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:8760
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What is the interpretation described as inappropriate?
+  sentences:
+  - . Factors to be considered in determining the reasonableness of the lawyer’s expectation
+    of confidentiality include the sensitivity of the information and the extent to
+    which the privacy of the communication is protected by law or by a confidentiality
+    agreement
+  - . 20 of competition and rests on an inappropriate interpretation of SBA regulation
+    13 C.F.R. § 125.9(b)(3)(i). See SHS MJAR at 16–23; VCH MJAR at 16–23
+  - . 29-2, the CIA’s declaration explains in much more detail what is meant by “intelligence
+    sources and methods” or “intelligence activities,” see Third Lutz Decl. ¶–30
+- source_sentence: What is the source of the information regarding Senetas's knowledge
+    about FDA approval?
+  sentences:
+  - . . . the exemption under which the deletion is made, shall be indicated at the
+    place in the record where such deletion is made.” Id. Finally, the FOIA provides
+    that “a court shall accord substantial weight to an affidavit of an agency concerning
+    the agency’s determination as to technical feasibility under . . . subsection
+    (b).” Id. § 552(a)(4)(B)
+  - . 52 Senetas asserts that it learned about the plan to discontinue seeking FDA
+    approval for DR’s products in September of 2018 after the decision had been made
+    without any Board involvement. Galbally Dep. Tr. 66:19-23
+  - . Conclusion Video footage, like social media evidence, is susceptible to alteration,
+    and the increased availability of new technology, particularly the advent of image-generating
+    artificial intelligence, may present unique challenges in authenticating videos
+    and photographs
+- source_sentence: What does Class Deviation CD-2020-14 allow for at the contract
+    level?
+  sentences:
+  - social media company that 7At trial, the State had attempted to introduce evidence
+    that was purportedly a printout from the MySpace page of the girlfriend of the
+    defendant (whose nickname was allegedly “Boozy”) to demonstrate that the girlfriend
+    had threatened a State’s witness
+  - .” Supplement 2 to Class Deviation CD-2020-14 (Supplement 2), AR at 2904. The
+    Senior Procurement Executive further elaborated that Class Deviation CD-2020-14
+    “allowed for the use of ‘unpriced labor’ categories at the contract level for
+    certain IDIQ multiple-award contracts.” Id
+  - . Circuit has recognized that, separate from claims seeking relief for specific
+    requests made under the FOIA, requesting parties may also assert a “claim that
+    an agency policy or practice will impair the party’s lawful access to information
+    in the future.” Payne Enters., Inc. v. United States, 837 F.2d 486, 491 (D.C.
+    Cir. 1988) (emphasis in original); 31 accord Newport Aeronautical Sales v
+- source_sentence: What should the agency describe about the non-exempt material in
+    a document?
+  sentences:
+  - . A straightforward reading of the 2019 NDAA reveals that the Commission’s members
+    are “temporary” federal employees. The Commission “shall be considered . . . a
+    temporary organization under [5 U.S.C. § 3161].” Pub. L. No. 115-232, § 1051(a)(2).
+    The Commission’s 15 members are “appointed for the life of the Commission” and
+    are “Federal employees.” Id. § 1051(a)(4)(A), (6)–(7)
+  - .15 Posteriormente, en armonía con el marco constitucional y doctrinario previamente
+    reseñado, el 13 de julio de 2011, nuestra Legislatura aprobó, la Ley del Derecho
+    sobre la Propia Imagen o Ley Núm. 139-201116. Dicho precepto legal estatuye una
+    causa de acción en daños y perjuicios debido al uso no autorizado de la imagen
+    con fines comerciales o publicitarios
+  - . To this end, the Circuit has said that “[i]n addition to a statement of its
+    reasons, an agency should also describe what proportion of the information in
+    a document is non-exempt and how that material is dispersed throughout the document.”
+    Id
+- source_sentence: Which offeror is mentioned as getting in if there is a points discrepancy?
+  sentences:
+  - . at 9:14–19 (“[I]f an offeror does not have the same number of points, if it’s
+    the 130th offeror and it doesn’t have the same number of points as the 90th offeror,
+    then the solicitation says the 90th offeror gets in and the 130th doesn’t.”)
+  - '. But the State had to establish that the communications were the handiwork of
+    the defendant. It was in that context that temporal proximity came into play:
+    The timing of the communications relative to other events connecting the defendant
+    to the alleged crime was circumstantial evidence of the defendant’s authorship.
+    Id. at 674-76'
+  - . Since the plaintiff does not address this issue in its sur-reply brief in No.
+    11-445, and because the plaintiff does not ask the Court to direct the DOJ to
+    produce Document 3 to the plaintiff, the plaintiff does not appear to continue
+    to challenge the DOJ’s decision to withhold Document 3. 140 recorded decision
+    to implement the opinion.” Id. at 32
+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: 0.582135523613963
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7494866529774127
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.795687885010267
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8572895277207392
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.582135523613963
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24982888432580422
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1591375770020534
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08572895277207392
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.582135523613963
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7494866529774127
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.795687885010267
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8572895277207392
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7211793259435271
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6775296600501939
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6827316333877884
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5657084188911704
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7330595482546202
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7915811088295688
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8531827515400411
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5657084188911704
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24435318275154005
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15831622176591376
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08531827515400411
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5657084188911704
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7330595482546202
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7915811088295688
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8531827515400411
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7102670568981261
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6645362765229291
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6695389256684248
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5410677618069816
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7063655030800822
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7659137577002053
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8305954825462012
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5410677618069816
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2354551676933607
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15318275154004105
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08305954825462013
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5410677618069816
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7063655030800822
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7659137577002053
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8305954825462012
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6839216686374571
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6371842508392814
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6427516419970609
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4887063655030801
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6581108829568788
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7176591375770021
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7802874743326489
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4887063655030801
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2193702943189596
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14353182751540042
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07802874743326488
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4887063655030801
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6581108829568788
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7176591375770021
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7802874743326489
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6318826024721981
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5846004041589256
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5917468903182894
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3798767967145791
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5462012320328542
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6139630390143738
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.704312114989733
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.3798767967145791
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1820670773442847
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12279260780287474
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0704312114989733
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.3798767967145791
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5462012320328542
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6139630390143738
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.704312114989733
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5333651837657117
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4796983475114887
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.4877644055271696
+      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("AdamLucek/modernbert-embed-quickb")
+# Run inference
+sentences = [
+    'Which offeror is mentioned as getting in if there is a points discrepancy?',
+    '. at 9:14–19 (“[I]f an offeror does not have the same number of points, if it’s the 130th offeror and it doesn’t have the same number of points as the 90th offeror, then the solicitation says the 90th offeror gets in and the 130th doesn’t.”)',
+    '. Since the plaintiff does not address this issue in its sur-reply brief in No. 11-445, and because the plaintiff does not ask the Court to direct the DOJ to produce Document 3 to the plaintiff, the plaintiff does not appear to continue to challenge the DOJ’s decision to withhold Document 3. 140 recorded decision to implement the opinion.” Id. at 32',
+]
+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   | 0.5821     | 0.5657     | 0.5411     | 0.4887     | 0.3799     |
+| cosine_accuracy@3   | 0.7495     | 0.7331     | 0.7064     | 0.6581     | 0.5462     |
+| cosine_accuracy@5   | 0.7957     | 0.7916     | 0.7659     | 0.7177     | 0.614      |
+| cosine_accuracy@10  | 0.8573     | 0.8532     | 0.8306     | 0.7803     | 0.7043     |
+| cosine_precision@1  | 0.5821     | 0.5657     | 0.5411     | 0.4887     | 0.3799     |
+| cosine_precision@3  | 0.2498     | 0.2444     | 0.2355     | 0.2194     | 0.1821     |
+| cosine_precision@5  | 0.1591     | 0.1583     | 0.1532     | 0.1435     | 0.1228     |
+| cosine_precision@10 | 0.0857     | 0.0853     | 0.0831     | 0.078      | 0.0704     |
+| cosine_recall@1     | 0.5821     | 0.5657     | 0.5411     | 0.4887     | 0.3799     |
+| cosine_recall@3     | 0.7495     | 0.7331     | 0.7064     | 0.6581     | 0.5462     |
+| cosine_recall@5     | 0.7957     | 0.7916     | 0.7659     | 0.7177     | 0.614      |
+| cosine_recall@10    | 0.8573     | 0.8532     | 0.8306     | 0.7803     | 0.7043     |
+| **cosine_ndcg@10**  | **0.7212** | **0.7103** | **0.6839** | **0.6319** | **0.5334** |
+| cosine_mrr@10       | 0.6775     | 0.6645     | 0.6372     | 0.5846     | 0.4797     |
+| cosine_map@100      | 0.6827     | 0.6695     | 0.6428     | 0.5917     | 0.4878     |
+
+<!--
+## 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: 8,760 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.54 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 76.24 tokens</li><li>max: 169 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is being compared in the Circuit's statement?</code>                             | <code>.2d at 1389–90. The Circuit rejected this analogy, stating that, in contrast to the CIA Act, the NSA Act “protects not only organizational matters . . . but also ‘any information with respect to the activities’ of the NSA.” Id. at 1390</code>                                                                                                                                                               |
+  | <code>What type of internal documents used by the CIA in FOIA requests is mentioned?</code> | <code>. 108 Accordingly, the Court holds that certain specific categories of information withheld by the CIA in this case pursuant to § 403g clearly fall outside that provision’s scope, including (1) internal templates utilized by the CIA in tasking FOIA requests, (2) internal rules, policies and procedures governing FOIA processing, and (7) information about the CIA’s “core functions,” including</code> |
+  | <code>How many documents did the CIA withhold under Exemption 2?</code>                     | <code>. The CIA states in its declaration that all thirteen documents withheld under 38 The plaintiff previously indicated that it intended to challenge Exemption 2 withholding decisions made by the ODNI as well. See Hackett Decl. Ex. E at 1, ECF No. 29-8. The plaintiff, however, does not pursue that challenge in its opposition to the defendants’ motions for summary judgment in No. 11-445</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
+- `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`: 32
+- `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 |
+|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.5839     | 10     | 67.1727       | -                      | -                      | -                      | -                      | -                     |
+| 1.0        | 18     | -             | 0.6999                 | 0.6820                 | 0.6577                 | 0.5988                 | 0.4855                |
+| 1.1168     | 20     | 32.4667       | -                      | -                      | -                      | -                      | -                     |
+| 1.7007     | 30     | 27.9435       | -                      | -                      | -                      | -                      | -                     |
+| 2.0        | 36     | -             | 0.7167                 | 0.7002                 | 0.6764                 | 0.6233                 | 0.5187                |
+| 2.2336     | 40     | 22.2924       | -                      | -                      | -                      | -                      | -                     |
+| 2.8175     | 50     | 20.5125       | -                      | -                      | -                      | -                      | -                     |
+| 3.0        | 54     | -             | 0.7190                 | 0.7080                 | 0.6824                 | 0.6318                 | 0.5339                |
+| 3.3504     | 60     | 18.3621       | -                      | -                      | -                      | -                      | -                     |
+| **3.8175** | **68** | **-**         | **0.7212**             | **0.7103**             | **0.6839**             | **0.6319**             | **0.5334**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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

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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",
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+  "classifier_pooling": "mean",
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+  "decoder_bias": true,
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+  "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",
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+  "repad_logits_with_grad": false,
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+  "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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+oid sha256:31f4fe186ba37b53cd5a38aa632cffb0ed11ff885fdcb0021d380cd6e430bad2
+size 596070136

modules.json

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