yahyaabd / allstats-semantic-search-model-v1-3

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on the allstats-semantic-search-synthetic-dataset-v1 dataset. 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/paraphrase-multilingual-mpnet-base-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • allstats-semantic-search-synthetic-dataset-v1

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': 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})
)

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("yahyaabd/allstats-semantic-search-model-v1-3")
# Run inference
sentences = [
    'perubahan nilai tukar petani bulan mei 2017',
    'Perkembangan Nilai Tukar Petani Mei 2017',
    'Statistik Restoran/Rumah Makan Tahun 2014',
]
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]

Evaluation

Metrics

Semantic Similarity

• Datasets: allstats-semantic-search-v1-3-dev and allstat-semantic-search-v1-3-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

allstats-semantic-search-synthetic-dataset-v1

• Dataset: allstats-semantic-search-synthetic-dataset-v1 at b13c0a7
• Size: 212,940 training samples
• Columns: query , doc , and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 5 tokens mean: 11.46 tokens max: 34 tokens	min: 5 tokens mean: 14.47 tokens max: 54 tokens	min: 0.0 mean: 0.5 max: 1.05

• Samples:

  query	doc	label
  aDta industri besar dan sedang Indonesia 2008	Statistik Industri Besar dan Sedang Indonesia 2008	0.9
  profil bisnis konstruksi individu jawa barat 2022	Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku	0.15
  data statistik ekonomi indonesia	Nilai Tukar Valuta Asing di Indonesia 2014	0.08

• Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

allstats-semantic-search-synthetic-dataset-v1

• Dataset: allstats-semantic-search-synthetic-dataset-v1 at b13c0a7
• Size: 26,618 evaluation samples
• Columns: query , doc , and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 5 tokens mean: 11.38 tokens max: 34 tokens	min: 4 tokens mean: 14.63 tokens max: 55 tokens	min: 0.0 mean: 0.51 max: 1.0

• Samples:

  query	doc	label
  tahun berapa ekspor naik 2,37% dan impor naik 30,30%?	Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %	1.0
  Berapa produksi padi pada tahun 2023?	Produksi padi tahun lainnya	0.0
  data statistik solus per aqua 2015	Statistik Solus Per Aqua (SPA) 2015	0.97

• Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 32
• per_device_eval_batch_size : 32
• num_train_epochs : 8
• warmup_ratio : 0.1
• fp16 : True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.2.2+cu121
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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",
}