231        | 0.9106          | 0.5106                     | 0.4843                | 0.4975                       |
-| 4.2674 | 3000 | 1.0183        | 0.9105          | 0.5114                     | 0.4761                | 0.4937                       |
+| 0      | 0    | -             | 1.2073          | 0.5887                     | 0.5786                | 0.5836                       |
+| 0.3556 | 250  | 1.19          | 0.9200          | 0.5466                     | 0.5332                | 0.5399                       |
+| 0.7112 | 500  | 1.0578        | 0.8943          | 0.5396                     | 0.5252                | 0.5324                       |
+| 1.0669 | 750  | 1.0352        | 0.8849          | 0.5497                     | 0.5252                | 0.5375                       |
+| 1.4225 | 1000 | 1.002         | 0.8761          | 0.5484                     | 0.5308                | 0.5396                       |
+| 1.7781 | 1250 | 0.9953        | 0.8732          | 0.5336                     | 0.5213                | 0.5274                       |
+| 2.1337 | 1500 | 0.9828        | 0.8686          | 0.5340                     | 0.5126                | 0.5233                       |
+| 2.4893 | 1750 | 0.965         | 0.8675          | 0.5417                     | 0.5094                | 0.5256                       |
+| 2.8450 | 2000 | 0.9651        | 0.8658          | 0.5467                     | 0.4994                | 0.5230                       |
+| 3.2006 | 2250 | 0.9522        | 0.8650          | 0.5295                     | 0.5097                | 0.5196                       |
+| 3.5562 | 2500 | 0.9521        | 0.8635          | 0.5446                     | 0.5124                | 0.5285                       |
+| 3.9118 | 2750 | 0.9444        | 0.8635          | 0.5529                     | 0.5070                | 0.5299                       |
+| 4.2674 | 3000 | 0.9397        | 0.8632          | 0.5503                     | 0.5087                | 0.5295                       |
 
 
 ### Framework Versions