radoslavralev commited on
Commit
635cb64
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1 Parent(s): ce2e6d5

Add new SentenceTransformer model

Browse files
2_Dense/config.json ADDED
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+ {
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+ "in_features": 768,
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+ "out_features": 768,
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+ "bias": true,
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+ "activation_function": "torch.nn.modules.activation.ReLU"
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+ }
2_Dense/model.safetensors ADDED
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3_Dense/config.json ADDED
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+ {
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+ "in_features": 768,
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+ "out_features": 768,
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+ "bias": true,
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+ "activation_function": "torch.nn.modules.linear.Identity"
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+ }
3_Dense/model.safetensors ADDED
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README.md CHANGED
@@ -63,49 +63,6 @@ datasets:
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  - redis/langcache-sentencepairs-v2
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  pipeline_tag: sentence-similarity
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  library_name: sentence-transformers
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- metrics:
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- - cosine_accuracy@1
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- - cosine_precision@1
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- - cosine_recall@1
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- - cosine_ndcg@10
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- - cosine_mrr@1
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- - cosine_map@100
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- - cosine_auc_precision_cache_hit_ratio
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- - cosine_auc_similarity_distribution
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- model-index:
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- - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
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- results:
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- - task:
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- type: custom-information-retrieval
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- name: Custom Information Retrieval
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- dataset:
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- name: test
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- type: test
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- metrics:
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- - type: cosine_accuracy@1
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- value: 0.5953768980477223
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- name: Cosine Accuracy@1
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- - type: cosine_precision@1
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- value: 0.5953768980477223
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- name: Cosine Precision@1
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- - type: cosine_recall@1
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- value: 0.5778879609728815
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- name: Cosine Recall@1
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- - type: cosine_ndcg@10
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- value: 0.7775436499957671
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- name: Cosine Ndcg@10
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- - type: cosine_mrr@1
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- value: 0.5953768980477223
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- name: Cosine Mrr@1
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- - type: cosine_map@100
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- value: 0.7274666565910912
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- name: Cosine Map@100
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- - type: cosine_auc_precision_cache_hit_ratio
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- value: 0.36387321267916206
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- name: Cosine Auc Precision Cache Hit Ratio
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- - type: cosine_auc_similarity_distribution
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- value: 0.15403918371209657
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- name: Cosine Auc Similarity Distribution
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  ---
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  # Redis fine-tuned BiEncoder model for semantic caching on LangCache
@@ -137,6 +94,8 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [A
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  SentenceTransformer(
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  (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
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  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 
 
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  )
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  ```
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@@ -169,9 +128,9 @@ print(embeddings.shape)
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  # Get the similarity scores for the embeddings
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  similarities = model.similarity(embeddings, embeddings)
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  print(similarities)
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- # tensor([[1.0001, 1.0001, 0.8242],
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- # [1.0001, 1.0001, 0.8242],
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- # [0.8242, 0.8242, 1.0000]])
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  ```
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  <!--
@@ -198,26 +157,6 @@ You can finetune this model on your own dataset.
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  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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  -->
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- ## Evaluation
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-
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- ### Metrics
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-
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- #### Custom Information Retrieval
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-
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- * Dataset: `test`
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- * Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code>
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-
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- | Metric | Value |
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- |:-------------------------------------|:-----------|
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- | cosine_accuracy@1 | 0.5954 |
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- | cosine_precision@1 | 0.5954 |
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- | cosine_recall@1 | 0.5779 |
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- | **cosine_ndcg@10** | **0.7775** |
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- | cosine_mrr@1 | 0.5954 |
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- | cosine_map@100 | 0.7275 |
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- | cosine_auc_precision_cache_hit_ratio | 0.3639 |
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- | cosine_auc_similarity_distribution | 0.154 |
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-
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  <!--
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  ## Bias, Risks and Limitations
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@@ -433,12 +372,6 @@ You can finetune this model on your own dataset.
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  </details>
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- ### Training Logs
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- | Epoch | Step | test_cosine_ndcg@10 |
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- |:-----:|:----:|:-------------------:|
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- | -1 | -1 | 0.7775 |
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-
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-
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  ### Framework Versions
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  - Python: 3.12.3
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  - Sentence Transformers: 5.1.0
 
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  - redis/langcache-sentencepairs-v2
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  pipeline_tag: sentence-similarity
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  library_name: sentence-transformers
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Redis fine-tuned BiEncoder model for semantic caching on LangCache
 
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  SentenceTransformer(
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  (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
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  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ (mlp_hidden): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.ReLU'})
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+ (mlp_out): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
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  )
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  ```
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  # Get the similarity scores for the embeddings
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  similarities = model.similarity(embeddings, embeddings)
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  print(similarities)
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+ # tensor([[1.0000, 1.0000, 0.7693],
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+ # [1.0000, 1.0000, 0.7693],
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+ # [0.7693, 0.7693, 1.0000]])
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  ```
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  <!--
 
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  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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  -->
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  <!--
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  ## Bias, Risks and Limitations
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  </details>
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  ### Framework Versions
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  - Python: 3.12.3
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  - Sentence Transformers: 5.1.0
modules.json CHANGED
@@ -10,5 +10,17 @@
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  "name": "1",
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  "path": "1_Pooling",
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  "type": "sentence_transformers.models.Pooling"
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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  ]
 
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  "name": "1",
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  "path": "1_Pooling",
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  "type": "sentence_transformers.models.Pooling"
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+ },
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+ {
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+ "idx": 2,
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+ "name": "mlp_hidden",
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+ "path": "2_Dense",
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+ "type": "sentence_transformers.models.Dense"
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+ },
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+ {
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+ "idx": 3,
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+ "name": "mlp_out",
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+ "path": "3_Dense",
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+ "type": "sentence_transformers.models.Dense"
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  }
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  ]