Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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:1047690
+- loss:CoSENTLoss
+base_model: Alibaba-NLP/gte-modernbert-base
+widget:
+- source_sentence: In 2015 Adolf Hitler appeared in the kickstarter short movie ``
+    Kung Fury `` as Taccone ( A.K.A .
+  sentences:
+  - In 2015 , Adolf Hitler appeared in the Kickstarter - short film `` Kung Fury ``
+    as Taccone ( A.K.A .
+  - In 1795 , the only white residents were Dr. John Laidley and two brothers with
+    the surname Ainslie .
+  - The 125th University Match was played in March 2014 at the Rye Golf Club , Oxford
+    , East Sussex won the game 8.5 - 6.5 .
+- source_sentence: From 1973 to 1974 , Aubrey toured with the Cambridge Theatre Company
+    as Diggory in `` She Stoops to Conquer `` and again as Aguecheek .
+  sentences:
+  - Oxide can be reduced to metallic samarium at higher temperatures by heating with
+    a reducing agent such as hydrogen or carbon monoxide .
+  - From 1973 to 1974 Aguecheek toured with the Cambridge Theatre Company as Diggory
+    in `` You Stoops to Conquer `` and again as Aubrey .
+  - The medals were presented by Barry Maister , IOC member , New Zealand and Sarah
+    Webb Gosling , Vice President of World Sailing .
+- source_sentence: There is no official wall on the border , although there are sections
+    of fence near populated areas and continuous border crossings .
+  sentences:
+  - The 2014 -- 15 Boston Bruins season was the 91st season for the National Hockey
+    League franchise that was established on November 1 , 1924 .
+  - He was trained by the Inghams and owned by John Hawkes .
+  - There is no continuous wall on the border , although there are fence sections
+    near populated areas and official border crossings .
+- source_sentence: Capital . `` The French established similar hill stations in Indochina
+    , such as Dalat built in 1921 .
+  sentences:
+  - Lubuk China is a small town in Alor Gajah District , Melaka , Malaysia . It is
+    situated near the border with Negeri Sembilan .
+  - The French established similar hill stations in Indochina , such as Dalat , built
+    in 1921 .
+  - John Potts ( or Pott ) was a doctor and colonial governor of Virginia in the Jamestown
+    settlement at Virginia Colony in the early 17th century .
+- source_sentence: The band pursued `` signals `` in January 2012 in three weeks ,
+    and drums were recorded in a day and a half .
+  sentences:
+  - It was repaired at the beginning of the 20th century and is listed as closed in
+    our records .
+  - The band tracked `` Signals `` in three weeks in January 2012 . Drums were recorded
+    in a day and a half .
+  - Contributors include actor Anton LaVey , Satanist Christopher Lee , serial killer
+    expert Clive Barker , author Karen Greenlee , and necrophile Robert Ressler .
+datasets:
+- redis/langcache-sentencepairs-v1
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: val
+      type: val
+    metrics:
+    - type: cosine_accuracy
+      value: 0.762879238548483
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8641344308853149
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.6906413705224409
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.826151430606842
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.6289324394017535
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.7657770800627943
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.7350886848165957
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.47694835496637344
+      name: Cosine Mcc
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: test
+      type: test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.7035036519888425
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8520702719688416
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.7118695167174169
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.8109757900238037
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.597953808752026
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8794040968342645
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6473233550443912
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.4409362621742405
+      name: Cosine Mcc
+---
+
+# 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-v1) 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:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
+- **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': 8192, '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 = [
+    'The band pursued `` signals `` in January 2012 in three weeks , and drums were recorded in a day and a half .',
+    'The band tracked `` Signals `` in three weeks in January 2012 . Drums were recorded in a day and a half .',
+    'Contributors include actor Anton LaVey , Satanist Christopher Lee , serial killer expert Clive Barker , author Karen Greenlee , and necrophile Robert Ressler .',
+]
+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, 0.9598, 0.4943],
+#         [0.9598, 0.9998, 0.5096],
+#         [0.4943, 0.5096, 1.0001]])
+```
+
+<!--
+### 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
+
+#### Binary Classification
+
+* Datasets: `val` and `test`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | val        | test       |
+|:--------------------------|:-----------|:-----------|
+| cosine_accuracy           | 0.7629     | 0.7035     |
+| cosine_accuracy_threshold | 0.8641     | 0.8521     |
+| cosine_f1                 | 0.6906     | 0.7119     |
+| cosine_f1_threshold       | 0.8262     | 0.811      |
+| cosine_precision          | 0.6289     | 0.598      |
+| cosine_recall             | 0.7658     | 0.8794     |
+| **cosine_ap**             | **0.7351** | **0.6473** |
+| cosine_mcc                | 0.4769     | 0.4409     |
+
+<!--
+## 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-v1)
+* Size: 62,021 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                             |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 27.46 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.36 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                   | sentence2                                                                                                                                     | label          |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                        | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                           | <code>1</code> |
+  | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
+  | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code>                       | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code>                | <code>1</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### LangCache Sentence Pairs (all)
+
+* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
+* Size: 62,021 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                             |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 27.46 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.36 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                   | sentence2                                                                                                                                     | label          |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                        | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                           | <code>1</code> |
+  | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
+  | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code>                       | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code>                | <code>1</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Logs
+| Epoch | Step | val_cosine_ap | test_cosine_ap |
+|:-----:|:----:|:-------------:|:--------------:|
+| -1    | -1   | 0.7351        | 0.6473         |
+
+
+### 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "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

@@ -0,0 +1,14 @@
+{
+  "__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"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f9247027e7d57e8b36440b5b3d10a785ded92c7c9f4a313ff7f54a549967290
+size 596070136

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 8192,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
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