--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:967831 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 widget: - source_sentence: 'Penghasilan rata-rata pelaku usaha mandiri: Analisis berdasarkan lokasi dan jenjang pendidikan, 2023' sentences: - Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang Ditamatkan, 2023 - Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 2013-2021 - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah) - source_sentence: Bagaimana traffic penerbangan internasional di Indonesia pada 2008? sentences: - Tingkat Inflasi Harga Konsumen Nasional Bulanan (M-to-M) 1 (2022=100) - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005 - Lalu Lintas Penerbangan Luar Negeri Indonesia Tahun 2003-2022 - source_sentence: Data indeks daya penyebaran dan derajat kepekaan sektor ekonomi, ambil contoh tahun 2005 sentences: - Indeks Daya Penyebaran dan Indeks Derajat Kepekaan Menurut Sektor Ekonomi, 1995, 2000, 2005, dan 2010 - Ekspor Kopi Menurut Negara Tujuan Utama, 2000-2023 - Anggaran Kesehatan dari Direktorat Penyusunan APBN - Direktorat Jenderal Anggaran, Kementerian Keuangan - source_sentence: Data aktivitas penduduk 15 tahun ke atas berdasarkan kelompok umur, satu minggu ke belakang (periode 2002) sentences: - Ekspor Lada Putih menurut Negara Tujuan Utama, 2012-2023 - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Selatan, 2018-2023 - Penduduk Berumur 15 Tahun Ke Atas Menurut Golongan Umur dan Jenis Kegiatan Selama Seminggu yang Lalu, 1997 - 2007 - source_sentence: Laporan singkat arus kas Q2 2005, dalam miliar sentences: - Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah) - Indikator Pendidikan, 1994-2023 - Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jumlah Jam Kerja Utama, 2020 datasets: - yahyaabd/statictable-triplets-all pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@1 - cosine_ndcg@5 - cosine_ndcg@10 - cosine_mrr@1 - cosine_mrr@5 - cosine_mrr@10 - cosine_map@1 - cosine_map@5 - cosine_map@10 model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: bps statictable ir type: bps-statictable-ir metrics: - type: cosine_accuracy@1 value: 0.8990228013029316 name: Cosine Accuracy@1 - type: cosine_accuracy@5 value: 0.9837133550488599 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8990228013029316 name: Cosine Precision@1 - type: cosine_precision@5 value: 0.21889250814332245 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12605863192182412 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7029638149674847 name: Cosine Recall@1 - type: cosine_recall@5 value: 0.789022126091837 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8116078533769628 name: Cosine Recall@10 - type: cosine_ndcg@1 value: 0.8990228013029316 name: Cosine Ndcg@1 - type: cosine_ndcg@5 value: 0.8178579787978988 name: Cosine Ndcg@5 - type: cosine_ndcg@10 value: 0.8156444177517035 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.8990228013029316 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.9347991313789358 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.9368827878599865 name: Cosine Mrr@10 - type: cosine_map@1 value: 0.8990228013029316 name: Cosine Map@1 - type: cosine_map@5 value: 0.772128121606949 name: Cosine Map@5 - type: cosine_map@10 value: 0.7635855701310564 name: Cosine Map@10 --- # 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 [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) 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:** - [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) ### 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/paraphrase-multilingual-miniLM-L12-v2-mnrl-beir-2") # Run inference sentences = [ 'Laporan singkat arus kas Q2 2005, dalam miliar', 'Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)', 'Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jumlah Jam Kerja Utama, 2020', ] 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 #### Information Retrieval * Dataset: `bps-statictable-ir` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.899 | | cosine_accuracy@5 | 0.9837 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.899 | | cosine_precision@5 | 0.2189 | | cosine_precision@10 | 0.1261 | | cosine_recall@1 | 0.703 | | cosine_recall@5 | 0.789 | | cosine_recall@10 | 0.8116 | | cosine_ndcg@1 | 0.899 | | cosine_ndcg@5 | 0.8179 | | **cosine_ndcg@10** | **0.8156** | | cosine_mrr@1 | 0.899 | | cosine_mrr@5 | 0.9348 | | cosine_mrr@10 | 0.9369 | | cosine_map@1 | 0.899 | | cosine_map@5 | 0.7721 | | cosine_map@10 | 0.7636 | ## Training Details ### Training Dataset #### statictable-triplets-all * Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030) * Size: 967,831 training samples * Columns: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:-------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------| | Indeks harga petani (diterima & dibayar) dan NTP per provinsi, 2012 | Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani (NTP) Menurut Provinsi, 2008-2016 | Persentase Rumah Tangga Menurut Provinsi dan KebiasaanMemanfaatkan Air Bekas untuk Keperluan Lain, 2013, 2014, 2017, 2021 | | Data rumah tangga perikanan budidaya Indonesia, detail per provinsi dan jenis budidaya, di tahun 2008 | Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016 | Ringkasan Neraca Arus Dana, 2005, (Miliar Rupiah) | | Lapangan pekerjaan vs pendidikan pekerja (15 tahun ke atas), 1986 hingga 1996 | Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 1986 -1996 | Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17 Lapangan Usaha), 2016 (Juta Rupiah) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### statictable-triplets-all * Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030) * Size: 967,831 evaluation samples * Columns: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:-------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | Bagaimana hubungan IHK dan rata-rata upah buruh industri (bukan supervisor) bulanan tahun 2010, acuan 1996? | IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100) | Rata-rata Harga Valuta Asing Terpilih menurut Provinsi, 2014 | | Berapa rata-rata gaji bulanan pekerja Indonesia berdasarkan ijazah terakhir dan sektor pekerjaannya (2017)? | Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017 | Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 2013-2021 | | Data luas lahan (hektar) yang dipakai untuk jenis budidaya perikanan di tiap provinsi tahun 2009 | Luas Area Usaha Budidaya Perikanan Menurut Provinsi dan Jenis Budidaya (ha), 2005-2016 | Ringkasan Neraca Arus Dana, Triwulan I, 2008, (Miliar Rupiah) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `weight_decay`: 0.01 - `num_train_epochs`: 2 - `lr_scheduler_type`: reduce_lr_on_plateau - `lr_scheduler_kwargs`: {'factor': 0.5, 'patience': 2} - `warmup_steps`: 10000 - `save_on_each_node`: True - `fp16`: True - `dataloader_num_workers`: 2 - `load_best_model_at_end`: True - `eval_on_start`: True - `batch_sampler`: no_duplicates #### 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`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: reduce_lr_on_plateau - `lr_scheduler_kwargs`: {'factor': 0.5, 'patience': 2} - `warmup_ratio`: 0.0 - `warmup_steps`: 10000 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: True - `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`: 2 - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | bps-statictable-ir_cosine_ndcg@10 | |:----------:|:---------:|:-------------:|:---------------:|:---------------------------------:| | 0 | 0 | - | 1.0819 | 0.4643 | | 0.0334 | 200 | 0.1373 | - | - | | 0.0668 | 400 | 0.0354 | - | - | | 0.0835 | 500 | - | 0.0132 | 0.8252 | | 0.1002 | 600 | 0.028 | - | - | | 0.1336 | 800 | 0.018 | - | - | | 0.1670 | 1000 | 0.0145 | 0.0096 | 0.8286 | | 0.2004 | 1200 | 0.0089 | - | - | | 0.2338 | 1400 | 0.0103 | - | - | | 0.2505 | 1500 | - | 0.0067 | 0.8312 | | 0.2672 | 1600 | 0.0098 | - | - | | 0.3006 | 1800 | 0.0086 | - | - | | 0.3339 | 2000 | 0.0086 | 0.0044 | 0.8246 | | 0.3673 | 2200 | 0.0088 | - | - | | 0.4007 | 2400 | 0.0075 | - | - | | 0.4174 | 2500 | - | 0.0051 | 0.8295 | | 0.4341 | 2600 | 0.0066 | - | - | | 0.4675 | 2800 | 0.0054 | - | - | | 0.5009 | 3000 | 0.0051 | 0.0059 | 0.8294 | | 0.5343 | 3200 | 0.0052 | - | - | | 0.5677 | 3400 | 0.0037 | - | - | | 0.5844 | 3500 | - | 0.0041 | 0.8126 | | 0.6011 | 3600 | 0.0078 | - | - | | 0.6345 | 3800 | 0.005 | - | - | | 0.6679 | 4000 | 0.0045 | 0.0050 | 0.8308 | | 0.7013 | 4200 | 0.0047 | - | - | | 0.7347 | 4400 | 0.0066 | - | - | | 0.7514 | 4500 | - | 0.0033 | 0.8233 | | 0.7681 | 4600 | 0.0043 | - | - | | 0.8015 | 4800 | 0.003 | - | - | | 0.8349 | 5000 | 0.0029 | 0.0036 | 0.8224 | | 0.8683 | 5200 | 0.0014 | - | - | | 0.9017 | 5400 | 0.0058 | - | - | | 0.9184 | 5500 | - | 0.0020 | 0.8169 | | 0.9350 | 5600 | 0.0045 | - | - | | 0.9684 | 5800 | 0.0036 | - | - | | 1.0018 | 6000 | 0.0053 | 0.0018 | 0.8152 | | 1.0352 | 6200 | 0.0035 | - | - | | 1.0686 | 6400 | 0.0017 | - | - | | 1.0853 | 6500 | - | 0.0024 | 0.8231 | | 1.1020 | 6600 | 0.0037 | - | - | | 1.1354 | 6800 | 0.0044 | - | - | | 1.1688 | 7000 | 0.0011 | 0.0113 | 0.8153 | | 1.2022 | 7200 | 0.0042 | - | - | | 1.2356 | 7400 | 0.0028 | - | - | | 1.2523 | 7500 | - | 0.0046 | 0.8253 | | 1.2690 | 7600 | 0.0005 | - | - | | 1.3024 | 7800 | 0.001 | - | - | | 1.3358 | 8000 | 0.0011 | 0.0017 | 0.8216 | | 1.3692 | 8200 | 0.0007 | - | - | | 1.4026 | 8400 | 0.0014 | - | - | | 1.4193 | 8500 | - | 0.0014 | 0.8253 | | 1.4360 | 8600 | 0.0003 | - | - | | 1.4694 | 8800 | 0.0005 | - | - | | 1.5028 | 9000 | 0.002 | 0.0012 | 0.8250 | | 1.5361 | 9200 | 0.0013 | - | - | | 1.5695 | 9400 | 0.0009 | - | - | | 1.5862 | 9500 | - | 0.0003 | 0.8162 | | 1.6029 | 9600 | 0.0021 | - | - | | 1.6363 | 9800 | 0.0013 | - | - | | 1.6697 | 10000 | 0.0005 | 0.0003 | 0.8234 | | 1.7031 | 10200 | 0.0004 | - | - | | 1.7365 | 10400 | 0.0004 | - | - | | **1.7532** | **10500** | **-** | **0.0001** | **0.8225** | | 1.7699 | 10600 | 0.0011 | - | - | | 1.8033 | 10800 | 0.0004 | - | - | | 1.8367 | 11000 | 0.0009 | 0.0008 | 0.8259 | | 1.8701 | 11200 | 0.0024 | - | - | | 1.9035 | 11400 | 0.0002 | - | - | | 1.9202 | 11500 | - | 0.0008 | 0.8156 | | 1.9369 | 11600 | 0.0007 | - | - | | 1.9703 | 11800 | 0.0007 | - | - | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.3.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```