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-miniLM-v1-4")
# 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
Binary Classification
- Datasets:
allstats-semantic-mini-v1_test
andallstats-semantic-mini-v1_dev
- Evaluated with
BinaryClassificationEvaluator
Metric | allstats-semantic-mini-v1_test | allstats-semantic-mini-v1_dev |
---|---|---|
cosine_accuracy | 0.9739 | 0.9695 |
cosine_accuracy_threshold | 0.7544 | 0.7802 |
cosine_f1 | 0.9602 | 0.9532 |
cosine_f1_threshold | 0.754 | 0.7692 |
cosine_precision | 0.9498 | 0.9437 |
cosine_recall | 0.9707 | 0.9628 |
cosine_ap | 0.9915 | 0.9911 |
cosine_mcc | 0.9409 | 0.9305 |
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
: 32per_device_eval_batch_size
: 32num_train_epochs
: 2warmup_ratio
: 0.1fp16
: 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
: 32per_device_eval_batch_size
: 32per_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
: 2max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_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_test_cosine_ap | allstats-semantic-mini-v1_dev_cosine_ap |
---|---|---|---|---|---|
-1 | -1 | - | - | 0.8789 | - |
0 | 0 | - | 1.0484 | - | 0.8789 |
0.025 | 20 | 0.9076 | 0.7143 | - | 0.8976 |
0.05 | 40 | 0.4666 | 0.4744 | - | 0.9234 |
0.075 | 60 | 0.4514 | 0.3208 | - | 0.9542 |
0.1 | 80 | 0.3153 | 0.2520 | - | 0.9666 |
0.125 | 100 | 0.1726 | 0.2074 | - | 0.9725 |
0.15 | 120 | 0.1056 | 0.1860 | - | 0.9750 |
0.175 | 140 | 0.1414 | 0.2540 | - | 0.9674 |
0.2 | 160 | 0.1091 | 0.2077 | - | 0.9747 |
0.225 | 180 | 0.108 | 0.2333 | - | 0.9690 |
0.25 | 200 | 0.1672 | 0.1618 | - | 0.9771 |
0.275 | 220 | 0.1086 | 0.1804 | - | 0.9775 |
0.3 | 240 | 0.083 | 0.1805 | - | 0.9760 |
0.325 | 260 | 0.083 | 0.1674 | - | 0.9709 |
0.35 | 280 | 0.1197 | 0.1735 | - | 0.9734 |
0.375 | 300 | 0.0811 | 0.1272 | - | 0.9805 |
0.4 | 320 | 0.049 | 0.1491 | - | 0.9791 |
0.425 | 340 | 0.0373 | 0.1651 | - | 0.9721 |
0.45 | 360 | 0.1116 | 0.1742 | - | 0.9756 |
0.475 | 380 | 0.0665 | 0.1175 | - | 0.9837 |
0.5 | 400 | 0.0698 | 0.1165 | - | 0.9841 |
0.525 | 420 | 0.1316 | 0.1353 | - | 0.9817 |
0.55 | 440 | 0.0753 | 0.1276 | - | 0.9824 |
0.575 | 460 | 0.0411 | 0.1353 | - | 0.9801 |
0.6 | 480 | 0.0099 | 0.1292 | - | 0.9811 |
0.625 | 500 | 0.0473 | 0.1118 | - | 0.9836 |
0.65 | 520 | 0.0201 | 0.1083 | - | 0.9836 |
0.675 | 540 | 0.0519 | 0.1089 | - | 0.9856 |
0.7 | 560 | 0.0652 | 0.1003 | - | 0.9875 |
0.725 | 580 | 0.0594 | 0.1201 | - | 0.9872 |
0.75 | 600 | 0.0536 | 0.0896 | - | 0.9893 |
0.775 | 620 | 0.0479 | 0.0855 | - | 0.9874 |
0.8 | 640 | 0.0301 | 0.0948 | - | 0.9876 |
0.825 | 660 | 0.014 | 0.0993 | - | 0.9883 |
0.85 | 680 | 0.0199 | 0.0930 | - | 0.9884 |
0.875 | 700 | 0.0375 | 0.0765 | - | 0.9918 |
0.9 | 720 | 0.0 | 0.0805 | - | 0.9916 |
0.925 | 740 | 0.0243 | 0.0816 | - | 0.9916 |
0.95 | 760 | 0.0209 | 0.0935 | - | 0.9896 |
0.975 | 780 | 0.02 | 0.0831 | - | 0.9897 |
1.0 | 800 | 0.0376 | 0.0849 | - | 0.9890 |
1.025 | 820 | 0.0113 | 0.0960 | - | 0.9883 |
1.05 | 840 | 0.01 | 0.1131 | - | 0.9868 |
1.075 | 860 | 0.0294 | 0.1069 | - | 0.9861 |
1.1 | 880 | 0.0367 | 0.0921 | - | 0.9899 |
1.125 | 900 | 0.0 | 0.0910 | - | 0.9898 |
1.15 | 920 | 0.0163 | 0.1122 | - | 0.9871 |
1.175 | 940 | 0.0072 | 0.1204 | - | 0.9852 |
1.2 | 960 | 0.0175 | 0.1047 | - | 0.9872 |
1.225 | 980 | 0.0065 | 0.0992 | - | 0.9882 |
1.25 | 1000 | 0.0104 | 0.0932 | - | 0.9890 |
1.275 | 1020 | 0.0281 | 0.0866 | - | 0.9897 |
1.3 | 1040 | 0.0169 | 0.0874 | - | 0.9899 |
1.325 | 1060 | 0.0069 | 0.0910 | - | 0.9904 |
1.35 | 1080 | 0.0 | 0.0983 | - | 0.9898 |
1.375 | 1100 | 0.0 | 0.0985 | - | 0.9897 |
1.4 | 1120 | 0.0146 | 0.0919 | - | 0.9904 |
1.425 | 1140 | 0.0075 | 0.0852 | - | 0.9908 |
1.45 | 1160 | 0.014 | 0.0845 | - | 0.9908 |
1.475 | 1180 | 0.0065 | 0.0816 | - | 0.9907 |
1.5 | 1200 | 0.0 | 0.0811 | - | 0.9907 |
1.525 | 1220 | 0.0103 | 0.0785 | - | 0.9910 |
1.55 | 1240 | 0.013 | 0.0721 | - | 0.9915 |
1.575 | 1260 | 0.0066 | 0.0793 | - | 0.9910 |
1.6 | 1280 | 0.0 | 0.0810 | - | 0.9909 |
1.625 | 1300 | 0.0239 | 0.0803 | - | 0.9912 |
1.65 | 1320 | 0.0155 | 0.0816 | - | 0.9908 |
1.675 | 1340 | 0.009 | 0.0859 | - | 0.9904 |
1.7 | 1360 | 0.0065 | 0.0855 | - | 0.9900 |
1.725 | 1380 | 0.0 | 0.0866 | - | 0.9899 |
1.75 | 1400 | 0.0127 | 0.0865 | - | 0.9907 |
1.775 | 1420 | 0.0064 | 0.0819 | - | 0.9909 |
1.8 | 1440 | 0.0 | 0.0828 | - | 0.9910 |
1.825 | 1460 | 0.0081 | 0.0818 | - | 0.9912 |
1.85 | 1480 | 0.0068 | 0.0875 | - | 0.9909 |
1.875 | 1500 | 0.0 | 0.0886 | - | 0.9909 |
1.9 | 1520 | 0.011 | 0.0846 | - | 0.9911 |
1.925 | 1540 | 0.0 | 0.0843 | - | 0.9911 |
1.95 | 1560 | 0.0 | 0.0843 | - | 0.9911 |
1.975 | 1580 | 0.0 | 0.0843 | - | 0.9911 |
2.0 | 1600 | 0.0162 | 0.0850 | - | 0.9911 |
-1 | -1 | - | - | 0.9915 | - |
- 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",
}
- Downloads last month
- 8
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
Model tree for yahyaabd/allstats-search-miniLM-v1-4
Dataset used to train yahyaabd/allstats-search-miniLM-v1-4
Evaluation results
- Cosine Accuracy on allstats semantic mini v1 testself-reported0.974
- Cosine Accuracy Threshold on allstats semantic mini v1 testself-reported0.754
- Cosine F1 on allstats semantic mini v1 testself-reported0.960
- Cosine F1 Threshold on allstats semantic mini v1 testself-reported0.754
- Cosine Precision on allstats semantic mini v1 testself-reported0.950
- Cosine Recall on allstats semantic mini v1 testself-reported0.971
- Cosine Ap on allstats semantic mini v1 testself-reported0.991
- Cosine Mcc on allstats semantic mini v1 testself-reported0.941
- Cosine Accuracy on allstats semantic mini v1 devself-reported0.970
- Cosine Accuracy Threshold on allstats semantic mini v1 devself-reported0.780