JuanIgnacioSolerno's picture
Add new SentenceTransformer model.
4974cd7 verified
metadata
language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:10K<n<100K
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
  - source_sentence: Tech Writer
    sentences:
      - Tech Specialist
      - Architectural Historian
      - Order Selector (Picker)
  - source_sentence: Controller
    sentences:
      - Assistant Controller
      - Key Accounts Supervisor
      - Cosmetologist - Plus Tips
  - source_sentence: Accountant
    sentences:
      - Financial Accountant
      - Manager, Corporate Sales
      - Materials Sourcing Lead
  - source_sentence: Planner III
    sentences:
      - Strategist
      - Product Finance Manager
      - Materials Sourcing Lead
  - source_sentence: AP Analyst
    sentences:
      - AP Specialist
      - ESCO Service Coordinator
      - Boiler Tender Lead
pipeline_tag: sentence-similarity

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
  • Maximum Sequence Length: 384 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

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]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 11,923 training samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 3 tokens
    • mean: 7.17 tokens
    • max: 27 tokens
    • min: 4 tokens
    • mean: 4.0 tokens
    • max: 4 tokens
    • min: 0.0
    • mean: 0.04
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    Land Coordinator, Renewable Development Energy Analyst 0.0
    Customer Service Advocate - Remote within the state of Colorado Energy Analyst 0.0
    Global Head of Infrastructure Energy Analyst 0.0
  • Loss: CosineSimilarityLoss with these parameters:
    {
        "loss_fct": "torch.nn.modules.loss.MSELoss"
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 2,981 evaluation samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 3 tokens
    • mean: 7.21 tokens
    • max: 28 tokens
    • min: 4 tokens
    • mean: 4.0 tokens
    • max: 4 tokens
    • min: 0.0
    • mean: 0.05
    • max: 1.0
  • Samples:
    sentence1 sentence2 score
    IT Data Coordinator - Customer Data & Integrations Team Energy Analyst 0.0
    Warehouse Associate Energy Analyst 0.0
    Human Resources Manager Energy Analyst 0.0
  • Loss: CosineSimilarityLoss 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",
}