Squash: snapshot current state

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1_Pooling/config.json

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+{
+    "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
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- biencoder
+- sentence-transformers
+- text-classification
+- sentence-pair-classification
+- semantic-similarity
+- semantic-search
+- retrieval
+- reranking
+- generated_from_trainer
+- dataset_size:76349300
+- loss:ArcFaceInBatchLoss
+base_model: Alibaba-NLP/gte-modernbert-base
+widget:
+- source_sentence: '"How much would I need to narrate a ""Let''s Play"" video in order
+    to make money from it on YouTube?"'
+  sentences:
+  - How much money do people make from YouTube videos with 1 million views?
+  - '"How much would I need to narrate a ""Let''s Play"" video in order to make money
+    from it on YouTube?"'
+  - '"Does the sentence, ""I expect to be disappointed,"" make sense?"'
+- source_sentence: '"I appreciate that.'
+  sentences:
+  - '"How is the Mariner rewarded in ""The Rime of the Ancient Mariner"" by Samuel
+    Taylor Coleridge?"'
+  - '"I appreciate that.'
+  - I can appreciate that.
+- source_sentence: '"""It is very easy to defeat someone, but too hard to win some
+    one"". What does the previous sentence mean?"'
+  sentences:
+  - '"How can you use the word ""visceral"" in a sentence?"'
+  - '"""It is very easy to defeat someone, but too hard to win some one"". What does
+    the previous sentence mean?"'
+  - '"What does ""The loudest one in the room is the weakest one in the room."" Mean?"'
+- source_sentence: '" We condemn this raid which is in our view illegal and morally
+    and politically unjustifiable , " London-based NCRI official Ali Safavi told Reuters
+    by telephone .'
+  sentences:
+  - 'London-based NCRI official Ali Safavi told Reuters : " We condemn this raid ,
+    which is in our view illegal and morally and politically unjustifiable . "'
+  - The social awkwardness is complicated by the fact that Marianne is a white girl
+    living with a black family .
+  - art's cause, this in my opinion
+- source_sentence: '"If you click ""like"" on an old post that someone made on your
+    wall yet you''re no longer Facebook friends, will they still receive a notification?"'
+  sentences:
+  - '"Is there is any two wheeler having a gear box which has the feature ""automatic
+    neutral"" when the engine is off while it is in gear?"'
+  - '"If you click ""like"" on an old post that someone made on your wall yet you''re
+    no longer Facebook friends, will they still receive a notification?"'
+  - '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would
+    you be concerned?"'
+datasets:
+- redis/langcache-sentencepairs-v2
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_precision@1
+- cosine_recall@1
+- cosine_ndcg@10
+- cosine_mrr@1
+- cosine_map@100
+- cosine_auc_precision_cache_hit_ratio
+- cosine_auc_similarity_distribution
+model-index:
+- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
+  results:
+  - task:
+      type: custom-information-retrieval
+      name: Custom Information Retrieval
+    dataset:
+      name: test
+      type: test
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5955802603036876
+      name: Cosine Accuracy@1
+    - type: cosine_precision@1
+      value: 0.5955802603036876
+      name: Cosine Precision@1
+    - type: cosine_recall@1
+      value: 0.5780913232288468
+      name: Cosine Recall@1
+    - type: cosine_ndcg@10
+      value: 0.777639866271746
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@1
+      value: 0.5955802603036876
+      name: Cosine Mrr@1
+    - type: cosine_map@100
+      value: 0.7275779687157514
+      name: Cosine Map@100
+    - type: cosine_auc_precision_cache_hit_ratio
+      value: 0.3639683124583609
+      name: Cosine Auc Precision Cache Hit Ratio
+    - type: cosine_auc_similarity_distribution
+      value: 0.15401896350374616
+      name: Cosine Auc Similarity Distribution
+---
+
+# Redis fine-tuned BiEncoder model for semantic caching on LangCache
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
+- **Maximum Sequence Length:** 100 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("redis/langcache-embed-v3")
+# Run inference
+sentences = [
+    '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
+    '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
+    '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would you be concerned?"',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 1.0000, 0.6758],
+#         [1.0000, 1.0000, 0.6758],
+#         [0.6758, 0.6758, 1.0078]], dtype=torch.bfloat16)
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Custom Information Retrieval
+
+* Dataset: `test`
+* Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code>
+
+| Metric                               | Value      |
+|:-------------------------------------|:-----------|
+| cosine_accuracy@1                    | 0.5956     |
+| cosine_precision@1                   | 0.5956     |
+| cosine_recall@1                      | 0.5781     |
+| **cosine_ndcg@10**                   | **0.7776** |
+| cosine_mrr@1                         | 0.5956     |
+| cosine_map@100                       | 0.7276     |
+| cosine_auc_precision_cache_hit_ratio | 0.364      |
+| cosine_auc_similarity_distribution   | 0.154      |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### LangCache Sentence Pairs (all)
+
+* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
+* Size: 132,354 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                        | positive                                                                                      | negative                                                                                       |
+  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
+  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
+  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Childlessness is low in Eastern European countries.</code>                               |
+* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### LangCache Sentence Pairs (all)
+
+* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
+* Size: 132,354 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                        | positive                                                                                      | negative                                                                                       |
+  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
+  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
+  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Childlessness is low in Eastern European countries.</code>                               |
+* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Logs
+| Epoch | Step | test_cosine_ndcg@10 |
+|:-----:|:----:|:-------------------:|
+| -1    | -1   | 0.7776              |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.0
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
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+    "ModernBertModel"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
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+  "global_attn_every_n_layers": 3,
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+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
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+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.0",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.0",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer"
+}

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