| --- |
| 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) <!-- at revision 117ddf58a25bdde8ba44b3c0e1bff6582bc34d17 --> |
| - **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) |
| <!-- - **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: 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] |
| ``` |
|
|
| <!-- |
| ### 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: `sts-dev` and `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](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** | |
| |
| <!-- |
| ## 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 |
| |
| #### 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: <code>query_id</code>, <code>query</code>, <code>corpus_id</code>, <code>title</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query_id | query | corpus_id | title | score | |
| |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | string | string | float | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 5.18 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.33 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.38 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.13 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.56</li><li>max: 0.9</li></ul> | |
| * Samples: |
| | query_id | query | corpus_id | title | score | |
| |:--------------------|:---------------------------------|:--------------------------------------|:---------------------------------------------------------------------|:-----------------| |
| | <code>q-1599</code> | <code>Nilai Tukar Nelayan</code> | <code>0b0da8fc2b6af9329a6d9cfe</code> | <code>Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013</code> | <code>0.1</code> | |
| | <code>q-1599</code> | <code>nilai tukar nelayan</code> | <code>0b0da8fc2b6af9329a6d9cfe</code> | <code>Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013</code> | <code>0.1</code> | |
| | <code>q-1599</code> | <code>NILAI TUKAR NELAYAN</code> | <code>0b0da8fc2b6af9329a6d9cfe</code> | <code>Statistik Hotel dan Akomodasi Lainnya di Indonesia 2013</code> | <code>0.1</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 |
| |
| #### 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: <code>query_id</code>, <code>query</code>, <code>corpus_id</code>, <code>title</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query_id | query | corpus_id | title | score | |
| |:--------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | string | string | float | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 5.2 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.77 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 17.25 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.37 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.57</li><li>max: 0.9</li></ul> | |
| * Samples: |
| | query_id | query | corpus_id | title | score | |
| |:--------------------|:---------------------------------|:--------------------------------------|:---------------------------------------------------------------|:-----------------| |
| | <code>q-1273</code> | <code>Sosek Desember 2021</code> | <code>b7890a143bc751d1d84dcf4a</code> | <code>Laporan Bulanan Data Sosial Ekonomi Desember 2021</code> | <code>0.9</code> | |
| | <code>q-1273</code> | <code>sosek desember 2021</code> | <code>b7890a143bc751d1d84dcf4a</code> | <code>Laporan Bulanan Data Sosial Ekonomi Desember 2021</code> | <code>0.9</code> | |
| | <code>q-1273</code> | <code>SOSEK DESEMBER 2021</code> | <code>b7890a143bc751d1d84dcf4a</code> | <code>Laporan Bulanan Data Sosial Ekonomi Desember 2021</code> | <code>0.9</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`: 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 |
| <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`: 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 |
|
|
| </details> |
|
|
| ### 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
