all-mpnet-base-v2-sts

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2 <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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:

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("JuanIgnacioSolerno/all-mpnet-base-v2-sts")
# Run inference
sentences = [
    'AP Analyst',
    'AP Specialist',
    'ESCO Service Coordinator',
]
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]

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Training Details

Training Dataset

Unnamed Dataset

• Size: 11,923 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 3 tokens</li><li>mean: 7.17 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>Land Coordinator, Renewable Development</code>	<code>Energy Analyst</code>	<code>0.0</code>
  <code>Customer Service Advocate - Remote within the state of Colorado</code>	<code>Energy Analyst</code>	<code>0.0</code>
  <code>Global Head of Infrastructure</code>	<code>Energy Analyst</code>	<code>0.0</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 2,981 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 3 tokens</li><li>mean: 7.21 tokens</li><li>max: 28 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.05</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>IT Data Coordinator - Customer Data & Integrations Team</code>	<code>Energy Analyst</code>	<code>0.0</code>
  <code>Warehouse Associate</code>	<code>Energy Analyst</code>	<code>0.0</code>
  <code>Human Resources Manager</code>	<code>Energy Analyst</code>	<code>0.0</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.0.0
• Transformers: 4.41.2
• PyTorch: 2.0.0.post200
• Accelerate: 0.30.1
• Datasets: 2.19.1
• Tokenizers: 0.19.1

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",
}

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