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

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.

python

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:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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

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",
}

	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

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`