SIRIS-Lab / affilgood-dense-retriever

SentenceTransformer

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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 1024 dimensions
• 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': 128, '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()
)

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("SIRIS-Lab/affilgood-dense-retriever")
# Run inference
sentences = [
    '[MENTION] Hyderabad Cleft Society [COUNTRY] India',
    '[MENTION] Hyderabad Cleft Society [ACRONYM] HCS [CITY] Hyderabad [COUNTRY] India',
    '[MENTION] Hyderabad Rheumatology Center [ACRONYM] HRC [CITY] Hyderabad [COUNTRY] India',
]
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]

Evaluation

Metrics

Semantic Similarity

• Dataset: entity_linking_eval
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 47,610 training samples
• Columns: sentence_0 , sentence_1 , and sentence_2
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 5 tokens mean: 13.61 tokens max: 32 tokens	min: 9 tokens mean: 18.63 tokens max: 56 tokens	min: 10 tokens mean: 19.66 tokens max: 58 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  [MENTION] The Prince Of Wales'S Institute Of Architecture [CITY] London [COUNTRY] United Kingdom	[MENTION] The Princes Foundation [CITY] London [COUNTRY] United Kingdom	[MENTION] Royal Institute of British Architects [ACRONYM] RIBA [CITY] London [COUNTRY] United Kingdom
  [MENTION] Development Finance & Public Policies [COUNTRY] Belgium	[MENTION] Development Finance and Public Policies [ACRONYM] DEFIPP [PARENT] University of Namur [CITY] Namur [COUNTRY] Belgium	[MENTION] Service Public Federal Finances [ACRONYM] SPF [CITY] Brussels [COUNTRY] Belgium
  [MENTION] EES [COUNTRY] United States	[MENTION] Emerald Education Systems [ACRONYM] EES [CITY] Pasadena [COUNTRY] United States	[MENTION] ESI Group (United States) [ACRONYM] ESI [PARENT] ESI Group (France) [ACRONYM] ESI [CITY] Farmington Hills [COUNTRY] United States

• 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 : 16
• per_device_eval_batch_size : 16
• fp16 : True
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.1
• Transformers: 4.41.2
• PyTorch: 2.2.0+cu121
• Accelerate: 1.2.1
• Datasets: 2.18.0
• 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",
}

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}
}