--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:25580 - loss:OnlineContrastiveLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 widget: - source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar) sentences: - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005 - Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau Jawa dan Sumatera dengan Nasional (2018=100) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023 - source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal kedua tahun 2015? sentences: - Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016 - Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023 - source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan, per provinsi, 2018? sentences: - Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama, 2012-2023 - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2017 - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100) - source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun 2002-2023 sentences: - Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia, 1999, 2002-2023 - Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang Ditamatkan (ribu rupiah), 2016 - Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar Harga Berlaku, 2010-2016 - source_sentence: Arus dana Q3 2006 sentences: - Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik (miliar rupiah), 2005-2018 - 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 datasets: - yahyaabd/query-hard-pos-neg-doc-pairs-statictable pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: allstats semantic mini v1 test type: allstats-semantic-mini-v1_test metrics: - type: cosine_accuracy value: 0.9649571089614893 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.688197910785675 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9462184873949578 name: Cosine F1 - type: cosine_f1_threshold value: 0.688197910785675 name: Cosine F1 Threshold - type: cosine_precision value: 0.9409470752089136 name: Cosine Precision - type: cosine_recall value: 0.9515492957746479 name: Cosine Recall - type: cosine_ap value: 0.9858302481584482 name: Cosine Ap - type: cosine_mcc value: 0.9202633777403256 name: Cosine Mcc - task: type: binary-classification name: Binary Classification dataset: name: allstats semantic mini v1 dev type: allstats-semantic-mini-v1_dev metrics: - type: cosine_accuracy value: 0.9651396240189816 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6833629608154297 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9464836088540207 name: Cosine F1 - type: cosine_f1_threshold value: 0.6833629608154297 name: Cosine F1 Threshold - type: cosine_precision value: 0.9414715719063546 name: Cosine Precision - type: cosine_recall value: 0.9515492957746479 name: Cosine Recall - type: cosine_ap value: 0.9862354589024407 name: Cosine Ap - type: cosine_mcc value: 0.9206641526376831 name: Cosine Mcc --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("yahyaabd/allstats-search-miniLM-v1-3") # 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` and `allstats-semantic-mini-v1_dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | allstats-semantic-mini-v1_test | allstats-semantic-mini-v1_dev | |:--------------------------|:-------------------------------|:------------------------------| | cosine_accuracy | 0.965 | 0.9651 | | cosine_accuracy_threshold | 0.6882 | 0.6834 | | cosine_f1 | 0.9462 | 0.9465 | | cosine_f1_threshold | 0.6882 | 0.6834 | | cosine_precision | 0.9409 | 0.9415 | | cosine_recall | 0.9515 | 0.9515 | | **cosine_ap** | **0.9858** | **0.9862** | | cosine_mcc | 0.9203 | 0.9207 | ## Training Details ### Training Dataset #### query-hard-pos-neg-doc-pairs-statictable * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) * Size: 25,580 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Evaluation Dataset #### query-hard-pos-neg-doc-pairs-statictable * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) * Size: 5,479 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `eval_on_start`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5e-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`: 1 - `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`: 0.0 - `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
### 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 | - | 0.4455 | - | 0.8789 | | 0.0125 | 20 | 0.4484 | 0.3363 | - | 0.8893 | | 0.0250 | 40 | 0.1921 | 0.2230 | - | 0.9052 | | 0.0375 | 60 | 0.1779 | 0.1435 | - | 0.9440 | | 0.0500 | 80 | 0.1047 | 0.1269 | - | 0.9511 | | 0.0625 | 100 | 0.0669 | 0.1498 | - | 0.9445 | | 0.0750 | 120 | 0.1662 | 0.1028 | - | 0.9630 | | 0.0876 | 140 | 0.0774 | 0.1115 | - | 0.9589 | | 0.1001 | 160 | 0.0947 | 0.1204 | - | 0.9500 | | 0.1126 | 180 | 0.1285 | 0.1464 | - | 0.9456 | | 0.1251 | 200 | 0.0793 | 0.1024 | - | 0.9600 | | 0.1376 | 220 | 0.0792 | 0.0992 | - | 0.9607 | | 0.1501 | 240 | 0.0696 | 0.0931 | - | 0.9642 | | 0.1626 | 260 | 0.0692 | 0.1205 | - | 0.9538 | | 0.1751 | 280 | 0.1015 | 0.0980 | - | 0.9629 | | 0.1876 | 300 | 0.0628 | 0.1001 | - | 0.9634 | | 0.2001 | 320 | 0.0335 | 0.1094 | - | 0.9650 | | 0.2126 | 340 | 0.0668 | 0.0941 | - | 0.9673 | | 0.2251 | 360 | 0.0662 | 0.0765 | - | 0.9748 | | 0.2376 | 380 | 0.0251 | 0.0674 | - | 0.9784 | | 0.2502 | 400 | 0.0771 | 0.0667 | - | 0.9805 | | 0.2627 | 420 | 0.0363 | 0.0576 | - | 0.9785 | | 0.2752 | 440 | 0.0762 | 0.0787 | - | 0.9726 | | 0.2877 | 460 | 0.0475 | 0.0649 | - | 0.9773 | | 0.3002 | 480 | 0.0086 | 0.0692 | - | 0.9760 | | 0.3127 | 500 | 0.0242 | 0.0636 | - | 0.9771 | | 0.3252 | 520 | 0.0342 | 0.0700 | - | 0.9758 | | 0.3377 | 540 | 0.0568 | 0.0547 | - | 0.9792 | | 0.3502 | 560 | 0.0286 | 0.0508 | - | 0.9808 | | 0.3627 | 580 | 0.0426 | 0.0518 | - | 0.9823 | | 0.3752 | 600 | 0.03 | 0.0553 | - | 0.9806 | | 0.3877 | 620 | 0.0146 | 0.0826 | - | 0.9748 | | 0.4003 | 640 | 0.0417 | 0.0667 | - | 0.9779 | | 0.4128 | 660 | 0.0081 | 0.0667 | - | 0.9775 | | 0.4253 | 680 | 0.0094 | 0.0704 | - | 0.9798 | | 0.4378 | 700 | 0.0225 | 0.0525 | - | 0.9841 | | 0.4503 | 720 | 0.0217 | 0.0462 | - | 0.9861 | | 0.4628 | 740 | 0.011 | 0.0466 | - | 0.9858 | | 0.4753 | 760 | 0.0191 | 0.0495 | - | 0.9846 | | 0.4878 | 780 | 0.0146 | 0.0478 | - | 0.9847 | | 0.5003 | 800 | 0.0076 | 0.0424 | - | 0.9852 | | 0.5128 | 820 | 0.035 | 0.0549 | - | 0.9821 | | 0.5253 | 840 | 0.0321 | 0.0551 | - | 0.9796 | | 0.5378 | 860 | 0.0241 | 0.0559 | - | 0.9781 | | 0.5503 | 880 | 0.0335 | 0.0525 | - | 0.9792 | | 0.5629 | 900 | 0.0125 | 0.0539 | - | 0.9799 | | 0.5754 | 920 | 0.0154 | 0.0512 | - | 0.9823 | | 0.5879 | 940 | 0.0133 | 0.0497 | - | 0.9824 | | 0.6004 | 960 | 0.0072 | 0.0532 | - | 0.9821 | | 0.6129 | 980 | 0.0192 | 0.0520 | - | 0.9809 | | 0.6254 | 1000 | 0.0199 | 0.0503 | - | 0.9811 | | 0.6379 | 1020 | 0.0069 | 0.0484 | - | 0.9824 | | 0.6504 | 1040 | 0.0065 | 0.0514 | - | 0.9806 | | 0.6629 | 1060 | 0.0098 | 0.0479 | - | 0.9834 | | 0.6754 | 1080 | 0.0 | 0.0480 | - | 0.9841 | | 0.6879 | 1100 | 0.0247 | 0.0508 | - | 0.9835 | | 0.7004 | 1120 | 0.0137 | 0.0481 | - | 0.9842 | | 0.7129 | 1140 | 0.0068 | 0.0512 | - | 0.9838 | | 0.7255 | 1160 | 0.0182 | 0.0473 | - | 0.9851 | | 0.7380 | 1180 | 0.0129 | 0.0442 | - | 0.9859 | | 0.7505 | 1200 | 0.0 | 0.0436 | - | 0.9860 | | 0.7630 | 1220 | 0.0073 | 0.0439 | - | 0.9858 | | 0.7755 | 1240 | 0.0081 | 0.0441 | - | 0.9859 | | 0.7880 | 1260 | 0.0305 | 0.0460 | - | 0.9857 | | 0.8005 | 1280 | 0.0003 | 0.0486 | - | 0.9851 | | 0.8130 | 1300 | 0.0218 | 0.0501 | - | 0.9852 | | 0.8255 | 1320 | 0.0187 | 0.0435 | - | 0.9844 | | 0.8380 | 1340 | 0.0205 | 0.0437 | - | 0.9846 | | 0.8505 | 1360 | 0.0094 | 0.0442 | - | 0.9851 | | 0.8630 | 1380 | 0.0083 | 0.0426 | - | 0.9856 | | **0.8755** | **1400** | **0.0** | **0.0423** | **-** | **0.9858** | | 0.8881 | 1420 | 0.0 | 0.0424 | - | 0.9859 | | 0.9006 | 1440 | 0.0073 | 0.0428 | - | 0.9859 | | 0.9131 | 1460 | 0.0075 | 0.0441 | - | 0.9859 | | 0.9256 | 1480 | 0.0177 | 0.0447 | - | 0.9858 | | 0.9381 | 1500 | 0.0 | 0.0438 | - | 0.9858 | | 0.9506 | 1520 | 0.0 | 0.0438 | - | 0.9858 | | 0.9631 | 1540 | 0.0072 | 0.0440 | - | 0.9860 | | 0.9756 | 1560 | 0.0101 | 0.0436 | - | 0.9861 | | 0.9881 | 1580 | 0.0277 | 0.0437 | - | 0.9862 | | -1 | -1 | - | - | 0.9858 | - | * 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 ```bibtex @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", } ```