Scitopicnomicembed

🚀 nomic-ai/nomic-embed-text-v1.5

This model is a Sentence Transformer finetuned from nomic-ai/nomic-embed-text-v1.5 on the sci_topic_triplets dataset. It maps sentences and paragraphs to a 768 - dimensional dense vector space. This enables a wide range of applications, including semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

✨ Features

• Maps sentences and paragraphs to a 768 - dimensional dense vector space.
• Suitable for various tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

📦 Installation

First, you need to install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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]

📚 Documentation

Model Details

Model Description

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})
)

Evaluation

Metrics

Information Retrieval

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

Training Details

Training Dataset

• 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

• 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

📄 License

This model is licensed under the apache - 2.0 license.