--- 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:73392 - loss:CosineSimilarityLoss widget: - source_sentence: Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum Nasional pada bulan April 2021? sentences: - Statistik Kriminal 2023 - Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y) - Survei Biaya Hidup (SBH) 2018 Ambon dan Tual - source_sentence: Usaha pertanian sampingan di Indonesia tahun 2022 sentences: - Analisis Hasil Survei Dampak Covid-19 Terhadap Pelaku Usaha - Direktori Usaha Pertanian Lainnya 2022 - EksporImpor September 2018 - source_sentence: Pertumbuhan industri Indonesia 2006-2009 sentences: - Pertumbuhan Produksi IBS Triwulan III 2019 Naik 4,35 Persen - Indikator Ekonomi April 2000 - Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009 - source_sentence: 'Sensus ekonomi Kalbar 2016: data usaha' sentences: - Pertumbuhan ekonomi Indonesia tahun 2022 - Buletin Statistik Perdagangan Luar Negeri Impor November 2017 - Data jumlah wisatawan mancanegara 2019 - source_sentence: Direktori perusahaan pengelola hutan 2015 sentences: - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, April 2017 - Direktori Perusahaan Kehutanan 2015 - Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39. 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 v2 eval type: allstats-semantic-mpnet-v2-eval metrics: - type: pearson_cosine value: 0.9437560461787071 name: Pearson Cosine - type: spearman_cosine value: 0.7866108512073439 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic mpnet v2 test type: allstats-semantic-mpnet-v2-test metrics: - type: pearson_cosine value: 0.9433638771860691 name: Pearson Cosine - type: spearman_cosine value: 0.7869770777792755 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-v2") # Run inference sentences = [ 'Direktori perusahaan pengelola hutan 2015', 'Direktori Perusahaan Kehutanan 2015', 'Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.', ] 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-v2-eval` and `allstats-semantic-mpnet-v2-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-mpnet-v2-eval | allstats-semantic-mpnet-v2-test | |:--------------------|:--------------------------------|:--------------------------------| | pearson_cosine | 0.9438 | 0.9434 | | **spearman_cosine** | **0.7866** | **0.787** | ## Training Details ### Training Dataset #### allstats-semantic-dataset-v4 * Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d) * Size: 73,392 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 | |:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| | Data bisnis Kalbar sensus 2016 | Indikator Ekonomi Oktober 2012 | 0.1 | | Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan? | Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023 | 0.88 | | Laopran keuagnan lmebaga non proft 20112-013 | Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013 | 0.93 | * 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 [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d) * Size: 15,726 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 | |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------| | Data transportasi bulan Februari 2021 | Tenaga Kerja Februari 2023 | 0.08 | | Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202? | Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen | 1.0 | | Data ekspor dan moda transportasi tahun 2018-2019 | Indikator Pasar Tenaga Kerja Indonesia Agustus 2012 | 0.08 | * 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`: 0.5 - `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`: 0.5 - `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-v2-eval_spearman_cosine | allstats-semantic-mpnet-v2-test_spearman_cosine | |:----------:|:--------:|:-------------:|:---------------:|:-----------------------------------------------:|:-----------------------------------------------:| | 0 | 0 | - | 0.1031 | 0.6244 | - | | 0.1090 | 250 | 0.0442 | 0.0321 | 0.7393 | - | | 0.2180 | 500 | 0.0295 | 0.0248 | 0.7641 | - | | 0.3269 | 750 | 0.0259 | 0.0224 | 0.7733 | - | | **0.4359** | **1000** | **0.0208** | **0.0199** | **0.7866** | **-** | | 0.5 | 1147 | - | - | - | 0.7870 | * 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", } ```