--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2602 - loss:ContrastiveLoss base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 widget: - source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah (triliun) 2010 sentences: - 'Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023' - Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015 - 'Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023' - source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah (triliun) 2010 sentences: - Tabungan Bruto, Investasi Nonfinansial, dan Pinjaman Neto Triwulanan Sektor Pemerintahan Umum (triliun rupiah), 2009-2015 - Produksi Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2020 - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur (ribu rupiah), 2017 - source_sentence: Gaji bersih vs kelompok umur dan lapangan pekerjaan, 2023 sentences: - Investasi Nonfinansial Menurut Sektor (triliun rupiah), 2008-2014 - Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar rupiah), 2012-2016 - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) - source_sentence: Data utang luar negeri Indonesia (pemerintah dan BI), detail kreditor dan syarat, tahun 2010 sentences: - Angka Partisipasi Sekolah (APS) Penduduk Umur 7-18 Tahun Menurut Klasifikasi Desa, Jenis Kelamin, dan Kelompok Umur, 2009-2023 - Indeks Integritas Ujian Nasional - Rekapitulasi Luas Penutupan Lahan Hutan dan Non Hutan Menurut Provinsi Tahun 2014-2022 (Ribu Ha) - source_sentence: Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015 sentences: - Indeks Harga Konsumen Menurut Kelompok Pengeluaran, 2020-2023 - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023 datasets: - yahyaabd/bps-statictable-query-title-pairs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic base v1 eval type: allstats-semantic-base-v1-eval metrics: - type: pearson_cosine value: 0.8898188833771716 name: Pearson Cosine - type: spearman_cosine value: 0.779923841631983 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic base v1 test type: allstat-semantic-base-v1-test metrics: - type: pearson_cosine value: 0.9039024076661341 name: Pearson Cosine - type: spearman_cosine value: 0.8077065435723709 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) dataset. It maps sentences & paragraphs to a 768-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) ### 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: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, '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-ir-mpnet-base-v1") # Run inference sentences = [ 'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015', 'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)', 'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test | |:--------------------|:-------------------------------|:------------------------------| | pearson_cosine | 0.8898 | 0.9039 | | **spearman_cosine** | **0.7799** | **0.8077** | ## Training Details ### Training Dataset #### bps-statictable-query-title-pairs * Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) * Size: 2,602 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 | |:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------| | Pertumbuhan populasi provinsi di Indonesia 1971-2024 | Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010 | 0 | | Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017. | Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100) | 1 | | Laporan singkat cash flow statement Q4/2005 | Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014 | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Dataset #### bps-statictable-query-title-pairs * Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) * Size: 558 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 558 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | doc | label | |:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan | Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84) | 0 | | Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021 | Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023 | 1 | | Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014? | Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024 | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 4 - `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`: 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`: 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`: 4 - `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-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |:----------:|:-------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| | 0 | 0 | - | 0.0099 | 0.7449 | - | | 0.1220 | 10 | 0.0091 | 0.0065 | 0.7640 | - | | 0.2439 | 20 | 0.0059 | 0.0040 | 0.7743 | - | | 0.3659 | 30 | 0.0045 | 0.0036 | 0.7688 | - | | 0.4878 | 40 | 0.0045 | 0.0036 | 0.7694 | - | | 0.6098 | 50 | 0.0032 | 0.0037 | 0.7758 | - | | 0.7317 | 60 | 0.003 | 0.0025 | 0.7753 | - | | 0.8537 | 70 | 0.0035 | 0.0029 | 0.7710 | - | | 0.9756 | 80 | 0.0028 | 0.0026 | 0.7745 | - | | 1.0976 | 90 | 0.0015 | 0.0023 | 0.7754 | - | | 1.2195 | 100 | 0.0013 | 0.0021 | 0.7760 | - | | 1.3415 | 110 | 0.0013 | 0.0022 | 0.7751 | - | | 1.4634 | 120 | 0.002 | 0.0021 | 0.7746 | - | | 1.5854 | 130 | 0.0012 | 0.0020 | 0.7750 | - | | 1.7073 | 140 | 0.0007 | 0.0019 | 0.7740 | - | | 1.8293 | 150 | 0.0008 | 0.0019 | 0.7738 | - | | 1.9512 | 160 | 0.0026 | 0.0018 | 0.7772 | - | | 2.0732 | 170 | 0.0009 | 0.0019 | 0.7785 | - | | 2.1951 | 180 | 0.0005 | 0.0020 | 0.7781 | - | | 2.3171 | 190 | 0.0009 | 0.0017 | 0.7777 | - | | 2.4390 | 200 | 0.0005 | 0.0017 | 0.7773 | - | | 2.5610 | 210 | 0.0004 | 0.0018 | 0.7766 | - | | 2.6829 | 220 | 0.0006 | 0.0018 | 0.7762 | - | | 2.8049 | 230 | 0.0006 | 0.0019 | 0.7756 | - | | 2.9268 | 240 | 0.0016 | 0.0019 | 0.7777 | - | | 3.0488 | 250 | 0.0008 | 0.0018 | 0.7796 | - | | 3.1707 | 260 | 0.0005 | 0.0017 | 0.7802 | - | | **3.2927** | **270** | **0.0006** | **0.0017** | **0.7802** | **-** | | 3.4146 | 280 | 0.0004 | 0.0017 | 0.7805 | - | | 3.5366 | 290 | 0.0004 | 0.0017 | 0.7805 | - | | 3.6585 | 300 | 0.0003 | 0.0018 | 0.7802 | - | | 3.7805 | 310 | 0.0006 | 0.0018 | 0.7800 | - | | 3.9024 | 320 | 0.0003 | 0.0018 | 0.7799 | - | | -1 | -1 | - | - | - | 0.8077 | * 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```