--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:44668 - loss:CosineSimilarityLoss base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 widget: - source_sentence: 'Ringkasan data ekonomi bulan Desember ' sentences: - 'Indikator Ekonomi Mei ' - Indikator Konstruksi Triwulanan III- - 'Direktori Perusahaan Pertanian, Peternakan ' - source_sentence: 'Data kesejahteraan rakyat Indonesia ' sentences: - 'Statistik Tanaman Hias Indonesia ' - 'Direktori Usaha/Perusahaan Menengah Besar Penyediaan Akomodasi dan Penyediaan Makan Minum Sensus Ekonomi ' - Produk Domestik Regional Bruto Provinsi-Provinsi di Indonesia menurut Pengeluaran, - - source_sentence: 'Buku direktori kontraktor Indonesia bagian barat ' sentences: - Statistik Perdagangan Luar Negeri Indonesia Ekspor, , Jilid I - 'Hasil Survei Kualitas Air di Daerah Istimewa Yogyakarta ' - 'Direktori Perusahaan Konstruksi , Buku II: Pulau Jawa, Bali, Nusa Tenggara, dan Kepulauan Maluku' - source_sentence: 'Direktori Perusahaan Jasa kesehatan Buku II Hasil SE ' sentences: - 'Direktori Perusahaan Jasa kesehatan Buku II Hasil SE ' - 'Statistik Transportasi ' - 'Statistik Produksi Kehutanan ' - source_sentence: Sistem neraca lingkungan dan ekonomi Indonesia, - sentences: - 'Distribusi Perdagangan Komoditas Minyak Goreng Indonesia ' - Sistem Terintegrasi Neraca Lingkungan dan Ekonomi Indonesia - - 'Statistik Tanaman Biofarmaka dan Obat-obatan ' datasets: - yahyaabd/bps-query-publication-similarity-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: allstat semantic dev type: allstat-semantic-dev metrics: - type: pearson_cosine value: 0.9671548110817865 name: Pearson Cosine - type: spearman_cosine value: 0.8713936346864137 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic test type: allstat-semantic-test metrics: - type: pearson_cosine value: 0.9643539430966529 name: Pearson Cosine - type: spearman_cosine value: 0.8571860451909368 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-query-publication-similarity-pairs](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-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-query-publication-similarity-pairs](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-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/allstat-semantic-search-mpnet-base-v3-sts") # Run inference sentences = [ 'Sistem neraca lingkungan dan ekonomi Indonesia, -', 'Sistem Terintegrasi Neraca Lingkungan dan Ekonomi Indonesia -', 'Distribusi Perdagangan Komoditas Minyak Goreng Indonesia ', ] 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: `allstat-semantic-dev` and `allstat-semantic-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstat-semantic-dev | allstat-semantic-test | |:--------------------|:---------------------|:----------------------| | pearson_cosine | 0.9672 | 0.9644 | | **spearman_cosine** | **0.8714** | **0.8572** | ## Training Details ### Training Dataset #### bps-query-publication-similarity-pairs * Dataset: [bps-query-publication-similarity-pairs](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-pairs) at [cf2836e](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-pairs/tree/cf2836e364b4ca465c3b32e19e754c77f0b90c26) * Size: 44,668 training samples * Columns: query, doc_title, and score * Approximate statistics based on the first 1000 samples: | | query | doc_title | score | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc_title | score | |:------------------------------------------------------|:---------------------------------------------------------------------|:------------------| | Tren bisnis perikanan di Indonesia | Statistik Perusahaan Perikanan | 0.88 | | Statistik APBDes | Statistik Perusahaan Peternakan Ternak Besar dan Kecil | 0.29 | | Laporan Indikator Konstruksi semester 1 | Statistik Air Bersih - | 0.25 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### bps-query-publication-similarity-pairs * Dataset: [bps-query-publication-similarity-pairs](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-pairs) at [cf2836e](https://huggingface.co/datasets/yahyaabd/bps-query-publication-similarity-pairs/tree/cf2836e364b4ca465c3b32e19e754c77f0b90c26) * Size: 2,482 evaluation samples * Columns: query, doc_title, and score * Approximate statistics based on the first 1000 samples: | | query | doc_title | score | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc_title | score | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------| | Dampak COVID-19 pada usaha mikro kecil | Statistik Penyedia Makan Minum | 0.2 | | Sektor konstruksi Aceh, data UMKM | Profil Usaha Konstruksi Perorangan Provinsi Aceh, | 0.88 | | SP2010: Statistik lansia Sumatera Selatan | Statistik Penduduk Lanjut Usia Provinsi Sumatera Selatan -Hasil Sensus Penduduk | 0.81 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: 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`: 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`: False - `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`: False - `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
Click to expand | Epoch | Step | Training Loss | Validation Loss | allstat-semantic-dev_spearman_cosine | allstat-semantic-test_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:------------------------------------:|:-------------------------------------:| | 0.0358 | 100 | 0.0498 | 0.0311 | 0.7840 | - | | 0.0716 | 200 | 0.0294 | 0.0245 | 0.7970 | - | | 0.1074 | 300 | 0.0241 | 0.0210 | 0.8040 | - | | 0.1433 | 400 | 0.0215 | 0.0192 | 0.8078 | - | | 0.1791 | 500 | 0.0208 | 0.0200 | 0.8091 | - | | 0.2149 | 600 | 0.0208 | 0.0183 | 0.8183 | - | | 0.2507 | 700 | 0.0216 | 0.0176 | 0.8177 | - | | 0.2865 | 800 | 0.02 | 0.0177 | 0.8192 | - | | 0.3223 | 900 | 0.0183 | 0.0180 | 0.8107 | - | | 0.3582 | 1000 | 0.0197 | 0.0190 | 0.8058 | - | | 0.3940 | 1100 | 0.0199 | 0.0176 | 0.8182 | - | | 0.4298 | 1200 | 0.0207 | 0.0193 | 0.8097 | - | | 0.4656 | 1300 | 0.0186 | 0.0190 | 0.8088 | - | | 0.5014 | 1400 | 0.0197 | 0.0178 | 0.8122 | - | | 0.5372 | 1500 | 0.0179 | 0.0177 | 0.8161 | - | | 0.5731 | 1600 | 0.0171 | 0.0169 | 0.8197 | - | | 0.6089 | 1700 | 0.0178 | 0.0162 | 0.8152 | - | | 0.6447 | 1800 | 0.0152 | 0.0162 | 0.8234 | - | | 0.6805 | 1900 | 0.0171 | 0.0162 | 0.8187 | - | | 0.7163 | 2000 | 0.0179 | 0.0154 | 0.8194 | - | | 0.7521 | 2100 | 0.0164 | 0.0158 | 0.8126 | - | | 0.7880 | 2200 | 0.016 | 0.0149 | 0.8254 | - | | 0.8238 | 2300 | 0.0164 | 0.0149 | 0.8193 | - | | 0.8596 | 2400 | 0.0151 | 0.0139 | 0.8297 | - | | 0.8954 | 2500 | 0.0151 | 0.0142 | 0.8306 | - | | 0.9312 | 2600 | 0.0136 | 0.0143 | 0.8315 | - | | 0.9670 | 2700 | 0.0157 | 0.0135 | 0.8342 | - | | 1.0029 | 2800 | 0.0133 | 0.0135 | 0.8330 | - | | 1.0387 | 2900 | 0.0116 | 0.0133 | 0.8369 | - | | 1.0745 | 3000 | 0.0106 | 0.0132 | 0.8357 | - | | 1.1103 | 3100 | 0.0113 | 0.0126 | 0.8395 | - | | 1.1461 | 3200 | 0.0123 | 0.0131 | 0.8362 | - | | 1.1819 | 3300 | 0.0117 | 0.0142 | 0.8289 | - | | 1.2178 | 3400 | 0.0133 | 0.0135 | 0.8322 | - | | 1.2536 | 3500 | 0.0113 | 0.0129 | 0.8358 | - | | 1.2894 | 3600 | 0.0109 | 0.0132 | 0.8352 | - | | 1.3252 | 3700 | 0.0107 | 0.0122 | 0.8394 | - | | 1.3610 | 3800 | 0.0125 | 0.0128 | 0.8364 | - | | 1.3968 | 3900 | 0.012 | 0.0126 | 0.8342 | - | | 1.4327 | 4000 | 0.0123 | 0.0128 | 0.8364 | - | | 1.4685 | 4100 | 0.0109 | 0.0127 | 0.8369 | - | | 1.5043 | 4200 | 0.0108 | 0.0125 | 0.8385 | - | | 1.5401 | 4300 | 0.011 | 0.0124 | 0.8416 | - | | 1.5759 | 4400 | 0.0104 | 0.0120 | 0.8455 | - | | 1.6117 | 4500 | 0.0107 | 0.0114 | 0.8498 | - | | 1.6476 | 4600 | 0.0095 | 0.0114 | 0.8485 | - | | 1.6834 | 4700 | 0.0114 | 0.0118 | 0.8457 | - | | 1.7192 | 4800 | 0.0101 | 0.0118 | 0.8417 | - | | 1.7550 | 4900 | 0.0127 | 0.0113 | 0.8466 | - | | 1.7908 | 5000 | 0.0112 | 0.0114 | 0.8466 | - | | 1.8266 | 5100 | 0.0095 | 0.0109 | 0.8485 | - | | 1.8625 | 5200 | 0.0107 | 0.0114 | 0.8465 | - | | 1.8983 | 5300 | 0.0113 | 0.0115 | 0.8454 | - | | 1.9341 | 5400 | 0.0107 | 0.0116 | 0.8473 | - | | 1.9699 | 5500 | 0.0102 | 0.0111 | 0.8526 | - | | 2.0057 | 5600 | 0.0097 | 0.0109 | 0.8542 | - | | 2.0415 | 5700 | 0.0082 | 0.0106 | 0.8534 | - | | 2.0774 | 5800 | 0.0069 | 0.0107 | 0.8551 | - | | 2.1132 | 5900 | 0.0077 | 0.0107 | 0.8533 | - | | 2.1490 | 6000 | 0.0076 | 0.0109 | 0.8532 | - | | 2.1848 | 6100 | 0.0071 | 0.0107 | 0.8515 | - | | 2.2206 | 6200 | 0.0075 | 0.0104 | 0.8563 | - | | 2.2564 | 6300 | 0.0074 | 0.0102 | 0.8567 | - | | 2.2923 | 6400 | 0.0083 | 0.0105 | 0.8567 | - | | 2.3281 | 6500 | 0.0075 | 0.0107 | 0.8515 | - | | 2.3639 | 6600 | 0.007 | 0.0103 | 0.8546 | - | | 2.3997 | 6700 | 0.0079 | 0.0103 | 0.8559 | - | | 2.4355 | 6800 | 0.0072 | 0.0102 | 0.8550 | - | | 2.4713 | 6900 | 0.0069 | 0.0098 | 0.8618 | - | | 2.5072 | 7000 | 0.0082 | 0.0099 | 0.8611 | - | | 2.5430 | 7100 | 0.0067 | 0.0101 | 0.8596 | - | | 2.5788 | 7200 | 0.0062 | 0.0097 | 0.8593 | - | | 2.6146 | 7300 | 0.0074 | 0.0094 | 0.8622 | - | | 2.6504 | 7400 | 0.008 | 0.0093 | 0.8624 | - | | 2.6862 | 7500 | 0.0066 | 0.0097 | 0.8610 | - | | 2.7221 | 7600 | 0.0066 | 0.0098 | 0.8616 | - | | 2.7579 | 7700 | 0.0066 | 0.0097 | 0.8593 | - | | 2.7937 | 7800 | 0.0076 | 0.0099 | 0.8582 | - | | 2.8295 | 7900 | 0.0078 | 0.0094 | 0.8625 | - | | 2.8653 | 8000 | 0.0075 | 0.0092 | 0.8639 | - | | 2.9011 | 8100 | 0.0077 | 0.0092 | 0.8620 | - | | 2.9370 | 8200 | 0.0067 | 0.0092 | 0.8643 | - | | 2.9728 | 8300 | 0.0069 | 0.0095 | 0.8625 | - | | 3.0086 | 8400 | 0.0067 | 0.0095 | 0.8632 | - | | 3.0444 | 8500 | 0.0051 | 0.0093 | 0.8652 | - | | 3.0802 | 8600 | 0.0046 | 0.0094 | 0.8662 | - | | 3.1160 | 8700 | 0.0046 | 0.0094 | 0.8669 | - | | 3.1519 | 8800 | 0.0047 | 0.0095 | 0.8671 | - | | 3.1877 | 8900 | 0.0049 | 0.0091 | 0.8688 | - | | 3.2235 | 9000 | 0.0048 | 0.0090 | 0.8688 | - | | 3.2593 | 9100 | 0.0047 | 0.0092 | 0.8697 | - | | 3.2951 | 9200 | 0.0058 | 0.0092 | 0.8686 | - | | 3.3309 | 9300 | 0.005 | 0.0091 | 0.8681 | - | | 3.3668 | 9400 | 0.0049 | 0.0090 | 0.8694 | - | | 3.4026 | 9500 | 0.0051 | 0.0091 | 0.8670 | - | | 3.4384 | 9600 | 0.0048 | 0.0090 | 0.8666 | - | | 3.4742 | 9700 | 0.0047 | 0.0089 | 0.8672 | - | | 3.5100 | 9800 | 0.0046 | 0.0091 | 0.8658 | - | | 3.5458 | 9900 | 0.0051 | 0.0090 | 0.8658 | - | | 3.5817 | 10000 | 0.0054 | 0.0089 | 0.8681 | - | | 3.6175 | 10100 | 0.0049 | 0.0089 | 0.8679 | - | | 3.6533 | 10200 | 0.0042 | 0.0089 | 0.8681 | - | | 3.6891 | 10300 | 0.0049 | 0.0089 | 0.8684 | - | | 3.7249 | 10400 | 0.0046 | 0.0088 | 0.8692 | - | | 3.7607 | 10500 | 0.0048 | 0.0088 | 0.8691 | - | | 3.7966 | 10600 | 0.0042 | 0.0088 | 0.8704 | - | | 3.8324 | 10700 | 0.0049 | 0.0088 | 0.8702 | - | | 3.8682 | 10800 | 0.0045 | 0.0088 | 0.8709 | - | | 3.9040 | 10900 | 0.0047 | 0.0088 | 0.8712 | - | | 3.9398 | 11000 | 0.0046 | 0.0088 | 0.8711 | - | | 3.9756 | 11100 | 0.0045 | 0.0088 | 0.8714 | - | | 4.0 | 11168 | - | - | - | 0.8572 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.2.2+cu121 - Accelerate: 1.2.1 - 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", } ```