--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:23478 - loss:ContrastiveLoss base_model: denaya/indoSBERT-large widget: - source_sentence: 'Pekerja anak Indonesia: Buku panduan 2022 (pr & pasca pandemi)' sentences: - Statistik Perusahaan Hak Pengusahaan Hutan 2010 - Statistik Kriminal 2016 - ' Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, November 2020' - source_sentence: Jumlah pascr tradisional, pusat perbelanjaan, dan toko modern tahun 2019 sentences: - Profil Pasar Tradisional, Pusat Perbelanjaan, dan Toko Modern 2019 - Laporan Perekonomian Indonesia 2008 - Profil Industri Mikro dan Kecil 2006 - source_sentence: Survei biay ahidup (SBH) di Ternate tahun 2012 sentences: - Laporan Bulanan Data Sosial Ekonomi Januari 2016 - Keadaan Angkatan kerja di Indonesia Agustus 2009 - Statistik Perdagangan Luar Negeri Indonesia Impor 2023 Buku I - source_sentence: Direktori perwsahaan air minum, listrik, dan gas di kota tahun 2009 sentences: - Statistik Indonesia 1991 - Direktori Perusahaan Air Minum Listrik dan Gas Kota 2009 - Direktori Eksportir Indonesia 2015 - source_sentence: Studi efisiensi industri manufaktr sentences: - Statistik Indonesia 2019 - Statistik Potensi Desa Provinsi Maluku 2011 - Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4 datasets: - yahyaabd/bps-publication-pos-neg-pairs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on denaya/indoSBERT-large results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic base v1 eval type: allstats-semantic-base-v1-eval metrics: - type: pearson_cosine value: 0.9658815836712943 name: Pearson Cosine - type: spearman_cosine value: 0.7841756166101173 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic base v1 test type: allstat-semantic-base-v1-test metrics: - type: pearson_cosine value: 0.9592021090962591 name: Pearson Cosine - type: spearman_cosine value: 0.7818288777895762 name: Spearman Cosine --- # SentenceTransformer based on denaya/indoSBERT-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) on the [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) dataset. It maps sentences & paragraphs to a 256-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:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 256 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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}) (2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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-base-v1-3") # Run inference sentences = [ 'Studi efisiensi industri manufaktr', 'Statistik Potensi Desa Provinsi Maluku 2011', 'Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 256] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test | |:--------------------|:-------------------------------|:------------------------------| | pearson_cosine | 0.9659 | 0.9592 | | **spearman_cosine** | **0.7842** | **0.7818** | ## Training Details ### Training Dataset #### bps-publication-pos-neg-pairs * Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b) * Size: 23,478 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | doc | label | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------| | Direktori perusahaan perantara keuangan bukan koperasi tahun 2006 (SE) | Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2018-2022, Buku 2 Pulau Jawa-Bali | 0 | | Informasi lengkap tentang PPLS 2011 | Indeks Harga Perdagangan Besar Indonesia tahun 2005 | 0 | | Data konversi GKG ke beras tahun 2012 | Indikator Ekonomi Juli 2023 | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Dataset #### bps-publication-pos-neg-pairs * Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b) * Size: 5,031 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | doc | label | |:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------| | Informasi angka tanaman berkhasiat ogbat dan tanaman hias di tahun 2005 | Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2010-2013 - Buku 2 Pulau Jawa-Bali | 0 | | Informasi lengkap statistik horsikultura tahun 2020 | NERACA ENERGI INDONESIA 2017-2021 | 0 | | Statistik air bersih Indonesia periode 2014-2019 | Profil Usaha Konstruksi Perorangan Provinsi Kalimantan Utara, 2022 | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 8 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `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`: 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`: 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`: 0 - `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`: 0.0 - `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`: 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-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| | 0 | 0 | - | 0.0053 | 0.7770 | - | | 0.5450 | 200 | 0.0023 | 0.0005 | 0.7842 | - | | 1.0899 | 400 | 0.0005 | 0.0002 | 0.7842 | - | | 1.6349 | 600 | 0.0002 | 0.0002 | 0.7842 | - | | 2.1798 | 800 | 0.0001 | 0.0001 | 0.7842 | - | | 2.7248 | 1000 | 0.0001 | 0.0001 | 0.7842 | - | | 3.2698 | 1200 | 0.0 | 0.0001 | 0.7842 | - | | 3.8147 | 1400 | 0.0 | 0.0001 | 0.7842 | - | | 4.3597 | 1600 | 0.0 | 0.0001 | 0.7842 | - | | **4.9046** | **1800** | **0.0** | **0.0001** | **0.7842** | **-** | | 5.4496 | 2000 | 0.0 | 0.0001 | 0.7842 | - | | 5.9946 | 2200 | 0.0 | 0.0001 | 0.7842 | - | | 6.5395 | 2400 | 0.0 | 0.0001 | 0.7842 | - | | 7.0845 | 2600 | 0.0 | 0.0001 | 0.7842 | - | | 7.6294 | 2800 | 0.0 | 0.0001 | 0.7842 | - | | -1 | -1 | - | - | - | 0.7818 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.4.0 - Transformers: 4.48.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```