--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:967831 - loss:MultipleNegativesRankingLoss base_model: denaya/indoSBERT-large widget: - source_sentence: Penghasilan freelancer per provinsi, beda umur 2016 sentences: - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur (ribu rupiah), 2016 - Konkordansi Klasifikasi Tabel Inter Regional Input-Output Indonesia, 2016 (52 Industri - 17 Lapangan Usaha) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Maluku, 2018-2023 - source_sentence: Tren angka partisipasi sekolah di Indonesia (7-23 tahun) berdasarkan gender dan kelompok umur, 2015-2023 sentences: - Jumlah Sekolah, Guru, dan Murid Sekolah Dasar (SD) di Bawah Kementerian Pendidikan dan Kebudayaan Menurut Provinsi Tahun Ajaran 2011/2012-2015/2016 - Rata-rata Harian Konsumsi Protein Per Kapita dan Konsumsi Kalori Per Kapita Tahun 1990 - 2023 - Persentase Penduduk Usia 7-23 Tahun Menurut Jenis Kelamin, Kelompok Umur Sekolah, dan Partisipasi Sekolah, 2015-2023 - source_sentence: Sumber penerangan rumah tangga per provinsi Indonesia 2018 sentences: - Nutrisi - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2016 - Persentase Rumah Tangga Menurut Provinsi dan Sumber Penerangan, 2015-2021 - source_sentence: Rumah tangga dengan lampu hemat energi per provinsi, 2014 vs 2021 (urban vs rural) sentences: - Persentase Rumah Tangga yang Menggunakan Lampu Hemat Energi Menurut Provinsi dan Daerah Tempat Tinggal, 2014, 2021 - Luas Daerah Pengaliran dan Debit dari Beberapa Sungai yang Daerah Pengalirannya Lebih dari 100 km2, 2015 - Perolehan Suara dan Kursi Dewan Perwakilan Rakyat (DPR) Menurut Partai Politik Hasil Pemilu Tahun 2009 dan 2014 - source_sentence: 'Upah bulanan rata-rata: Hubungan pendidikan tertinggi dan sektor pekerjaan utama, data 2021' sentences: - IHK dan Rata-rata Upah per Bulan Buruh Peternakan dan Perikanan di Bawah Mandor (Supervisor), 2007-2014 (2007=100) - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jenis Pekerjaan Utama, 2021 - Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021 datasets: - yahyaabd/statictable-triplets-all pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@1 - cosine_ndcg@5 - cosine_ndcg@10 - cosine_mrr@1 - cosine_mrr@5 - cosine_mrr@10 - cosine_map@1 - cosine_map@5 - cosine_map@10 model-index: - name: SentenceTransformer based on denaya/indoSBERT-large results: - task: type: information-retrieval name: Information Retrieval dataset: name: bps statictable ir type: bps-statictable-ir metrics: - type: cosine_accuracy@1 value: 0.9218241042345277 name: Cosine Accuracy@1 - type: cosine_accuracy@5 value: 0.990228013029316 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.996742671009772 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9218241042345277 name: Cosine Precision@1 - type: cosine_precision@5 value: 0.2247557003257329 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13159609120521173 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7225077889088528 name: Cosine Recall@1 - type: cosine_recall@5 value: 0.793020064240505 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8181542032723246 name: Cosine Recall@10 - type: cosine_ndcg@1 value: 0.9218241042345277 name: Cosine Ndcg@1 - type: cosine_ndcg@5 value: 0.8340748596494166 name: Cosine Ndcg@5 - type: cosine_ndcg@10 value: 0.8332473439965864 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.9218241042345277 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.9522258414766559 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.9532340623545834 name: Cosine Mrr@10 - type: cosine_map@1 value: 0.9218241042345277 name: Cosine Map@1 - type: cosine_map@5 value: 0.7919598262757872 name: Cosine Map@5 - type: cosine_map@10 value: 0.7847729133274736 name: Cosine Map@10 --- # 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 [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) 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:** - [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) ### 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/indoSBERT-Large-mnrl-2") # Run inference sentences = [ 'Upah bulanan rata-rata: Hubungan pendidikan tertinggi dan sektor pekerjaan utama, data 2021', 'Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021', 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Jenis Pekerjaan Utama, 2021', ] 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 #### Information Retrieval * Dataset: `bps-statictable-ir` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9218 | | cosine_accuracy@5 | 0.9902 | | cosine_accuracy@10 | 0.9967 | | cosine_precision@1 | 0.9218 | | cosine_precision@5 | 0.2248 | | cosine_precision@10 | 0.1316 | | cosine_recall@1 | 0.7225 | | cosine_recall@5 | 0.793 | | cosine_recall@10 | 0.8182 | | cosine_ndcg@1 | 0.9218 | | cosine_ndcg@5 | 0.8341 | | **cosine_ndcg@10** | **0.8332** | | cosine_mrr@1 | 0.9218 | | cosine_mrr@5 | 0.9522 | | cosine_mrr@10 | 0.9532 | | cosine_map@1 | 0.9218 | | cosine_map@5 | 0.792 | | cosine_map@10 | 0.7848 | ## Training Details ### Training Dataset #### statictable-triplets-all * Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030) * Size: 967,831 training samples * Columns: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------| | Data input-output antar daerah, 34 provinsi: Transaksi domestik (52 industri, harga produsen, 2016) | Tabel Inter Regional Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen Menurut 34 Provinsi dan 52 Industri, 2016 (Juta Rupiah) | Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024 | | Data total penghasilan berbagai golongan rumah tangga setelah dipotong pajak, tahun 2000 (dalam miliar rupiah) | Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008 | Institusi Korporasi Non Finansial Neraca Institusi Terintegrasi ( triliun rupiah), 2016 2022 | | Rumah tangga dengan area resapan, data per provinsi, 2014 | Persentase Rumah Tangga Menurut Provinsi dan Keberadaan Area Resapan Air, 2013-2014 | Nilai Produksi dan Biaya Produksi per Musim Tanam per Hektar Budidaya Tanaman Padi Sawah, Padi Ladang, Jagung, dan Kedelai, 2017 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### statictable-triplets-all * Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030) * Size: 967,831 evaluation samples * Columns: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Kredit UMKM bank umum (miliar rupiah), 2012-2016 | Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar rupiah), 2012-2016 | Jumlah Penghuni Lapas per Kanwil | | Infant Mortality Rate di Indonesia per provinsi, 1971 | Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020 | Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Kejuruan (SMK) di Bawah Kementrian Pendidikan dan Kebudayaan Menurut Provinsi tahun ajaran 2011/2012-2015/2016 | | Partisipasi sekolah anak dan remaja: Data persentase usia 7-24 tahun per gender dan kelompok umur, 2021 | Persentase Penduduk Usia 7-24 Tahun Menurut Jenis Kelamin, Kelompok Umur, dan Partisipasi Sekolah, 2002-2023 | Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Pembeli (17 Produk), 2016 (Juta Rupiah) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `eval_on_start`: True - `batch_sampler`: no_duplicates #### 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`: 1 - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | bps-statictable-ir_cosine_ndcg@10 | |:----------:|:-------:|:-------------:|:---------------:|:---------------------------------:| | 0 | 0 | - | 0.7678 | 0.7378 | | 0.1391 | 100 | 0.2164 | 0.0292 | 0.8324 | | 0.2782 | 200 | 0.032 | 0.0143 | 0.8383 | | 0.4172 | 300 | 0.0221 | 0.0077 | 0.8392 | | 0.5563 | 400 | 0.0088 | 0.0055 | 0.8391 | | 0.6954 | 500 | 0.0058 | 0.0033 | 0.8301 | | **0.8345** | **600** | **0.0039** | **0.0016** | **0.8331** | | 0.9736 | 700 | 0.0027 | 0.0019 | 0.8332 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.3.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```