yahyaabd / allstats-semantic-search-mini-model-v2-2

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the allstats-semantic-search-synthetic-dataset-v2-mini dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • allstats-semantic-search-synthetic-dataset-v2-mini

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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-mini-model-v2-2")
# Run inference
sentences = [
    'Perdagangan luar negeri, impor, Oktober 2020',
    'Indikator Ekonomi November 1999',
    'Indikator Ekonomi September 2005',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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-mini-v2-eval and allstat-semantic-search-mini-v2-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

allstats-semantic-search-synthetic-dataset-v2-mini

• Dataset: allstats-semantic-search-synthetic-dataset-v2-mini at 8222b01
• Size: 70,280 training samples
• Columns: query , doc , and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 3 tokens mean: 10.92 tokens max: 50 tokens	min: 4 tokens mean: 14.68 tokens max: 59 tokens	min: 0.0 mean: 0.52 max: 1.0

• Samples:

  query	doc	label
  Statistik perusahaan pembudidaya tanaman kehutanan 2018	Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018	0.97
  Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002?	Inflasi Bulan November 2002 Sebesar 1,85 %	0.0
  Perdagangan luar negeri Indonesia, impor 2019, jilid 2	Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat	0.06

• Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

allstats-semantic-search-synthetic-dataset-v2-mini

• Dataset: allstats-semantic-search-synthetic-dataset-v2-mini at 8222b01
• Size: 15,060 evaluation samples
• Columns: query , doc , and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 4 tokens mean: 10.96 tokens max: 48 tokens	min: 4 tokens mean: 14.74 tokens max: 70 tokens	min: 0.0 mean: 0.5 max: 1.0

• Samples:

  query	doc	label
  Review PDRB daerah di Pulau Sumatera 2010-2013	Statistik Pendidikan 2006	0.12
  Analisis data angkatan kerja Agustus 2021	Booklet Survei Angkatan Kerja Nasional Agustus 2021	0.9
  Berapa persen inflasi yang terjadi pada Juli 2015?	Inflasi pada bulan lain tidak disebutkan	0.0

• 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 : 64
• per_device_eval_batch_size : 64
• num_train_epochs : 24
• warmup_ratio : 0.1
• bf16 : True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.2.0
• 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",
}