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

@@ -0,0 +1,855 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1567
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: How many authors are listed for the trial?
+  sentences:
+  - 'chemotherapy and bone marrow transplantation for certain malignancies and has
+    a long track
+
+    record of safe use in adults and children. The incidence of adverse events such
+    as fever, chills,
+
+    bone pain, dyspnea, tachycardia, and hemodynamic instability was no different
+    between GM-
+
+    CSF and placebo-treated groups in controlled adult BMT studies. Rapid IV administration
+    of'
+  - 'clinical ICU staff in accordance with institutional practice and judgment.
+
+    Child Assent Subjects who are eligible for this study will be critically ill,
+    and child assent is
+
+    typically not possible at the time of study enrollment. However, during follow
+    up after discharge
+
+    from the ICU, issues about assent become applicable. Children who are capable
+    of giving assent'
+  - 'Controlled Phase 2 Trial. Stroke, 49(5):1210–1216, 2018.
+
+    [76] M. K. R. Somagutta, M. K. Lourdes Pormento, P. Hamid, A. Hamdan, M. A. Khan,
+
+    R. Desir, R. Vijayan, S. Shirke, R. Jeyakumar, Z. Dogar, S. S. Makkar, P. Guntipalli,
+
+    N. N. Ngardig, M. S. Nagineni, T. Paul, E. Luvsannyam, C. Riddick, and M. A. Sanchez-'
+- source_sentence: What type of event can lead to the suspension of enrollment in
+    the study?
+  sentences:
+  - 'and data generated by this study must be available for inspection upon request
+    by representatives
+
+    (when applicable) of the Food and Drug Administration (FDA), NIH, other Federal
+    funders or
+
+    study sponsors, and the Institutional Review Board (IRB) for each study site.
+
+    9 Protection of Human Subjects
+
+    9.1 Risks to Human Subjects
+
+    9.1.1 Human Subjects Involvement and Characteristics'
+  - 'two consecutive days while receiving study drug, the drug will be discontinued.
+
+    Adverse events will be monitored as described in Section 10.2.6 on page 61. The
+    medical
+
+    monitor has the authority to suspend enrollment in the event of an unexpected,
+    study-related
+
+    serious adverse event that is judged to change the risk/benefit of subject participation.'
+  - 'innate immune system is common and measurable in pediatric sepsis. Innate immune
+    cells such
+
+    as monocytes and neutrophils serve critical functions including migration to sites
+    of infection,
+
+    phagocytosis of pathogens, promotion of microbial killing, antigen presentation,
+    and production
+
+    of immunomodulatory cytokines. We have repeatedly shown that severe reduction
+    in the ability'
+- source_sentence: When will the reviews start?
+  sentences:
+  - 'mg/kg/day given for three days by continuous infusion was used.23, 63 Despite
+    its apparent safety
+
+    in adults, this dose is substantially higher than what has been used in children
+    with HLH/MAS
+
+    or adults with COVID-19.
+
+    In the largest (to date) published study of anakinra in hospitalized, hyper-inflamed
+    adults
+
+    with COVID-19 (N=392), a dose of 10 mg/kg/day IV divided every 12 hours (infused
+    over 1'
+  - 'data are required for Federal reporting purposes to delineate subject accrual
+    by race, ethnicity,
+
+    and gender.
+
+    For purposes of the DCC handling potential protected health information (PHI)
+    and pro-
+
+    ducing the de–identified research data sets that will be used for analyses, all
+    study sites have
+
+    been offered a Business Associate Agreement with the University of Utah. Copies
+    of executed'
+  - 'empirically whether these patients differ from those remaining in the study for
+    the scheduled
+
+    treatment and follow-up time. Missingness for primary, secondary, exploratory,
+    and safety
+
+    outcomes will be reviewed in aggregate and by site. Reviews will start as soon
+    as enrollment
+
+    opens and will be regulatory monitored so missing data problems can be addressed
+    early in the
+
+    study.'
+- source_sentence: What type of results will be communicated to the Data Coordinating
+    Center and clinical site investigator?
+  sentences:
+  - 'ing of a medical condition that was present at the time of randomization will
+    be considered a
+
+    new adverse event and reported.
+
+    After patient randomization all adverse events (including serious adverse events)
+    will be
+
+    recorded according to relatedness, severity, and expectedness, as well as their
+    duration and'
+  - '12.2 Health Insurance Portability and Accountability Act
+
+    Data elements collected include the date of birth and date of admission. Prior
+    to statistical
+
+    analyses, dates will be used to calculate patient age at the time of the study
+    events.
+
+    Data elements for race, ethnicity, and gender are also being collected. These
+    demographic'
+  - 'The Collaborative Pediatric Critical Care Research NetworkPage 34 of 76 Protocol
+    90 (Hall, Zuppa and Mourani)
+
+    4.5 Randomization
+
+    Upon determination of a subject’s immunophenotype, Dr. Hall or his designee will
+    notify the
+
+    Data Coordinating Center and the clinical site investigator of the laboratory
+    results. Subjects'
+- source_sentence: What age groups will be enrolled in the study?
+  sentences:
+  - 'have mild to moderate inflammation (i.e. a serum ferritin level <2,000 ng/ml)
+    from the TRIPS
+
+    trial. Those subjects will be instead entered into a completely distinct clinical
+    trial of immune
+
+    stimulation with GM-CSF (GRACE-2) that is covered by a separate IND (#112277).
+
+    PRECISE Protocol Version 1.07
+
+    Protocol Version Date: June 16, 2023'
+  - 'Subject Population to be Studied Participating sites will enroll infants, children
+    and adoles-
+
+    cent patients who are admitted to a Pediatric or Cardiac Intensive Care Unit with
+    sepsis-induced
+
+    multiple organ dysfunction syndrome (MODS). The goal is to determine if personalized
+    im-
+
+    munomodulation is an effective strategy to reduce mortality and morbidity from
+    sepsis-induced'
+  - 'Loosdregt, N. M. Wulffraat, S. de Roock, and S. J. Vastert. Treatment to target
+    using
+
+    recombinant interleukin-1 receptor antagonist as first-line monotherapy in new-onset
+
+    systemic juvenile idiopathic arthritis: Results from a five-year follow-up study.
+    Arthritis
+
+    Rheumatol, 71(7):1163–1173, 2019.
+
+    [78] R. K. Thakkar, R. Devine, J. Popelka, J. Hensley, R. Fabia, J. A. Muszynski,
+    and M. W.'
+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: 0.5714285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7828571428571428
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8114285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8742857142857143
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5714285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2609523809523809
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16228571428571423
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08742857142857141
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5714285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7828571428571428
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8114285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8742857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7304617900805063
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6836485260770975
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6898282619821292
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5485714285714286
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7885714285714286
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8285714285714286
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8685714285714285
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5485714285714286
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2628571428571428
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16571428571428568
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08685714285714283
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5485714285714286
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7885714285714286
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8285714285714286
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8685714285714285
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7172419802927883
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6675759637188208
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6741729815259775
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5485714285714286
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.76
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.84
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9085714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5485714285714286
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2533333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16799999999999995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09085714285714283
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5485714285714286
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.76
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.84
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9085714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7268936400245406
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6687596371882085
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6719911574054431
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.49142857142857144
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7028571428571428
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7885714285714286
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8685714285714285
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.49142857142857144
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.23428571428571424
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15771428571428567
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08685714285714284
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.49142857142857144
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7028571428571428
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7885714285714286
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8685714285714285
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6778419592624233
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6168730158730158
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6219971103464577
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.38285714285714284
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5714285714285714
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6571428571428571
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7885714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.38285714285714284
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19047619047619044
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1314285714285714
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07885714285714283
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.38285714285714284
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5714285714285714
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6571428571428571
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7885714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5697625172066919
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5015079365079367
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5090522718083348
+      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("Mdean77/modernbert-embed-quickb")
+# Run inference
+sentences = [
+    'What age groups will be enrolled in the study?',
+    'Subject Population to be Studied Participating sites will enroll infants, children and adoles-\ncent patients who are admitted to a Pediatric or Cardiac Intensive Care Unit with sepsis-induced\nmultiple organ dysfunction syndrome (MODS). The goal is to determine if personalized im-\nmunomodulation is an effective strategy to reduce mortality and morbidity from sepsis-induced',
+    'have mild to moderate inflammation (i.e. a serum ferritin level <2,000 ng/ml) from the TRIPS\ntrial. Those subjects will be instead entered into a completely distinct clinical trial of immune\nstimulation with GM-CSF (GRACE-2) that is covered by a separate IND (#112277).\nPRECISE Protocol Version 1.07\nProtocol Version Date: June 16, 2023',
+]
+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   | 0.5714     | 0.5486     | 0.5486     | 0.4914     | 0.3829     |
+| cosine_accuracy@3   | 0.7829     | 0.7886     | 0.76       | 0.7029     | 0.5714     |
+| cosine_accuracy@5   | 0.8114     | 0.8286     | 0.84       | 0.7886     | 0.6571     |
+| cosine_accuracy@10  | 0.8743     | 0.8686     | 0.9086     | 0.8686     | 0.7886     |
+| cosine_precision@1  | 0.5714     | 0.5486     | 0.5486     | 0.4914     | 0.3829     |
+| cosine_precision@3  | 0.261      | 0.2629     | 0.2533     | 0.2343     | 0.1905     |
+| cosine_precision@5  | 0.1623     | 0.1657     | 0.168      | 0.1577     | 0.1314     |
+| cosine_precision@10 | 0.0874     | 0.0869     | 0.0909     | 0.0869     | 0.0789     |
+| cosine_recall@1     | 0.5714     | 0.5486     | 0.5486     | 0.4914     | 0.3829     |
+| cosine_recall@3     | 0.7829     | 0.7886     | 0.76       | 0.7029     | 0.5714     |
+| cosine_recall@5     | 0.8114     | 0.8286     | 0.84       | 0.7886     | 0.6571     |
+| cosine_recall@10    | 0.8743     | 0.8686     | 0.9086     | 0.8686     | 0.7886     |
+| **cosine_ndcg@10**  | **0.7305** | **0.7172** | **0.7269** | **0.6778** | **0.5698** |
+| cosine_mrr@10       | 0.6836     | 0.6676     | 0.6688     | 0.6169     | 0.5015     |
+| cosine_map@100      | 0.6898     | 0.6742     | 0.672      | 0.622      | 0.5091     |
+
+<!--
+## 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: 1,567 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: 8 tokens</li><li>mean: 15.03 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 90.85 tokens</li><li>max: 185 tokens</li></ul> |
+* Samples:
+  | anchor                                                                       | positive                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:-----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How many terabytes of data are referenced?</code>                      | <code>over 125 terabytes of data.<br>Information systems are available 24/7/365 unless a scheduled maintenance period or<br>mitigation of an unexpected event is required. Critical systems availability has exceeded 99.9%<br>for the past 5 years.<br>7.2.3 Security, Support, Encryption, and Confidentiality<br>The data center coordinates the network infrastructure and security with University Information</code>              |
+  | <code>What regulation allows single parent permission for the study?</code>  | <code>for their child in the study. Single parent permission is permitted under 45 CFR §46.405. The<br>parent or legal guardian will be informed about the objectives of the study and the potential<br>risks and benefits of their child’s participation. If the parent or legal guardian refuses permission<br>for their child to participate, then all clinical management will continue to be provided by the</code>                |
+  | <code>What is included in the follow-up plan for non-compliant sites?</code> | <code>planned site visits, criteria for focused visits, additional visits or remote monitoring, a plan for<br>chart review and a follow up plan for non-compliant sites. The monitoring plan also describes<br>the type of monitoring that will take place (e.g., sample of all subjects within a site; key data or<br>all data), the schedule of visits, how they are reported and a time frame to resolve any issues<br>found.</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`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: False
+- `load_best_model_at_end`: True
+- `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`: 16
+- `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`: False
+- `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
+- `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        | 7      | -             | 0.6698                 | 0.6606                 | 0.6458                 | 0.6146                 | 0.5049                |
+| 1.4898     | 10     | 55.7211       | -                      | -                      | -                      | -                      | -                     |
+| 2.0        | 14     | -             | 0.7210                 | 0.7080                 | 0.7183                 | 0.6653                 | 0.5621                |
+| 2.9796     | 20     | 26.9161       | -                      | -                      | -                      | -                      | -                     |
+| 3.0        | 21     | -             | 0.7309                 | 0.7172                 | 0.7262                 | 0.6762                 | 0.5694                |
+| **3.4898** | **24** | **-**         | **0.7305**             | **0.7172**             | **0.7269**             | **0.6778**             | **0.5698**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.6.0
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- 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",
+  "classifier_bias": false,
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+  "classifier_pooling": "mean",
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+  "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": false,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0"
+  },
+  "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:c6ce0148002922bf5e359f1bc8bdec35d15cc554b250144d023afb333a12b7b1
+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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+    "rstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
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