--- base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 datasets: - yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:70280 - loss:CosineSimilarityLoss widget: - source_sentence: Data SBH tahun 2012 di Mamuju sentences: - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Harmonized System November 2013 - SBH 2012 - Mamuju - IHK di 66 Kota di Indonesia 2013 - source_sentence: Statistik konstruksi tahun 2020 sentences: - Indeks Ketimpangan Gender 2022 - Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020 - Perkembangan Beberapa Indikator Utama sosial-Ekonomi Indonesia Edisi Februari 2016 - source_sentence: Berapa besar inflasi pada bulan Oktober 2008? sentences: - Tinjauan Ekonomi Regional Indonesia Berdasarkan Data PDRB 2004-2008 Buku 2 - Statistik Sumber Daya Laut dan Pesisir 2020 - Inflasi September 2008 sebesar 0,97 persen. - source_sentence: 'Sektor konstruksi Indonesia: data statistik 1990-2013' sentences: - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Lapangan Pekerjaan Utama, 2023 - Direktori Perusahaan Kehutanan 2019 - Sensus Ekonomi 2006 Hasil Pendaftaran Perusahaan Sumatera Selatan - source_sentence: Perdagangan luar negeri, impor, Oktober 2020 sentences: - Indikator Ekonomi September 2005 - Statistik Potensi Desa Provinsi DI Yogyakarta 2005 - Indikator Ekonomi November 1999 model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic search mini v2 eval type: allstats-semantic-search-mini-v2-eval metrics: - type: pearson_cosine value: 0.9617082550278393 name: Pearson Cosine - type: spearman_cosine value: 0.8518022238549516 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic search mini v2 test type: allstat-semantic-search-mini-v2-test metrics: - type: pearson_cosine value: 0.9604638064122318 name: Pearson Cosine - type: spearman_cosine value: 0.8480797444308495 name: Spearman Cosine --- # 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 [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) 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:** - [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) ### 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-semantic-search-mini-model-v2-2") # Run inference sentences = [ 'Perdagangan luar negeri, impor, Oktober 2020', 'Indikator Ekonomi November 1999', 'Indikator Ekonomi September 2005', ] 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 #### Semantic Similarity * Datasets: `allstats-semantic-search-mini-v2-eval` and `allstat-semantic-search-mini-v2-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-search-mini-v2-eval | allstat-semantic-search-mini-v2-test | |:--------------------|:--------------------------------------|:-------------------------------------| | pearson_cosine | 0.9617 | 0.9605 | | **spearman_cosine** | **0.8518** | **0.8481** | ## Training Details ### Training Dataset #### allstats-semantic-search-synthetic-dataset-v2-mini * Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c) * Size: 70,280 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:------------------| | Statistik perusahaan pembudidaya tanaman kehutanan 2018 | Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018 | 0.97 | | Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002? | Inflasi Bulan November 2002 Sebesar 1,85 % | 0.0 | | Perdagangan luar negeri Indonesia, impor 2019, jilid 2 | Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat | 0.06 | * 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 #### allstats-semantic-search-synthetic-dataset-v2-mini * Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c) * Size: 15,060 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:----------------------------------------------------------------|:-----------------------------------------------------------------|:------------------| | Review PDRB daerah di Pulau Sumatera 2010-2013 | Statistik Pendidikan 2006 | 0.12 | | Analisis data angkatan kerja Agustus 2021 | Booklet Survei Angkatan Kerja Nasional Agustus 2021 | 0.9 | | Berapa persen inflasi yang terjadi pada Juli 2015? | Inflasi pada bulan lain tidak disebutkan | 0.0 | * 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`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 24 - `warmup_ratio`: 0.1 - `bf16`: 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`: 64 - `per_device_eval_batch_size`: 64 - `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`: 24 - `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`: True - `fp16`: False - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-mini-v2-eval_spearman_cosine | allstat-semantic-search-mini-v2-test_spearman_cosine | |:-------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------------:|:----------------------------------------------------:| | 0.4550 | 500 | 0.0643 | 0.0413 | 0.6996 | - | | 0.9099 | 1000 | 0.0348 | 0.0280 | 0.7533 | - | | 1.3649 | 1500 | 0.0254 | 0.0238 | 0.7737 | - | | 1.8198 | 2000 | 0.0223 | 0.0205 | 0.7831 | - | | 2.2748 | 2500 | 0.0181 | 0.0197 | 0.7894 | - | | 2.7298 | 3000 | 0.0173 | 0.0184 | 0.7876 | - | | 3.1847 | 3500 | 0.0152 | 0.0170 | 0.7954 | - | | 3.6397 | 4000 | 0.0123 | 0.0175 | 0.7970 | - | | 4.0946 | 4500 | 0.0125 | 0.0163 | 0.8118 | - | | 4.5496 | 5000 | 0.01 | 0.0161 | 0.8047 | - | | 5.0045 | 5500 | 0.0103 | 0.0157 | 0.8126 | - | | 5.4595 | 6000 | 0.0079 | 0.0150 | 0.8224 | - | | 5.9145 | 6500 | 0.0087 | 0.0156 | 0.8219 | - | | 6.3694 | 7000 | 0.0071 | 0.0152 | 0.8145 | - | | 6.8244 | 7500 | 0.0068 | 0.0153 | 0.8172 | - | | 7.2793 | 8000 | 0.0061 | 0.0147 | 0.8216 | - | | 7.7343 | 8500 | 0.0062 | 0.0146 | 0.8267 | - | | 8.1893 | 9000 | 0.0055 | 0.0145 | 0.8325 | - | | 8.6442 | 9500 | 0.005 | 0.0146 | 0.8335 | - | | 9.0992 | 10000 | 0.0052 | 0.0143 | 0.8356 | - | | 9.5541 | 10500 | 0.0043 | 0.0144 | 0.8313 | - | | 10.0091 | 11000 | 0.0051 | 0.0144 | 0.8362 | - | | 10.4641 | 11500 | 0.004 | 0.0145 | 0.8376 | - | | 10.9190 | 12000 | 0.0039 | 0.0142 | 0.8331 | - | | 11.3740 | 12500 | 0.0034 | 0.0141 | 0.8397 | - | | 11.8289 | 13000 | 0.0033 | 0.0140 | 0.8398 | - | | 12.2839 | 13500 | 0.0032 | 0.0143 | 0.8411 | - | | 12.7389 | 14000 | 0.003 | 0.0141 | 0.8407 | - | | 13.1938 | 14500 | 0.0031 | 0.0141 | 0.8379 | - | | 13.6488 | 15000 | 0.0026 | 0.0141 | 0.8419 | - | | 14.1037 | 15500 | 0.0028 | 0.0141 | 0.8442 | - | | 14.5587 | 16000 | 0.0023 | 0.0138 | 0.8455 | - | | 15.0136 | 16500 | 0.0025 | 0.0147 | 0.8359 | - | | 15.4686 | 17000 | 0.0021 | 0.0141 | 0.8459 | - | | 15.9236 | 17500 | 0.0023 | 0.0140 | 0.8433 | - | | 16.3785 | 18000 | 0.002 | 0.0139 | 0.8465 | - | | 16.8335 | 18500 | 0.002 | 0.0139 | 0.8461 | - | | 17.2884 | 19000 | 0.0018 | 0.0139 | 0.8482 | - | | 17.7434 | 19500 | 0.0018 | 0.0138 | 0.8477 | - | | 18.1984 | 20000 | 0.0017 | 0.0138 | 0.8503 | - | | 18.6533 | 20500 | 0.0016 | 0.0136 | 0.8493 | - | | 19.1083 | 21000 | 0.0016 | 0.0139 | 0.8501 | - | | 19.5632 | 21500 | 0.0015 | 0.0138 | 0.8478 | - | | 20.0182 | 22000 | 0.0015 | 0.0139 | 0.8501 | - | | 20.4732 | 22500 | 0.0013 | 0.0139 | 0.8508 | - | | 20.9281 | 23000 | 0.0015 | 0.0139 | 0.8511 | - | | 21.3831 | 23500 | 0.0013 | 0.0139 | 0.8517 | - | | 21.8380 | 24000 | 0.0013 | 0.0139 | 0.8512 | - | | 22.2930 | 24500 | 0.0012 | 0.0139 | 0.8512 | - | | 22.7480 | 25000 | 0.0012 | 0.0138 | 0.8520 | - | | 23.2029 | 25500 | 0.0012 | 0.0139 | 0.8520 | - | | 23.6579 | 26000 | 0.0011 | 0.0139 | 0.8518 | - | | 24.0 | 26376 | - | - | - | 0.8481 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## 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", } ```