Entity Matching Jobs

🚀 SentenceTransformer

This is a model trained with sentence-transformers. It maps sentences and paragraphs to a 1024-dimensional dense vector space, enabling applications such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

🚀 Quick Start

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

✨ Features

• Maps sentences and paragraphs to a 1024 - dimensional dense vector space.
• Applicable for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, etc.

📦 Installation

First, 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("engineai/entity_matching_jobs")
# Run inference
sentences = [
    'Nigh Auditor',
    'Night Auditor',
    'Security Shift Supervisor',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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()
)

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,997 training samples
• Columns: text_a, text_b, and label
• Approximate statistics based on the first 1000 samples: | | text_a | text_b | label | |---------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-----------------------------| | type | string | string | int | | details |
  • min: 3 tokens
  • mean: 5.66 tokens
  • max: 10 tokens
  |
  • min: 3 tokens
  • mean: 5.48 tokens
  • max: 12 tokens
  |
  • 1: 100.00%
  |
• Samples: | text_a | text_b | label | |--------------------------------------|----------------------------------------------|----------------| | Nrs | Nurse | 1 | | Nirse | Nurse | 1 | | Consumer Services Agent | Customer Service Representative | 1 |
• Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

• Size: 5,707 evaluation samples
• Columns: text_a, text_b, and label
• Approximate statistics based on the first 1000 samples: | | text_a | text_b | label | |---------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-----------------------------| | type | string | string | int | | details |
  • min: 3 tokens
  • mean: 5.69 tokens
  • max: 11 tokens
  |
  • min: 3 tokens
  • mean: 5.54 tokens
  • max: 15 tokens
  |
  • 1: 100.00%
  |
• Samples: | text_a | text_b | label | |-----------------------------------|--------------------------------------|----------------| | Catering Supervisor | Food Service Supervisor | 1 | | Catering Supervisor | Food Service Supervisor | 1 | | Cshier | Cashier | 1 |
• Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non - Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 200
• learning_rate: 4e - 05
• weight_decay: 0.01
• num_train_epochs: 40
• warmup_ratio: 0.2
• load_best_model_at_end: True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.1.0+cu118
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

📄 License

No license information provided.

📖 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",
}

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}
}