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-mini-v1-64-1")
# 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 | 0.9843 | 0.9744 |
cosine_accuracy_threshold | 0.7189 | 0.6994 |
cosine_f1 | 0.976 | 0.9617 |
cosine_f1_threshold | 0.7184 | 0.6994 |
cosine_precision | 0.9733 | 0.9351 |
cosine_recall | 0.9788 | 0.9899 |
cosine_ap | 0.9972 | 0.9915 |
cosine_mcc | 0.9644 | 0.9434 |
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
: 64num_train_epochs
: 1warmup_ratio
: 0.1fp16
: Truedataloader_num_workers
: 4load_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
: 1max_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
: 4dataloader_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 | - | - | 0.8910 | - |
0 | 0 | - | 2.3102 | - | 0.8789 |
0.05 | 20 | 1.6432 | 1.0995 | - | 0.9347 |
0.1 | 40 | 0.8514 | 0.6258 | - | 0.9701 |
0.15 | 60 | 0.3663 | 0.4353 | - | 0.9799 |
0.2 | 80 | 0.2139 | 0.5758 | - | 0.9775 |
0.25 | 100 | 0.3067 | 0.3477 | - | 0.9851 |
0.3 | 120 | 0.1662 | 0.3278 | - | 0.9855 |
0.35 | 140 | 0.1227 | 0.3024 | - | 0.9854 |
0.4 | 160 | 0.1103 | 0.2967 | - | 0.9868 |
0.45 | 180 | 0.1701 | 0.2558 | - | 0.9879 |
0.5 | 200 | 0.0995 | 0.2341 | - | 0.9898 |
0.55 | 220 | 0.1347 | 0.2723 | - | 0.9879 |
0.6 | 240 | 0.0742 | 0.2294 | - | 0.9892 |
0.65 | 260 | 0.0611 | 0.2327 | - | 0.9893 |
0.7 | 280 | 0.1164 | 0.2080 | - | 0.9894 |
0.75 | 300 | 0.0514 | 0.1944 | - | 0.9902 |
0.8 | 320 | 0.0708 | 0.1767 | - | 0.9912 |
0.85 | 340 | 0.0663 | 0.1710 | - | 0.9917 |
0.9 | 360 | 0.0501 | 0.1709 | - | 0.9916 |
0.95 | 380 | 0.05 | 0.1823 | - | 0.9914 |
1.0 | 400 | 0.0269 | 0.1801 | - | 0.9915 |
-1 | -1 | - | - | 0.9972 | - |
- 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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Model tree for yahyaabd/allstats-search-mini-v1-64-1
Dataset used to train yahyaabd/allstats-search-mini-v1-64-1
Evaluation results
- Cosine Accuracy on allstats semantic mini v1 testself-reported0.984
- Cosine Accuracy Threshold on allstats semantic mini v1 testself-reported0.719
- Cosine F1 on allstats semantic mini v1 testself-reported0.976
- Cosine F1 Threshold on allstats semantic mini v1 testself-reported0.718
- Cosine Precision on allstats semantic mini v1 testself-reported0.973
- Cosine Recall on allstats semantic mini v1 testself-reported0.979
- Cosine Ap on allstats semantic mini v1 testself-reported0.997
- Cosine Mcc on allstats semantic mini v1 testself-reported0.964
- Cosine Accuracy on allstats semantic mini v1 devself-reported0.974
- Cosine Accuracy Threshold on allstats semantic mini v1 devself-reported0.699