--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:64260 - loss:CosineSimilarityLoss base_model: yahyaabd/allstats-search-mini-v1-1-mnrl widget: - source_sentence: q-2216 sentences: - Statistik Potensi Desa Provinsi Jambi 2008 - Indeks Harga Sahsm - 17cb76daaeda2a9d92a30af3 - source_sentence: q-4069 sentences: - 61e74412ad7c948492537b61 - Ihpb Indonesia Tahun 2014 - Indeks Harga Perdagangan Besar Indonesia 2014, 2010=100 - source_sentence: q-748 sentences: - 20dac9022b69b62ab3479d37 - Statistik Potensi Desa Provinsi Sulawesi Utara 2014 - data potensi dpsa di Provinsi Sulawesi Utara tahun 2014 - source_sentence: q-7475 sentences: - Harga Konsumen Beberapa Barang dan Jasa Kelompok Kesehatan, Transportasi, dan Pendidikan 90 Kota di Indonesia 2021 - Volume ekspor CPO Indonesia - b2dbf308898a6d1748629240 - source_sentence: q-786 sentences: - Statistik eCommerce 2022/2023 - Angka Kematian Bayi oper P#rovinsi - f3b02f2b6706e104ea9d5b74 datasets: - yahyaabd/bps-pub-cosine-pairs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.9040861364751858 name: Pearson Cosine - type: spearman_cosine value: 0.8334861589775715 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.9069041337320248 name: Pearson Cosine - type: spearman_cosine value: 0.8380868510850786 name: Spearman Cosine --- # SentenceTransformer based on yahyaabd/allstats-search-mini-v1-1-mnrl This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [yahyaabd/allstats-search-mini-v1-1-mnrl](https://huggingface.co/yahyaabd/allstats-search-mini-v1-1-mnrl) on the [bps-pub-cosine-pairs](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-pairs) dataset. It maps sentences & paragraphs to a 384-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:** [yahyaabd/allstats-search-mini-v1-1-mnrl](https://huggingface.co/yahyaabd/allstats-search-mini-v1-1-mnrl) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [bps-pub-cosine-pairs](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-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': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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-search-mini-v2") # Run inference sentences = [ 'q-786', 'Angka Kematian Bayi oper P#rovinsi', 'f3b02f2b6706e104ea9d5b74', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.9041 | 0.9069 | | **spearman_cosine** | **0.8335** | **0.8381** | ## Training Details ### Training Dataset #### bps-pub-cosine-pairs * Dataset: [bps-pub-cosine-pairs](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-pairs) at [038a9de](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-pairs/tree/038a9de1c44c113be84c41cc01f75a2627dd735c) * Size: 64,260 training samples * Columns: query_id, query, corpus_id, title, and score * Approximate statistics based on the first 1000 samples: | | query_id | query | corpus_id | title | score | |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | string | string | float | | details | | | | | | * Samples: | query_id | query | corpus_id | title | score | |:--------------------|:---------------------------------|:--------------------------------------|:---------------------------------------------------------------------|:-----------------| | q-1599 | Nilai Tukar Nelayan | 0b0da8fc2b6af9329a6d9cfe | Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013 | 0.1 | | q-1599 | nilai tukar nelayan | 0b0da8fc2b6af9329a6d9cfe | Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013 | 0.1 | | q-1599 | NILAI TUKAR NELAYAN | 0b0da8fc2b6af9329a6d9cfe | Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013 | 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 #### bps-pub-cosine-pairs * Dataset: [bps-pub-cosine-pairs](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-pairs) at [038a9de](https://huggingface.co/datasets/yahyaabd/bps-pub-cosine-pairs/tree/038a9de1c44c113be84c41cc01f75a2627dd735c) * Size: 8,067 evaluation samples * Columns: query_id, query, corpus_id, title, and score * Approximate statistics based on the first 1000 samples: | | query_id | query | corpus_id | title | score | |:--------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | string | string | float | | details | | | | | | * Samples: | query_id | query | corpus_id | title | score | |:--------------------|:---------------------------------|:--------------------------------------|:---------------------------------------------------------------|:-----------------| | q-1273 | Sosek Desember 2021 | b7890a143bc751d1d84dcf4a | Laporan Bulanan Data Sosial Ekonomi Desember 2021 | 0.9 | | q-1273 | sosek desember 2021 | b7890a143bc751d1d84dcf4a | Laporan Bulanan Data Sosial Ekonomi Desember 2021 | 0.9 | | q-1273 | SOSEK DESEMBER 2021 | b7890a143bc751d1d84dcf4a | Laporan Bulanan Data Sosial Ekonomi Desember 2021 | 0.9 | * 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`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 1e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `label_smoothing_factor`: 0.01 - `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`: 1e-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`: 2 - `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`: 0.01 - `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 | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:--------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:| | 0 | 0 | - | 0.3848 | 0.8288 | - | | 0.0995 | 100 | 0.236 | 0.0950 | 0.8396 | - | | 0.1990 | 200 | 0.0655 | 0.0487 | 0.8452 | - | | 0.2985 | 300 | 0.0407 | 0.0342 | 0.8437 | - | | 0.3980 | 400 | 0.0309 | 0.0291 | 0.8427 | - | | 0.4975 | 500 | 0.0247 | 0.0253 | 0.8427 | - | | 0.5970 | 600 | 0.0211 | 0.0235 | 0.8427 | - | | 0.6965 | 700 | 0.0198 | 0.0224 | 0.8395 | - | | 0.7960 | 800 | 0.0168 | 0.0212 | 0.8405 | - | | 0.8955 | 900 | 0.0166 | 0.0206 | 0.8384 | - | | 0.9950 | 1000 | 0.0145 | 0.0195 | 0.8388 | - | | 1.0945 | 1100 | 0.0119 | 0.0193 | 0.8395 | - | | 1.1940 | 1200 | 0.0113 | 0.0190 | 0.8376 | - | | 1.2935 | 1300 | 0.0108 | 0.0189 | 0.8330 | - | | 1.3930 | 1400 | 0.0119 | 0.0180 | 0.8364 | - | | 1.4925 | 1500 | 0.0105 | 0.0184 | 0.8338 | - | | 1.5920 | 1600 | 0.0092 | 0.0180 | 0.8355 | - | | 1.6915 | 1700 | 0.009 | 0.0182 | 0.8319 | - | | 1.7910 | 1800 | 0.0096 | 0.0178 | 0.8337 | - | | 1.8905 | 1900 | 0.0099 | 0.0178 | 0.8326 | - | | **1.99** | **2000** | **0.0094** | **0.0178** | **0.8335** | **-** | | -1 | -1 | - | - | - | 0.8381 | * 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", } ```