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 query-hard-pos-neg-doc-pairs-statictable 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:
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-multilingual-miniLM-v1")
# Run inference
sentences = [
'Arus dana Q3 2006',
'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)',
'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012',
]
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-mini-v1-eval
andallstat-search-mini-v1-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | allstats-semantic-mini-v1-eval | allstat-search-mini-v1-test |
---|---|---|
pearson_cosine | 0.8665 | 0.8772 |
spearman_cosine | 0.8063 | 0.8096 |
Training Details
Training Dataset
query-hard-pos-neg-doc-pairs-statictable
- Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
- Size: 25,580 training samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 1000 samples:
query doc label type string string int details - min: 7 tokens
- mean: 20.14 tokens
- max: 55 tokens
- min: 5 tokens
- mean: 24.9 tokens
- max: 47 tokens
- 0: ~70.80%
- 1: ~29.20%
- Samples:
query doc label Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020
Jumlah Penghuni Lapas per Kanwil
0
status pekerjaan utama penduduk usia 15+ yang bekerja, 2020
Jumlah Penghuni Lapas per Kanwil
0
STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020
Jumlah Penghuni Lapas per Kanwil
0
- Loss:
OnlineContrastiveLoss
Evaluation Dataset
query-hard-pos-neg-doc-pairs-statictable
- Dataset: query-hard-pos-neg-doc-pairs-statictable at 7b28b96
- Size: 5,479 evaluation samples
- Columns:
query
,doc
, andlabel
- Approximate statistics based on the first 1000 samples:
query doc label type string string int details - min: 7 tokens
- mean: 20.78 tokens
- max: 52 tokens
- min: 4 tokens
- mean: 26.28 tokens
- max: 43 tokens
- 0: ~71.50%
- 1: ~28.50%
- Samples:
query doc label Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?
Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017
0
bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?
Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017
0
BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?
Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017
0
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64warmup_ratio
: 0.05fp16
: Trueload_best_model_at_end
: Trueeval_on_start
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.05warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Trueuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mini-v1-eval_spearman_cosine | allstat-search-mini-v1-test_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | 2.3102 | 0.6855 | - |
0.05 | 20 | 1.7642 | 1.2458 | 0.7253 | - |
0.1 | 40 | 0.9637 | 0.6870 | 0.7751 | - |
0.15 | 60 | 0.4319 | 0.4890 | 0.7897 | - |
0.2 | 80 | 0.3251 | 0.4944 | 0.7899 | - |
0.25 | 100 | 0.2665 | 0.3988 | 0.7954 | - |
0.3 | 120 | 0.1938 | 0.3795 | 0.7972 | - |
0.35 | 140 | 0.1495 | 0.2839 | 0.8014 | - |
0.4 | 160 | 0.0681 | 0.3011 | 0.8021 | - |
0.45 | 180 | 0.1775 | 0.3116 | 0.8004 | - |
0.5 | 200 | 0.0829 | 0.2536 | 0.8028 | - |
0.55 | 220 | 0.2332 | 0.2887 | 0.8015 | - |
0.6 | 240 | 0.1171 | 0.2862 | 0.8021 | - |
0.65 | 260 | 0.1059 | 0.2467 | 0.8023 | - |
0.7 | 280 | 0.1089 | 0.2240 | 0.8033 | - |
0.75 | 300 | 0.0445 | 0.1772 | 0.8048 | - |
0.8 | 320 | 0.0633 | 0.2392 | 0.8030 | - |
0.85 | 340 | 0.0506 | 0.2440 | 0.8027 | - |
0.9 | 360 | 0.1086 | 0.1926 | 0.8054 | - |
0.95 | 380 | 0.064 | 0.2984 | 0.8025 | - |
1.0 | 400 | 0.0478 | 0.2764 | 0.8025 | - |
1.05 | 420 | 0.0508 | 0.2393 | 0.8038 | - |
1.1 | 440 | 0.0266 | 0.2295 | 0.8039 | - |
1.15 | 460 | 0.0236 | 0.2477 | 0.8032 | - |
1.2 | 480 | 0.0142 | 0.2077 | 0.8045 | - |
1.25 | 500 | 0.0128 | 0.1972 | 0.8047 | - |
1.3 | 520 | 0.0205 | 0.2116 | 0.8042 | - |
1.35 | 540 | 0.0447 | 0.2425 | 0.8033 | - |
1.4 | 560 | 0.0 | 0.1999 | 0.8045 | - |
1.45 | 580 | 0.0284 | 0.1989 | 0.8046 | - |
1.5 | 600 | 0.0222 | 0.1789 | 0.8049 | - |
1.55 | 620 | 0.0066 | 0.1957 | 0.8045 | - |
1.6 | 640 | 0.0187 | 0.1993 | 0.8046 | - |
1.65 | 660 | 0.0489 | 0.1901 | 0.8051 | - |
1.7 | 680 | 0.0236 | 0.1556 | 0.8058 | - |
1.75 | 700 | 0.0186 | 0.1597 | 0.8059 | - |
1.8 | 720 | 0.0475 | 0.1813 | 0.8053 | - |
1.85 | 740 | 0.0215 | 0.1689 | 0.8060 | - |
1.9 | 760 | 0.0066 | 0.1746 | 0.8057 | - |
1.95 | 780 | 0.0158 | 0.1808 | 0.8054 | - |
2.0 | 800 | 0.0412 | 0.1799 | 0.8050 | - |
2.05 | 820 | 0.0 | 0.1809 | 0.8049 | - |
2.1 | 840 | 0.0072 | 0.1519 | 0.8059 | - |
2.15 | 860 | 0.032 | 0.1538 | 0.8060 | - |
2.2 | 880 | 0.0 | 0.1605 | 0.8058 | - |
2.25 | 900 | 0.016 | 0.1812 | 0.8053 | - |
2.3 | 920 | 0.0216 | 0.1550 | 0.8060 | - |
2.35 | 940 | 0.0124 | 0.1533 | 0.8062 | - |
2.4 | 960 | 0.0087 | 0.1499 | 0.8064 | - |
2.45 | 980 | 0.0 | 0.1493 | 0.8064 | - |
2.5 | 1000 | 0.0063 | 0.1483 | 0.8063 | - |
2.55 | 1020 | 0.0 | 0.1505 | 0.8063 | - |
2.6 | 1040 | 0.0 | 0.1508 | 0.8063 | - |
2.65 | 1060 | 0.0 | 0.1508 | 0.8063 | - |
2.7 | 1080 | 0.0 | 0.1508 | 0.8063 | - |
2.75 | 1100 | 0.0191 | 0.1546 | 0.8062 | - |
2.8 | 1120 | 0.0073 | 0.1566 | 0.8063 | - |
2.85 | 1140 | 0.0095 | 0.1529 | 0.8063 | - |
2.9 | 1160 | 0.0065 | 0.1512 | 0.8064 | - |
2.95 | 1180 | 0.0 | 0.1508 | 0.8063 | - |
3.0 | 1200 | 0.0 | 0.1508 | 0.8063 | - |
-1 | -1 | - | - | - | 0.8096 |
- 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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Dataset used to train yahyaabd/allstats-search-multilingual-miniLM-v1
Evaluation results
- Pearson Cosine on allstats semantic mini v1 evalself-reported0.866
- Spearman Cosine on allstats semantic mini v1 evalself-reported0.806
- Pearson Cosine on allstat search mini v1 testself-reported0.877
- Spearman Cosine on allstat search mini v1 testself-reported0.810