| --- |
| 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) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| - **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) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Datasets: `allstats-semantic-mpnet-v2-eval` and `allstats-semantic-mpnet-v2-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](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** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 11.28 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.71 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| |
| | <code>Data bisnis Kalbar sensus 2016</code> | <code>Indikator Ekonomi Oktober 2012</code> | <code>0.1</code> | |
| | <code>Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan?</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023</code> | <code>0.88</code> | |
| | <code>Laopran keuagnan lmebaga non proft 20112-013</code> | <code>Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013</code> | <code>0.93</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 11.52 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.38 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------| |
| | <code>Data transportasi bulan Februari 2021</code> | <code>Tenaga Kerja Februari 2023</code> | <code>0.08</code> | |
| | <code>Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202?</code> | <code>Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen</code> | <code>1.0</code> | |
| | <code>Data ekspor dan moda transportasi tahun 2018-2019</code> | <code>Indikator Pasar Tenaga Kerja Indonesia Agustus 2012</code> | <code>0.08</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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 |
| <details><summary>Click to expand</summary> |
|
|
| - `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 |
|
|
| </details> |
|
|
| ### 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", |
| } |
| ``` |
|
|
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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
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| ## Model Card Authors |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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