Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:318
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What type of deal did Conan O'Brien sign with SiriusXM?
+  sentences:
+  - 'References:
+
+    - ([Andy Richter Net Worth | Celebrity Net Worth](https://www.celebritynetworth.com))
+
+
+    Media:
+
+    - image: https://upload.wikimedia.org/wikipedia/commons/8/8f/Conan_O%27Brien_and_Andy_Richter.jpg
+
+    - video: https://www.youtube.com/watch?v=WKLSAeIuK-8'
+  - "Description: Max Weinberg led the band on 'Late Night' and 'The Tonight Show\
+    \ with Conan O'Brien' before departing in 2010.\n- TBS-era bandleader jimmy_vivino\
+    \ (Strength: strong)\n  Description: Jimmy Vivino took over as bandleader on Conan's\
+    \ TBS show after Max Weinberg stepped away.\n- Spouse liza_powel_obrien (Strength:\
+    \ very strong)"
+  - "Relationships:\n- Owner team_coco (Strength: strong)\n  Description: SiriusXM\
+    \ owns Team Coco and its digital content library.\n- On-air talent conan_obrien\
+    \ (Strength: strong)\n  Description: Conan signed a multi-year deal with SiriusXM\
+    \ to produce content.\n\nAwards and Recognitions:\n- Leader in comedy audio"
+- source_sentence: What webcomic did Pierre Bernard co-create with Conan O'Brien?
+  sentences:
+  - 'Graph: Team Coco Knowledge Graph
+
+    Node ID: pierre_bernard
+
+    Category: people
+
+    Name: Pierre Bernard
+
+    Type: Person
+
+
+    Description: Pierre Bernard is a graphic artist recognized for his illustration
+    work on Conan O''Brien''s shows and for co-creating the ''Morrigan Webcomic''
+    with Conan.'
+  - "Relationships:\n- Host conan_obrien (Strength: creator)\n  Description: Conan\
+    \ developed his comedic voice on 'Late Night'.\n- Original sidekick andy_richter\
+    \ (Strength: core cast)\n  Description: Andy Richter contributed greatly to the\
+    \ show's dynamic.\n- Network nbc (Strength: parent)\n  Description: 'Late Night'\
+    \ was produced and aired on NBC.\n- Production company conaco (Strength: partner)"
+  - "Relationships:\n- Host & friend conan_obrien (Strength: very strong)\n  Description:\
+    \ Andy has been Conan's primary collaborator across multiple talk shows and tours.\n\
+    - Performer team_coco (Strength: strong)\n  Description: Andy is a prominent member\
+    \ of Team Coco, hosting live events and his own podcast series."
+- source_sentence: What type of entity is Liza Powel O'Brien classified as?
+  sentences:
+  - 'Graph: Team Coco Knowledge Graph
+
+    Node ID: 2010_nbc_conflict
+
+    Category: events
+
+    Name: 2010 Tonight Show Conflict
+
+    Type: Event
+
+
+    Description: A public and professional clash in 2010 when NBC reinstated Jay Leno
+    as host of ''The Tonight Show'', leading Conan to leave the network.
+
+
+    Relationships:
+
+    - Key figure conan_obrien
+
+    - Network nbc'
+  - "Relationships:\n- Late-night host conan_obrien (Strength: strong)\n  Description:\
+    \ Conan headlined TBS's late-night lineup for 11 years.\n- Digital content partner\
+    \ team_coco (Strength: strong)\n  Description: Team Coco produced many online\
+    \ segments during the TBS era.\n\nAwards and Recognitions:\n- Innovative blend\
+    \ of on-air and online content\n- Consistent ratings success"
+  - 'Graph: Team Coco Knowledge Graph
+
+    Node ID: liza_powel_obrien
+
+    Category: people
+
+    Name: Liza Powel O''Brien
+
+    Type: Person'
+- source_sentence: What is the name of the podcast production team?
+  sentences:
+  - 'Description: Smigel voices and operates the Triumph puppet in comedic sketches.'
+  - 'Major Events:
+
+    - 1954 ''Tonight'' Launch – Established late-night TV with ''The Tonight Show''.
+
+    - 2009 Transition – Briefly handed ''Tonight'' to Conan, then reinstated Leno.'
+  - "- Producer team_coco (Strength: parent)\n  Description: The podcast is produced\
+    \ by Team Coco in partnership with Stitcher's Earwolf."
+- source_sentence: What role did Andy Richter have when he joined 'Late Night'?
+  sentences:
+  - 'Graph: Team Coco Knowledge Graph
+
+    Node ID: jeff_ross_producer
+
+    Category: people
+
+    Name: Jeff Ross (Producer)
+
+    Type: Person
+
+
+    Description: Jeff Ross is a television producer who has served as Conan O''Brien''s
+    executive producer since 1993. He is a key business partner in Conan''s media
+    ventures and helped establish the Team Coco brand.'
+  - 'Collaboration Timeline:
+
+    - 1993: Joined Conan''s ''Late Night'' as sidekick ([Late Night with Conan O''Brien
+    - Wikipedia](https://en.wikipedia.org/wiki/Late_Night_with_Conan_O%27Brien))
+
+    - 2000: Left ''Late Night'' to pursue acting ([Andy Richter - Wikipedia](https://en.wikipedia.org/wiki/Andy_Richter))
+
+    - 2009: Reunited with Conan as announcer for NBC''s ''Tonight Show'''
+  - "Relationships:\n- Founder & figurehead conan_obrien (Strength: very strong)\n\
+    \  Description: Conan drives the creative vision of Team Coco.\n- Parent company\
+    \ siriusxm (Strength: strong)\n  Description: SiriusXM acquired Team Coco in 2022.\n\
+    - Production partner conaco (Strength: strong)\n  Description: Team Coco collaborates\
+    \ with Conaco to produce Conan's TV and streaming content."
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7222222222222222
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8611111111111112
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8888888888888888
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7222222222222222
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2870370370370371
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1777777777777778
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7222222222222222
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8611111111111112
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8888888888888888
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8504185091821412
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8041225749559083
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8041225749559082
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6944444444444444
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8611111111111112
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6944444444444444
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777785
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17222222222222225
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6944444444444444
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8611111111111112
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8349549866777143
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7842151675485008
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7842151675485008
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6944444444444444
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8611111111111112
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6944444444444444
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777785
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17222222222222225
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6944444444444444
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8611111111111112
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8363970656972288
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7866071428571428
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7866071428571428
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6111111111111112
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7777777777777778
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8055555555555556
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9722222222222222
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6111111111111112
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2592592592592593
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16111111111111112
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09722222222222224
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6111111111111112
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7777777777777778
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8055555555555556
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9722222222222222
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7747574471542903
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7143077601410934
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7158509700176366
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6944444444444444
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8055555555555556
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9166666666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.23148148148148145
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16111111111111112
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09166666666666669
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6944444444444444
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8055555555555556
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9166666666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6915040161197078
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6216049382716049
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6252403054619593
+      name: Cosine Map@100
+---
+
+# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). It maps sentences & paragraphs to a 768-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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("densonsmith/modernbert-embed-quickb")
+# Run inference
+sentences = [
+    "What role did Andy Richter have when he joined 'Late Night'?",
+    "Collaboration Timeline:\n- 1993: Joined Conan's 'Late Night' as sidekick ([Late Night with Conan O'Brien - Wikipedia](https://en.wikipedia.org/wiki/Late_Night_with_Conan_O%27Brien))\n- 2000: Left 'Late Night' to pursue acting ([Andy Richter - Wikipedia](https://en.wikipedia.org/wiki/Andy_Richter))\n- 2009: Reunited with Conan as announcer for NBC's 'Tonight Show'",
+    "Graph: Team Coco Knowledge Graph\nNode ID: jeff_ross_producer\nCategory: people\nName: Jeff Ross (Producer)\nType: Person\n\nDescription: Jeff Ross is a television producer who has served as Conan O'Brien's executive producer since 1993. He is a key business partner in Conan's media ventures and helped establish the Team Coco brand.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512   | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.7222     | 0.6944    | 0.6944     | 0.6111     | 0.5        |
+| cosine_accuracy@3   | 0.8611     | 0.8333    | 0.8333     | 0.7778     | 0.6944     |
+| cosine_accuracy@5   | 0.8889     | 0.8611    | 0.8611     | 0.8056     | 0.8056     |
+| cosine_accuracy@10  | 1.0        | 1.0       | 1.0        | 0.9722     | 0.9167     |
+| cosine_precision@1  | 0.7222     | 0.6944    | 0.6944     | 0.6111     | 0.5        |
+| cosine_precision@3  | 0.287      | 0.2778    | 0.2778     | 0.2593     | 0.2315     |
+| cosine_precision@5  | 0.1778     | 0.1722    | 0.1722     | 0.1611     | 0.1611     |
+| cosine_precision@10 | 0.1        | 0.1       | 0.1        | 0.0972     | 0.0917     |
+| cosine_recall@1     | 0.7222     | 0.6944    | 0.6944     | 0.6111     | 0.5        |
+| cosine_recall@3     | 0.8611     | 0.8333    | 0.8333     | 0.7778     | 0.6944     |
+| cosine_recall@5     | 0.8889     | 0.8611    | 0.8611     | 0.8056     | 0.8056     |
+| cosine_recall@10    | 1.0        | 1.0       | 1.0        | 0.9722     | 0.9167     |
+| **cosine_ndcg@10**  | **0.8504** | **0.835** | **0.8364** | **0.7748** | **0.6915** |
+| cosine_mrr@10       | 0.8041     | 0.7842    | 0.7866     | 0.7143     | 0.6216     |
+| cosine_map@100      | 0.8041     | 0.7842    | 0.7866     | 0.7159     | 0.6252     |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 318 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 318 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 14.24 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 74.8 tokens</li><li>max: 117 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                          | positive                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the Node ID of TBS?</code>                                                                        | <code>Graph: Team Coco Knowledge Graph<br>Node ID: tbs<br>Category: organizations<br>Name: TBS<br>Type: Organization<br><br>Description: TBS is the cable network that hosted Conan O'Brien's late-night show 'Conan' from 2010 until 2021.</code>                                                                                                                                                                   |
+  | <code>Where can information about 'The Legally Prohibited from Being Funny on Television Tour' be found?</code> | <code>References:<br>- ([The Legally Prohibited from Being Funny on Television Tour - Wikipedia](https://en.wikipedia.org/wiki/The_Legally_Prohibited_from_Being_Funny_on_Television_Tour))<br>- ([15 Years Ago, Conan O'Brien's 'Tonight Show'...](https://www.tvinsider.com))</code>                                                                                                                               |
+  | <code>What is the strength classification for the creator & performer triumph_insult_comic_dog?</code>          | <code>Relationships:<br>- Creative collaborator conan_obrien (Strength: strong)<br>  Description: Robert helped shape Conan's early comedy, including the creation of signature characters.<br>- Instrumental in sidekick role andy_richter (Strength: moderate)<br>  Description: Smigel recognized Andy's on-camera chemistry with Conan.<br>- Creator & performer triumph_insult_comic_dog (Strength: N/A)</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 4
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 1.0     | 5      | -             | 0.7899                 | 0.7843                 | 0.7653                 | 0.7110                 | 0.6216                |
+| 2.0     | 10     | 18.3776       | 0.8343                 | 0.8248                 | 0.8061                 | 0.7663                 | 0.6764                |
+| **3.0** | **15** | **-**         | **0.8504**             | **0.835**              | **0.8226**             | **0.7748**             | **0.6894**            |
+| 4.0     | 20     | 6.1577        | 0.8504                 | 0.8350                 | 0.8364                 | 0.7748                 | 0.6915                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.4
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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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+{
+  "_name_or_path": "nomic-ai/modernbert-embed-base",
+  "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,
+  "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",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30007d809f0dc784f184bff2181f312b34b89a55bb3f690aaccf00c3aaa6bb3f
+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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
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+    "rstrip": false,
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+  "unk_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
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