allstats-search-mini-v1-1-mnrl-v2

SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl

This is a sentence-transformers model finetuned from yahyaabd/allstats-search-mini-v1-1-mnrl on the bps-pub-cosine-pairs 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: yahyaabd/allstats-search-mini-v1-1-mnrl <!-- at revision 117ddf58a25bdde8ba44b3c0e1bff6582bc34d17 -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • bps-pub-cosine-pairs

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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-search-mini-v1-1-mnrl-v2")
# Run inference
sentences = [
    'q-4068',
    'Berapa persentase rumah tangga dengan akses sanitasi layak?',
    '43a5856225b1ff1cb95e319a',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Semantic Similarity

• Datasets: sts-dev and sts-test
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

bps-pub-cosine-pairs

• Dataset: bps-pub-cosine-pairs at d58662e
• Size: 8,082 training samples
• Columns: <code>query_id</code>, <code>query</code>, <code>corpus_id</code>, <code>title</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	query_id	query	corpus_id	title	score
  type	string	string	string	string	float
  details	<ul><li>min: 4 tokens</li><li>mean: 5.21 tokens</li><li>max: 6 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 17.4 tokens</li><li>max: 23 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 13.02 tokens</li><li>max: 43 tokens</li></ul>	<ul><li>min: 0.1</li><li>mean: 0.55</li><li>max: 0.9</li></ul>

• Samples:

  query_id	query	corpus_id	title	score
  <code>q-1599</code>	<code>Nilai Tukar Nelayan</code>	<code>0b0da8fc2b6af9329a6d9cfe</code>	<code>Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013</code>	<code>0.1</code>
  <code>q-3595</code>	<code>Berapa angka statistik pertambangan non migas Indonesia periode 2012?</code>	<code>3c83610c3e2e5242177e2b11</code>	<code>Statistik Pertambangan Non Minyak dan Gas Bumi 2011-2015</code>	<code>0.9</code>
  <code>q-9112</code>	<code>Bagaimana situasi angkatan kerja Indonesia di bulan Februari 2021?</code>	<code>b547a5642aeb04d071cb83d4</code>	<code>Keadaan Angkatan Kerja di Indonesia Februari 2021</code>	<code>0.9</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

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

Evaluation Dataset

bps-pub-cosine-pairs

• Dataset: bps-pub-cosine-pairs at d58662e
• Size: 1,010 evaluation samples
• Columns: <code>query_id</code>, <code>query</code>, <code>corpus_id</code>, <code>title</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	query_id	query	corpus_id	title	score
  type	string	string	string	string	float
  details	<ul><li>min: 4 tokens</li><li>mean: 5.22 tokens</li><li>max: 6 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 11.19 tokens</li><li>max: 31 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 17.25 tokens</li><li>max: 23 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 13.24 tokens</li><li>max: 44 tokens</li></ul>	<ul><li>min: 0.1</li><li>mean: 0.56</li><li>max: 0.9</li></ul>

• Samples:

  query_id	query	corpus_id	title	score
  <code>q-1273</code>	<code>Sosek Desember 2021</code>	<code>b7890a143bc751d1d84dcf4a</code>	<code>Laporan Bulanan Data Sosial Ekonomi Desember 2021</code>	<code>0.9</code>
  <code>q-4882</code>	<code>Ekspor Indonesia menurut SITC 2019-2020</code>	<code>9f3d9054c2f29bc478d56cd1</code>	<code>Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode SITC, 2019-2020</code>	<code>0.9</code>
  <code>q-7141</code>	<code>Pengeluaran konsumsi penduduk Indonesia Maret 2018</code>	<code>4194e924ca33f087b68ab2de</code>	<code>Pengeluaran untuk Konsumsi Penduduk Indonesia, Maret 2018</code>	<code>0.9</code>

• Loss: <code>CosineSimilarityLoss</code> 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
• learning_rate: 1e-05
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• label_smoothing_factor: 0.01
• eval_on_start: True

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.01
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: True
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.0
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• 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",
}

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