Add new SentenceTransformer model

README.md

@@ -7,7 +7,7 @@ tags:
 - generated_from_trainer
 - dataset_size:111470
 - loss:MultipleNegativesRankingLoss
-base_model: sentence-transformers/all-MiniLM-L6-v2
+base_model: sentence-transformers/all-MiniLM-L12-v2
 widget:
 - source_sentence: why are some rocks radioactive
   sentences:
@@ -106,7 +106,7 @@ 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
@@ -116,49 +116,49 @@ model-index:
       type: NanoMSMARCO
     metrics:
     - type: cosine_accuracy@1
-      value: 0.32
+      value: 0.34
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5
+      value: 0.54
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.56
+      value: 0.64
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
       value: 0.72
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.32
+      value: 0.34
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16666666666666669
+      value: 0.18
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11200000000000002
+      value: 0.12800000000000003
       name: Cosine Precision@5
     - type: cosine_precision@10
       value: 0.07200000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.32
+      value: 0.34
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5
+      value: 0.54
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.56
+      value: 0.64
       name: Cosine Recall@5
     - type: cosine_recall@10
       value: 0.72
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.5076844979819899
+      value: 0.5240037339745247
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.4414682539682539
+      value: 0.4620555555555555
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4541878759718346
+      value: 0.47561895719519554
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -168,49 +168,49 @@ model-index:
       type: NanoNQ
     metrics:
     - type: cosine_accuracy@1
-      value: 0.32
+      value: 0.42
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.48
+      value: 0.6
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.52
+      value: 0.6
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.54
+      value: 0.68
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.32
+      value: 0.42
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16666666666666663
+      value: 0.21333333333333332
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.10800000000000001
+      value: 0.128
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.05800000000000001
+      value: 0.07200000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.29
+      value: 0.38
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.45
+      value: 0.58
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.49
+      value: 0.58
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.52
+      value: 0.65
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4230776979752646
+      value: 0.5323118123166091
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.40852380952380957
+      value: 0.5109126984126984
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4024677771121777
+      value: 0.49965941289619986
       name: Cosine Map@100
   - task:
       type: nano-beir
@@ -220,61 +220,61 @@ model-index:
       type: NanoBEIR_mean
     metrics:
     - type: cosine_accuracy@1
-      value: 0.32
+      value: 0.38
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.49
+      value: 0.5700000000000001
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.54
+      value: 0.62
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.63
+      value: 0.7
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.32
+      value: 0.38
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.16666666666666666
+      value: 0.19666666666666666
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11000000000000001
+      value: 0.128
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.065
+      value: 0.07200000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.305
+      value: 0.36
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.475
+      value: 0.56
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.525
+      value: 0.61
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.62
+      value: 0.685
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.46538109797862726
+      value: 0.5281577731455669
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.42499603174603173
+      value: 0.4864841269841269
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.42832782654200613
+      value: 0.4876391850456977
       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
@@ -327,9 +327,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 0.9805],
-#         [1.0000, 1.0000, 0.9805],
-#         [0.9805, 0.9805, 1.0000]])
+# tensor([[1.0000, 1.0000, 0.9589],
+#         [1.0000, 1.0000, 0.9589],
+#         [0.9589, 0.9589, 1.0001]])
 ```
 
 <!--
@@ -367,21 +367,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | NanoMSMARCO | NanoNQ     |
 |:--------------------|:------------|:-----------|
-| cosine_accuracy@1   | 0.32        | 0.32       |
-| cosine_accuracy@3   | 0.5         | 0.48       |
-| cosine_accuracy@5   | 0.56        | 0.52       |
-| cosine_accuracy@10  | 0.72        | 0.54       |
-| cosine_precision@1  | 0.32        | 0.32       |
-| cosine_precision@3  | 0.1667      | 0.1667     |
-| cosine_precision@5  | 0.112       | 0.108      |
-| cosine_precision@10 | 0.072       | 0.058      |
-| cosine_recall@1     | 0.32        | 0.29       |
-| cosine_recall@3     | 0.5         | 0.45       |
-| cosine_recall@5     | 0.56        | 0.49       |
-| cosine_recall@10    | 0.72        | 0.52       |
-| **cosine_ndcg@10**  | **0.5077**  | **0.4231** |
-| cosine_mrr@10       | 0.4415      | 0.4085     |
-| cosine_map@100      | 0.4542      | 0.4025     |
+| cosine_accuracy@1   | 0.34        | 0.42       |
+| cosine_accuracy@3   | 0.54        | 0.6        |
+| cosine_accuracy@5   | 0.64        | 0.6        |
+| cosine_accuracy@10  | 0.72        | 0.68       |
+| cosine_precision@1  | 0.34        | 0.42       |
+| cosine_precision@3  | 0.18        | 0.2133     |
+| cosine_precision@5  | 0.128       | 0.128      |
+| cosine_precision@10 | 0.072       | 0.072      |
+| cosine_recall@1     | 0.34        | 0.38       |
+| cosine_recall@3     | 0.54        | 0.58       |
+| cosine_recall@5     | 0.64        | 0.58       |
+| cosine_recall@10    | 0.72        | 0.65       |
+| **cosine_ndcg@10**  | **0.524**   | **0.5323** |
+| cosine_mrr@10       | 0.4621      | 0.5109     |
+| cosine_map@100      | 0.4756      | 0.4997     |
 
 #### Nano BEIR
 
@@ -399,21 +399,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.32       |
-| cosine_accuracy@3   | 0.49       |
-| cosine_accuracy@5   | 0.54       |
-| cosine_accuracy@10  | 0.63       |
-| cosine_precision@1  | 0.32       |
-| cosine_precision@3  | 0.1667     |
-| cosine_precision@5  | 0.11       |
-| cosine_precision@10 | 0.065      |
-| cosine_recall@1     | 0.305      |
-| cosine_recall@3     | 0.475      |
-| cosine_recall@5     | 0.525      |
-| cosine_recall@10    | 0.62       |
-| **cosine_ndcg@10**  | **0.4654** |
-| cosine_mrr@10       | 0.425      |
-| cosine_map@100      | 0.4283     |
+| cosine_accuracy@1   | 0.38       |
+| cosine_accuracy@3   | 0.57       |
+| cosine_accuracy@5   | 0.62       |
+| cosine_accuracy@10  | 0.7        |
+| cosine_precision@1  | 0.38       |
+| cosine_precision@3  | 0.1967     |
+| cosine_precision@5  | 0.128      |
+| cosine_precision@10 | 0.072      |
+| cosine_recall@1     | 0.36       |
+| cosine_recall@3     | 0.56       |
+| cosine_recall@5     | 0.61       |
+| cosine_recall@10    | 0.685      |
+| **cosine_ndcg@10**  | **0.5282** |
+| cosine_mrr@10       | 0.4865     |
+| cosine_map@100      | 0.4876     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -487,9 +487,9 @@ You can finetune this model on your own dataset.
 - `eval_strategy`: steps
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
-- `learning_rate`: 0.0001
+- `learning_rate`: 8e-05
 - `weight_decay`: 0.005
-- `max_steps`: 2250
+- `max_steps`: 1687
 - `warmup_ratio`: 0.1
 - `fp16`: True
 - `dataloader_drop_last`: True
@@ -516,14 +516,14 @@ You can finetune this model on your own dataset.
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 0.0001
+- `learning_rate`: 8e-05
 - `weight_decay`: 0.005
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
 - `num_train_epochs`: 3.0
-- `max_steps`: 2250
+- `max_steps`: 1687
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
 - `warmup_ratio`: 0.1
@@ -630,16 +630,13 @@ 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    | -             | 1.1445          | 0.5540                     | 0.5931                | 0.5735                       |
-| 0.2874 | 250  | 1.0772        | 0.8647          | 0.4705                     | 0.5045                | 0.4875                       |
-| 0.5747 | 500  | 0.9853        | 0.8353          | 0.4884                     | 0.4658                | 0.4771                       |
-| 0.8621 | 750  | 0.9571        | 0.8153          | 0.5181                     | 0.4371                | 0.4776                       |
-| 1.1494 | 1000 | 0.8923        | 0.8060          | 0.4848                     | 0.4274                | 0.4561                       |
-| 1.4368 | 1250 | 0.8458        | 0.8020          | 0.5415                     | 0.4466                | 0.4941                       |
-| 1.7241 | 1500 | 0.8394        | 0.7929          | 0.4928                     | 0.4569                | 0.4748                       |
-| 2.0115 | 1750 | 0.8308        | 0.7917          | 0.5332                     | 0.4285                | 0.4808                       |
-| 2.2989 | 2000 | 0.7709        | 0.7921          | 0.4952                     | 0.4088                | 0.4520                       |
-| 2.5862 | 2250 | 0.7685        | 0.7914          | 0.5077                     | 0.4231                | 0.4654                       |
+| 0      | 0    | -             | 1.1142          | 0.5887                     | 0.5786                | 0.5836                       |
+| 0.2874 | 250  | 1.0151        | 0.8262          | 0.5547                     | 0.5323                | 0.5435                       |
+| 0.5747 | 500  | 0.9318        | 0.8010          | 0.5094                     | 0.5342                | 0.5218                       |
+| 0.8621 | 750  | 0.9087        | 0.7803          | 0.5242                     | 0.5461                | 0.5352                       |
+| 1.1494 | 1000 | 0.8448        | 0.7753          | 0.5291                     | 0.5333                | 0.5312                       |
+| 1.4368 | 1250 | 0.798         | 0.7693          | 0.5249                     | 0.5104                | 0.5176                       |
+| 1.7241 | 1500 | 0.7883        | 0.7618          | 0.5240                     | 0.5323                | 0.5282                       |
 
 
 ### Framework Versions