--- base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 datasets: - yahyaabd/allstats-semantic-dataset-v4 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:88250 - loss:CosineSimilarityLoss widget: - source_sentence: Laporan ekspor Indonesia Juli 2020 sentences: - Statistik Produksi Kehutanan 2021 - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juli 2020 - Statistik Politik 2017 - source_sentence: Bulan apa yang dicatat data kunjungan wisatawan mancanegara? sentences: - Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2005 - Data NTP bulan Maret 2022. - Kunjungan wisatawan mancanegara pada Oktober 2023 mencapai 978,50 ribu kunjungan, naik 33,27 persen (year-on-year) - source_sentence: Seberapa besar kenaikan upah nominal harian buruh tani nasional Januari 2016? sentences: - Keadaan Angkatan Kerja di Indonesia Mei 2013 - Profil Pasar Gorontalo 2020 - Tingkat pengangguran terbuka (TPT) Agustus 2024 sebesar 5,3%. - source_sentence: Ringkasan data statistik Indonesia 1997 sentences: - Statistik Upah 2007 - Harga konsumen bbrp jenis barang kelompok perumahan 2005 - Statistik Indonesia 1997 - source_sentence: Pernikahan usia anak di Indonesia periode 2013-2015 sentences: - Jumlah penduduk Indonesia 2013-2015 - Indikator Ekonomi Desember 2006 - Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2013 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 mpnet eval type: allstats-semantic-mpnet-eval metrics: - type: pearson_cosine value: 0.9714169395957917 name: Pearson Cosine - type: spearman_cosine value: 0.8933550959155299 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic mpnet test type: allstats-semantic-mpnet-test metrics: - type: pearson_cosine value: 0.9723087139367028 name: Pearson Cosine - type: spearman_cosine value: 0.8932385415736595 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 [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) 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:** - [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) ### 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-semantic-mpnet") # Run inference sentences = [ 'Pernikahan usia anak di Indonesia periode 2013-2015', 'Jumlah penduduk Indonesia 2013-2015', 'Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2013', ] 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-mpnet-eval` and `allstats-semantic-mpnet-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-mpnet-eval | allstats-semantic-mpnet-test | |:--------------------|:-----------------------------|:-----------------------------| | pearson_cosine | 0.9714 | 0.9723 | | **spearman_cosine** | **0.8934** | **0.8932** | ## Training Details ### Training Dataset #### allstats-semantic-dataset-v4 * Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [06c3cf8](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/06c3cf8715472fba6be04302a12790a6bd80443a) * Size: 88,250 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 | |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | Industri teh Indonesia tahun 2021 | Statistik Transportasi Laut 2014 | 0.1 | | Tahun berapa data pertumbuhan ekonomi Indonesia tersebut? | Nilai Tukar Petani (NTP) November 2023 sebesar 116,73 atau naik 0,82 persen. Harga Gabah Kering Panen di Tingkat Petani turun 1,94 persen dan Harga Beras Premium di Penggilingan turun 0,91 persen. | 0.0 | | Kemiskinan di Indonesia Maret | 2018 Feb Tenaga Kerja | 0.1 | * 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-dataset-v4 * Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [06c3cf8](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/06c3cf8715472fba6be04302a12790a6bd80443a) * Size: 18,910 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 | |:--------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:-----------------| | nAalisis keuangam deas tshun 019 | Statistik Migrasi Nusa Tenggara Barat Hasil Survei Penduduk Antar Sensus 2015 | 0.1 | | Data tanaman buah dan sayur Indonesia tahun 2016 | Statistik Penduduk Lanjut Usia 2010 | 0.1 | | Pasar beras di Indonesia tahun 2018 | Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, April 2021 | 0.2 | * 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`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 8 - `warmup_ratio`: 0.1 - `fp16`: True - `dataloader_num_workers`: 4 - `load_best_model_at_end`: True - `label_smoothing_factor`: 0.05 - `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`: 8 - `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`: 4 - `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`: 0.05 - `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-mpnet-eval_spearman_cosine | allstats-semantic-mpnet-test_spearman_cosine | |:----------:|:---------:|:-------------:|:---------------:|:--------------------------------------------:|:--------------------------------------------:| | 0 | 0 | - | 0.0979 | 0.6119 | - | | 0.0906 | 250 | 0.0646 | 0.0427 | 0.7249 | - | | 0.1813 | 500 | 0.039 | 0.0324 | 0.7596 | - | | 0.2719 | 750 | 0.032 | 0.0271 | 0.7860 | - | | 0.3626 | 1000 | 0.0276 | 0.0255 | 0.7920 | - | | 0.4532 | 1250 | 0.0264 | 0.0230 | 0.8072 | - | | 0.5439 | 1500 | 0.0249 | 0.0222 | 0.8197 | - | | 0.6345 | 1750 | 0.0226 | 0.0210 | 0.8200 | - | | 0.7252 | 2000 | 0.0218 | 0.0209 | 0.8202 | - | | 0.8158 | 2250 | 0.0208 | 0.0201 | 0.8346 | - | | 0.9065 | 2500 | 0.0209 | 0.0211 | 0.8240 | - | | 0.9971 | 2750 | 0.0211 | 0.0190 | 0.8170 | - | | 1.0877 | 3000 | 0.0161 | 0.0182 | 0.8332 | - | | 1.1784 | 3250 | 0.0158 | 0.0179 | 0.8393 | - | | 1.2690 | 3500 | 0.0167 | 0.0189 | 0.8341 | - | | 1.3597 | 3750 | 0.0152 | 0.0168 | 0.8371 | - | | 1.4503 | 4000 | 0.0151 | 0.0165 | 0.8435 | - | | 1.5410 | 4250 | 0.0143 | 0.0156 | 0.8365 | - | | 1.6316 | 4500 | 0.0147 | 0.0157 | 0.8467 | - | | 1.7223 | 4750 | 0.0138 | 0.0155 | 0.8501 | - | | 1.8129 | 5000 | 0.0147 | 0.0154 | 0.8457 | - | | 1.9036 | 5250 | 0.0137 | 0.0152 | 0.8498 | - | | 1.9942 | 5500 | 0.0144 | 0.0143 | 0.8485 | - | | 2.0848 | 5750 | 0.0108 | 0.0139 | 0.8439 | - | | 2.1755 | 6000 | 0.01 | 0.0146 | 0.8563 | - | | 2.2661 | 6250 | 0.011 | 0.0141 | 0.8558 | - | | 2.3568 | 6500 | 0.0107 | 0.0144 | 0.8497 | - | | 2.4474 | 6750 | 0.01 | 0.0138 | 0.8577 | - | | 2.5381 | 7000 | 0.0097 | 0.0136 | 0.8585 | - | | 2.6287 | 7250 | 0.0102 | 0.0135 | 0.8521 | - | | 2.7194 | 7500 | 0.0106 | 0.0133 | 0.8537 | - | | 2.8100 | 7750 | 0.0098 | 0.0133 | 0.8643 | - | | 2.9007 | 8000 | 0.0105 | 0.0138 | 0.8543 | - | | 2.9913 | 8250 | 0.009 | 0.0129 | 0.8555 | - | | 3.0819 | 8500 | 0.0071 | 0.0121 | 0.8692 | - | | 3.1726 | 8750 | 0.006 | 0.0120 | 0.8709 | - | | 3.2632 | 9000 | 0.0078 | 0.0120 | 0.8660 | - | | 3.3539 | 9250 | 0.0072 | 0.0122 | 0.8656 | - | | 3.4445 | 9500 | 0.007 | 0.0123 | 0.8696 | - | | 3.5352 | 9750 | 0.0075 | 0.0117 | 0.8707 | - | | 3.6258 | 10000 | 0.0081 | 0.0115 | 0.8682 | - | | 3.7165 | 10250 | 0.0083 | 0.0116 | 0.8617 | - | | 3.8071 | 10500 | 0.0075 | 0.0116 | 0.8665 | - | | 3.8978 | 10750 | 0.0077 | 0.0119 | 0.8733 | - | | 3.9884 | 11000 | 0.008 | 0.0113 | 0.8678 | - | | 4.0790 | 11250 | 0.0051 | 0.0110 | 0.8760 | - | | 4.1697 | 11500 | 0.0052 | 0.0108 | 0.8729 | - | | 4.2603 | 11750 | 0.0056 | 0.0108 | 0.8771 | - | | 4.3510 | 12000 | 0.0052 | 0.0109 | 0.8793 | - | | 4.4416 | 12250 | 0.0049 | 0.0109 | 0.8766 | - | | 4.5323 | 12500 | 0.0055 | 0.0114 | 0.8742 | - | | 4.6229 | 12750 | 0.0061 | 0.0108 | 0.8749 | - | | 4.7136 | 13000 | 0.0058 | 0.0109 | 0.8833 | - | | 4.8042 | 13250 | 0.0049 | 0.0108 | 0.8767 | - | | 4.8949 | 13500 | 0.0046 | 0.0108 | 0.8839 | - | | 4.9855 | 13750 | 0.0052 | 0.0104 | 0.8790 | - | | 5.0761 | 14000 | 0.0041 | 0.0102 | 0.8826 | - | | 5.1668 | 14250 | 0.004 | 0.0103 | 0.8775 | - | | 5.2574 | 14500 | 0.0036 | 0.0102 | 0.8855 | - | | 5.3481 | 14750 | 0.0037 | 0.0104 | 0.8841 | - | | 5.4387 | 15000 | 0.0036 | 0.0101 | 0.8860 | - | | 5.5294 | 15250 | 0.0043 | 0.0104 | 0.8852 | - | | 5.6200 | 15500 | 0.004 | 0.0100 | 0.8856 | - | | 5.7107 | 15750 | 0.0043 | 0.0101 | 0.8842 | - | | 5.8013 | 16000 | 0.0043 | 0.0099 | 0.8835 | - | | 5.8920 | 16250 | 0.0041 | 0.0099 | 0.8852 | - | | 5.9826 | 16500 | 0.0036 | 0.0101 | 0.8866 | - | | 6.0732 | 16750 | 0.0031 | 0.0100 | 0.8881 | - | | 6.1639 | 17000 | 0.0031 | 0.0098 | 0.8880 | - | | 6.2545 | 17250 | 0.0027 | 0.0098 | 0.8886 | - | | 6.3452 | 17500 | 0.0032 | 0.0097 | 0.8868 | - | | 6.4358 | 17750 | 0.0027 | 0.0097 | 0.8876 | - | | 6.5265 | 18000 | 0.0031 | 0.0097 | 0.8893 | - | | 6.6171 | 18250 | 0.0032 | 0.0096 | 0.8903 | - | | 6.7078 | 18500 | 0.003 | 0.0096 | 0.8898 | - | | 6.7984 | 18750 | 0.0029 | 0.0098 | 0.8907 | - | | 6.8891 | 19000 | 0.003 | 0.0096 | 0.8896 | - | | 6.9797 | 19250 | 0.0026 | 0.0096 | 0.8913 | - | | 7.0703 | 19500 | 0.0024 | 0.0096 | 0.8921 | - | | 7.1610 | 19750 | 0.0021 | 0.0097 | 0.8920 | - | | 7.2516 | 20000 | 0.0023 | 0.0096 | 0.8910 | - | | 7.3423 | 20250 | 0.002 | 0.0096 | 0.8920 | - | | 7.4329 | 20500 | 0.0022 | 0.0096 | 0.8924 | - | | 7.5236 | 20750 | 0.002 | 0.0097 | 0.8917 | - | | 7.6142 | 21000 | 0.0024 | 0.0096 | 0.8923 | - | | 7.7049 | 21250 | 0.0025 | 0.0095 | 0.8928 | - | | 7.7955 | 21500 | 0.0022 | 0.0095 | 0.8931 | - | | 7.8861 | 21750 | 0.0023 | 0.0095 | 0.8932 | - | | **7.9768** | **22000** | **0.0022** | **0.0095** | **0.8934** | **-** | | 8.0 | 22064 | - | - | - | 0.8932 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.48.0 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - 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", } ```