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:5822
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: "VCH qualifies as a service-disabled veteran-owned small business\
+    \ and is an actual offeror on the \nSDVOSB Pool Solicitation under the Polaris\
+    \ Program.  VCH Compl. ¶¶ 2, 5.  Both SHS and VCH \nalso claim to be prospective\
+    \ offerors on the SB Pool Solicitation.  SHS Compl. ¶ 5; VCH Compl. \n¶ 5.  Both\
+    \ SHS and VCH state they have prepared, but not yet formally submitted, proposals\
+    \ in"
+  sentences:
+  - What type of request must be submitted according to the information security procedures?
+  - On which solicitation is VCH an actual offeror?
+  - According to which U.S. Code section is the term 'infrastructure security information'
+    defined?
+- source_sentence: "Count Nine in No. 11-444: May 13, 2010 FOIA Request to the CIA\
+    \ \nFirst, the CIA refused to process the plaintiff’s FOIA request which sought\
+    \ “a \nrepresentative sample of [CIA] analytical reports and memoranda presenting\
+    \ psychological \nanalyses or profiles of foreign government officials, terrorist\
+    \ leaders, international criminals,"
+  sentences:
+  - Under what section were 'Regional Boards' considered 'agencies'?
+  - What is the count number associated with the May 13, 2010 FOIA Request to the
+    CIA?
+  - What type of contracts does FAR 37.102(a) most prefer?
+- source_sentence: "records referencing FOIA and Privacy Act requests submitted by\
+    \ [ten listed parties] that contain \nremarks, comments, notes, explanations,\
+    \ etc. made by CIA personnel or contractors about the \nprocessing of these requests\
+    \ (and appeals, if appropriate), the invocations of exemptions, or \nrelated matters.”\
+    \  See Decl. of Martha M. Lutz (Sept. 26, 2012) (“Third Lutz Decl.”) Ex. A at\
+    \ 1,"
+  sentences:
+  - What is mentioned as possibly contradicting the defendant's statement?
+  - On what date is the document 'Third Lutz Decl.' declared?
+  - What does the plaintiff argue regarding the phrase 'on notice'?
+- source_sentence: "“[a]gencies shall evaluate all proposals in accordance with 15.305(a),\
+    \ and, if discussions are to be \nconducted, establish the competitive range.\
+    \  Based on the ratings of each proposal against all \nevaluation criteria, the\
+    \ contracting officer shall establish a competitive range comprised of all of\
+    \ \nthe most highly rated proposals . . . .”  FAR 15.306(c)(1) (emphasis added).\
+    \  This last provision"
+  sentences:
+  - Who establishes the competitive range based on proposal ratings?
+  - What type of basis are the services to be acquired on?
+  - What is the source of the statement quoted about the agencies and advisory committees?
+- source_sentence: "for a specific procurement through separate joint ventures with\
+    \ different protégés.”  Id.  The SBA \nunderscored this purpose by highlighting\
+    \ that in acquiring a second protégé, the mentor “has \nalready assured SBA that\
+    \ the two protégés would not be competitors.  If the two mentor-protégé \nrelationships\
+    \ were approved in the same [North American Industry Classification System] code,"
+  sentences:
+  - Where can the details of the CIA's framing of the plaintiff's injury be found?
+  - What is the context of the mentor-protégé relationships mentioned?
+  - What requirement does the FOIA have regarding the release of segregable portions
+    of a record?
+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: ModernBERT Embed base Legal Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5564142194744977
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6058732612055642
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6955177743431221
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7758887171561051
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5564142194744977
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.5265327150953116
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.40216383307573417
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.23879443585780524
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.19770736733642452
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5216383307573416
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6432251416795467
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.755280783101494
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6620393079072384
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6046944383111306
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6446423018095604
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5440494590417311
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5873261205564142
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6846986089644513
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7604327666151468
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5440494590417311
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.5105615662029882
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.3935085007727976
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.23431221020092735
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1947449768160742
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5066975785677487
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6317619783616693
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7434312210200927
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6493066767347394
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.591142268344741
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6320199198507571
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5131375579598145
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5548686244204019
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6383307573415765
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7078825347758887
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5131375579598145
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.4848016486347243
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.3712519319938176
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.2185471406491499
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18019062339000513
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.47797527047913446
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5926069036579084
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.693070582174137
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6070012758911372
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5553402762444491
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5963300240538415
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.45904173106646057
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5115919629057187
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5873261205564142
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6553323029366306
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.45904173106646057
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.43379701184956204
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.3372488408037094
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.2017001545595054
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.16679546625450797
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4330242143225141
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5422462648119527
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6388459556929419
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5563637742997848
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.50374622801207
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.546814426794716
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.36476043276661513
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.40494590417310666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.47449768160741884
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5409582689335394
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.36476043276661513
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3482740855229263
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.2741885625965997
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.16599690880989182
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1281555899021123
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.34119010819165374
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.4383049974240082
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5255023183925811
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.44955240085023923
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.40338436250337323
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.4452507749409145
+      name: Cosine Map@100
+---
+
+# ModernBERT Embed base Legal Matryoshka
+
+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) on the json dataset. 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:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **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}) 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("chrisekwugum/modernbert-embed-base-legal-matryoshka-2")
+# Run inference
+sentences = [
+    'for a specific procurement through separate joint ventures with different protégés.”  Id.  The SBA \nunderscored this purpose by highlighting that in acquiring a second protégé, the mentor “has \nalready assured SBA that the two protégés would not be competitors.  If the two mentor-protégé \nrelationships were approved in the same [North American Industry Classification System] code,',
+    'What is the context of the mentor-protégé relationships mentioned?',
+    "Where can the details of the CIA's framing of the plaintiff's injury be found?",
+]
+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   | 0.5564    | 0.544      | 0.5131    | 0.459      | 0.3648     |
+| cosine_accuracy@3   | 0.6059    | 0.5873     | 0.5549    | 0.5116     | 0.4049     |
+| cosine_accuracy@5   | 0.6955    | 0.6847     | 0.6383    | 0.5873     | 0.4745     |
+| cosine_accuracy@10  | 0.7759    | 0.7604     | 0.7079    | 0.6553     | 0.541      |
+| cosine_precision@1  | 0.5564    | 0.544      | 0.5131    | 0.459      | 0.3648     |
+| cosine_precision@3  | 0.5265    | 0.5106     | 0.4848    | 0.4338     | 0.3483     |
+| cosine_precision@5  | 0.4022    | 0.3935     | 0.3713    | 0.3372     | 0.2742     |
+| cosine_precision@10 | 0.2388    | 0.2343     | 0.2185    | 0.2017     | 0.166      |
+| cosine_recall@1     | 0.1977    | 0.1947     | 0.1802    | 0.1668     | 0.1282     |
+| cosine_recall@3     | 0.5216    | 0.5067     | 0.478     | 0.433      | 0.3412     |
+| cosine_recall@5     | 0.6432    | 0.6318     | 0.5926    | 0.5422     | 0.4383     |
+| cosine_recall@10    | 0.7553    | 0.7434     | 0.6931    | 0.6388     | 0.5255     |
+| **cosine_ndcg@10**  | **0.662** | **0.6493** | **0.607** | **0.5564** | **0.4496** |
+| cosine_mrr@10       | 0.6047    | 0.5911     | 0.5553    | 0.5037     | 0.4034     |
+| cosine_map@100      | 0.6446    | 0.632      | 0.5963    | 0.5468     | 0.4453     |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 5,822 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                            | anchor                                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                           |
+  | details | <ul><li>min: 28 tokens</li><li>mean: 97.21 tokens</li><li>max: 170 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.7 tokens</li><li>max: 39 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                              | anchor                                                                                          |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | <code>Counts Seven, Nine, and Ten in No. 11-445: February 6, 2010 FOIA  <br>            Requests to the CIA, State Department, and NSA <br>On February 6, 2010, the plaintiff submitted three substantially identical FOIA <br>requests—one to the CIA, one to the State Department, and one to the National Security Agency <br>(“NSA”).  The request to the CIA sought “all current training handbooks, manuals, guidelines,</code> | <code>What is the number associated with the case involving Counts Seven, Nine, and Ten?</code> |
+  | <code>The Government’s notion of a categorical principle stems mainly from a series of <br>decisions in this District.  Defs.’ Mem. at 14; Defs.’ Reply at 9 n.2.  The first was Gates v. <br>Schlesinger, 366 F. Supp. 797 (D.D.C. 1973), which stated that “an advisory committee is not an <br>‘agency.’”  Id. at 799. <br> <br>Gates’s first rationale for this conclusion was that FACA “utilizes the definition of</code>       | <code>From where does the Government's notion of a categorical principle mainly stem?</code>    |
+  | <code>sort its incoming FOIA requests based on fee categories.”  First Lutz Decl. ¶ 11.  The CIA’s <br>declarant also states that “this information [i.e., fee category] is not included in the electronic <br>system,” though the CIA’s declarant also avers that “[f]ee category is not a mandatory field,” and <br>thus “this information is often not included in a FOIA request record.”  Id.  The plaintiff focuses</code>      | <code>According to the CIA's declarant, is fee category a mandatory field?</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`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: False
+- `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`: 32
+- `per_device_eval_batch_size`: 16
+- `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`: False
+- `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 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.8791  | 10     | 91.392        | -                      | -                      | -                      | -                      | -                     |
+| 1.0     | 12     | -             | 0.6238                 | 0.6027                 | 0.5669                 | 0.5230                 | 0.4009                |
+| 1.7033  | 20     | 38.8819       | -                      | -                      | -                      | -                      | -                     |
+| 2.0     | 24     | -             | 0.6596                 | 0.6423                 | 0.5986                 | 0.5491                 | 0.4384                |
+| 2.5275  | 30     | 28.6263       | -                      | -                      | -                      | -                      | -                     |
+| **3.0** | **36** | **-**         | **0.6615**             | **0.6502**             | **0.6058**             | **0.5575**             | **0.4486**            |
+| 3.3516  | 40     | 25.2135       | -                      | -                      | -                      | -                      | -                     |
+| 3.7033  | 44     | -             | 0.6620                 | 0.6493                 | 0.6070                 | 0.5564                 | 0.4496                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.1
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### 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

@@ -0,0 +1,47 @@
+{
+  "_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": "4.48.3",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "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 sha256:8e068a21f2649606e1c7bd024521fde76ad8dfedf5ad61a7996226ebbdfd3511
+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": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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