language:

• en license: apache-2.0 tags:
• sentence-transformers
• sentence-similarity
• feature-extraction
• generated_from_trainer
• dataset_size:35964
• loss:MatryoshkaLoss
• loss:MultipleNegativesRankingLoss base_model: nomic-ai/nomic-embed-text-v1.5 widget:
• source_sentence: Despite the crucial role of phosphorus in global food production, there is a lack of comprehensive analysis on the economic and policy aspects of phosphorus supply and demand, highlighting a significant knowledge gap in the field of natural resource economics. sentences:
  • The human brain is intrinsically organized into dynamic, anticorrelated functional networks
  • 'The story of phosphorus: Global food security and food for thought'
  • Identifying a knowledge gap in the field of study
• source_sentence: Despite the comprehensive data sources used in this analysis, it is important to note that uncertainties remain in the estimation of global precipitation, particularly in data-sparse regions, and careful interpretation of the findings is advised. sentences:
  • The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar
  • Advising cautious interpretation of the findings
  • 'Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs'
• source_sentence: The study found that participants' value functions were characterized by loss aversion, risk aversion, and the concavity of the utility function in gains and the convexity in losses. sentences:
  • Ordered mesoporous molecular sieves synthesized by a liquid-crystal template mechanism
  • 'Prospect theory: An analysis of decision under risk'
  • Summarising the results section
• source_sentence: Further research is needed to explore the potential role of individual amino acids in optimizing protein intake and promoting optimal health outcomes. sentences:
  • Suggestions for future work
  • Validation of a modified Early Warning Score in medical admissions
  • Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein and Amino Acids
• source_sentence: The IANA Task Force (2021) builds upon previous research suggesting that slower gait speed is associated with increased risk of adverse outcomes in older adults (Levine et al., 2015; Schoenfeld et al., 2016). sentences:
  • 'Transdisciplinary research in sustainability science: practice, principles, and challenges'
  • Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force
  • Referring to another writer’s idea(s) or position datasets:
• Corran/SciTopicTriplets 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: nomic-ai/nomic-embed-text-v1.5 results:
  • task: type: information-retrieval name: Information Retrieval dataset: name: SciGen Eval Set type: SciGen-Eval-Set metrics:
    • type: cosine_accuracy@1 value: 0.19750889679715303 name: Cosine Accuracy@1
    • type: cosine_accuracy@3 value: 0.5547153024911032 name: Cosine Accuracy@3
    • type: cosine_accuracy@5 value: 0.81605871886121 name: Cosine Accuracy@5
    • type: cosine_accuracy@10 value: 0.9893238434163701 name: Cosine Accuracy@10
    • type: cosine_precision@1 value: 0.19750889679715303 name: Cosine Precision@1
    • type: cosine_precision@3 value: 0.1849051008303677 name: Cosine Precision@3
    • type: cosine_precision@5 value: 0.16321174377224199 name: Cosine Precision@5
    • type: cosine_precision@10 value: 0.098932384341637 name: Cosine Precision@10
    • type: cosine_recall@1 value: 0.19750889679715303 name: Cosine Recall@1
    • type: cosine_recall@3 value: 0.5547153024911032 name: Cosine Recall@3
    • type: cosine_recall@5 value: 0.81605871886121 name: Cosine Recall@5
    • type: cosine_recall@10 value: 0.9893238434163701 name: Cosine Recall@10
    • type: cosine_ndcg@10 value: 0.5663698287874538 name: Cosine Ndcg@10
    • type: cosine_mrr@10 value: 0.43265442297915546 name: Cosine Mrr@10
    • type: cosine_map@100 value: 0.433292401944685 name: Cosine Map@100

nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5 on the sci_topic_triplets 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/nomic-embed-text-v1.5
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • sci_topic_triplets
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Corran/SciTopicNomicEmbed")
# Run inference
sentences = [
    'The IANA Task Force (2021) builds upon previous research suggesting that slower gait speed is associated with increased risk of adverse outcomes in older adults (Levine et al., 2015; Schoenfeld et al., 2016).',
    'Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force',
    'Referring to another writer’s idea(s) or position',
]
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]

Evaluation

Metrics

Information Retrieval

• Dataset: SciGen-Eval-Set
• Evaluated with InformationRetrievalEvaluator

Training Details

Training Dataset

sci_topic_triplets

• Dataset: sci_topic_triplets at 8bf9936
• Size: 35,964 training samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 40.37 tokens max: 93 tokens	min: 5 tokens mean: 18.75 tokens max: 56 tokens	min: 5 tokens mean: 10.74 tokens max: 23 tokens

• Samples:

  query	positive	negative
  This study provides comprehensive estimates of life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death and 195 countries and territories from 1980 to 2015, allowing for a detailed understanding of global health trends and patterns over the past four decades.	Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015	Explaining the significance of the current study
  This paper explores the relationship between the expected value and the volatility of the nominal excess return on stocks using a econometric approach.	On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks	Stating the focus, aim, or argument of a short paper
  Despite the increasing attention given to the role of audit committees and board of directors in mitigating earnings management, several studies have reported inconclusive or even negative findings.	Audit committee, board of director characteristics, and earnings management	General reference to previous research or scholarship: highlighting negative outcomes

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Evaluation Dataset

sci_topic_triplets

• Dataset: sci_topic_triplets at 8bf9936
• Size: 4,495 evaluation samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 40.1 tokens max: 87 tokens	min: 5 tokens mean: 18.75 tokens max: 58 tokens	min: 5 tokens mean: 10.74 tokens max: 23 tokens

• Samples:

  query	positive	negative
  In this cluster-randomised controlled trial, the authors aimed to evaluate the effectiveness of introducing the Medical Emergency Team (MET) system in reducing response times and improving patient outcomes in emergency departments.	Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial	Some ways of introducing quotations
  In the data collection phase of our study, we employed both surveys and interviews as research methods. Specifically, we administered surveys to 200 participants and conducted interviews with 10 key industry experts to gather proportional data on various aspects of management science practices.	Research Methodology: A Step-by-Step Guide for Beginners	Surveys and interviews: Reporting proportions
  Several density functional theory (DFT) based chemical reactivity indexes, such as the Fukui functions and the electrophilic and nucleophilic indices, are discussed in detail for their ability to predict chemical reactivity.	Chemical reactivity indexes in density functional theory	General comments on the relevant literature

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}