Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,529 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2436
+- loss:CosineSimilarityLoss
+base_model: yahyaabd/allstats-search-mini-v1-1-mnrl
+widget:
+- source_sentence: Persentase penduduk usia 15-24 tahun di Kota Bandar Lampung yang
+    tidak sekolah dan tidak bekerja (NEET) adalah 10%.
+  sentences:
+  - Lembaga layanan menerima sekitar sepuluh ribu pengaduan kekerasan terhadap perempuan
+    pada 2023.
+  - Volume sampah plastik yang dihasilkan Kota Bandar Lampung setiap hari mencapai
+    100 ton.
+  - Komponen volatile foods mengalami deflasi 0,5 persen secara bulanan pada Mei 2025.
+- source_sentence: Jumlah pengaduan kasus pencemaran lingkungan yang diterima KLHK
+    pada tahun 2023 sebanyak 1.500 kasus.
+  sentences:
+  - Kualitas air laut di Teluk Jakarta tercemar berat akibat limbah industri dan domestik
+    dari daratan.
+  - 'Statistik Pengaduan Lingkungan Hidup: Jumlah Kasus Pencemaran Air, Udara, dan
+    Limbah B3 Menurut Provinsi dan Status Tindak Lanjut, Tahun 2023'
+  - Sosialisasi peta rawan bencana kepada masyarakat di daerah rentan perlu ditingkatkan
+    untuk meningkatkan kesiapsiagaan.
+- source_sentence: Pulau Lombok di Provinsi Nusa Tenggara Barat (NTB) memiliki Gunung
+    Rinjani.
+  sentences:
+  - Sektor yang paling diminati investor PMDN tahun 2023 adalah industri pengolahan.
+  - Persentase Penduduk Usia 25 Tahun Ke Atas Menurut Tingkat Pendidikan Tertinggi
+    yang Ditamatkan (Termasuk S1), Indonesia, 2024
+  - Ayam Taliwang adalah kuliner pedas khas NTB.
+- source_sentence: Luas terumbu karang yang mengalami pemutihan (bleaching) di perairan
+    Raja Ampat pada awal tahun 2024 mencapai 5% dari total area.
+  sentences:
+  - Jumlah Pompa Air dan Kapasitasnya untuk Penanganan Banjir Jakarta
+  - Kenaikan harga tiket pesawat rute Palembang-Jakarta terjadi menjelang libur Idul
+    Adha.
+  - Sekitar 5 persen dari total area terumbu karang di Raja Ampat terdampak fenomena
+    pemutihan pada awal 2024.
+- source_sentence: PDRB per kapita Provinsi Riau sangat dipengaruhi oleh harga minyak
+    bumi dunia.
+  sentences:
+  - Persentase Penduduk Lanjut Usia (60 Tahun Ke Atas) Menurut Provinsi (dalam Statistik
+    Penduduk Lanjut Usia Indonesia 2023)
+  - Di wilayah perkotaan, angka kemiskinan pada Maret 2023 adalah 7,29%.
+  - The Riau Islands province is known for its beautiful beaches and marine tourism.
+datasets:
+- yahyaabd/BPS-STS-dataset-v1
+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: 0.8598548892892474
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8569191140389504
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.8884601567043606
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8818393243914469
+      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-sts-dataset-v1](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1) 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-sts-dataset-v1](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1)
+<!-- - **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-v1-1-mnrl-1")
+# Run inference
+sentences = [
+    'PDRB per kapita Provinsi Riau sangat dipengaruhi oleh harga minyak bumi dunia.',
+    'The Riau Islands province is known for its beautiful beaches and marine tourism.',
+    'Di wilayah perkotaan, angka kemiskinan pada Maret 2023 adalah 7,29%.',
+]
+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      | 0.8599     | 0.8885     |
+| **spearman_cosine** | **0.8569** | **0.8818** |
+
+<!--
+## 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-sts-dataset-v1
+
+* Dataset: [bps-sts-dataset-v1](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1) at [5c8f96e](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1/tree/5c8f96e30c138042010e024d0a04ec82f5b36758)
+* Size: 2,436 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 20.49 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.71 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                | sentence2                                                                                                   | score            |
+  |:-----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>bagaimana capaian Tujuan Pembangunan Berkelanjutan di Indonesia?</code>            | <code>Laporan Pencapaian Indikator Tujuan Pembangunan Berkelanjutan (TPB/SDGs) Indonesia, Edisi 2024</code> | <code>0.8</code> |
+  | <code>Jumlah perpustakaan umum di Indonesia tahun 2022 sebanyak 170.000 unit.</code>     | <code>Minat baca masyarakat Indonesia masih perlu ditingkatkan melalui berbagai program literasi.</code>    | <code>0.4</code> |
+  | <code>Jumlah sekolah negeri jenjang SMP di Kota Bandar Lampung adalah 30 sekolah.</code> | <code>Laju deforestasi di Provinsi Kalimantan Tengah masih mengkhawatirkan.</code>                          | <code>0.0</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-sts-dataset-v1
+
+* Dataset: [bps-sts-dataset-v1](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1) at [5c8f96e](https://huggingface.co/datasets/yahyaabd/BPS-STS-dataset-v1/tree/5c8f96e30c138042010e024d0a04ec82f5b36758)
+* Size: 522 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 522 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                         |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 20.83 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.84 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                  | sentence2                                                                                                                                                   | score            |
+  |:---------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Persentase desa yang memiliki fasilitas internet di Provinsi Y pada tahun 2021 adalah 85%.</code>                    | <code>Luas perkebunan kelapa sawit di Provinsi Y pada tahun 2021 adalah 500.000 hektar.</code>                                                              | <code>0.2</code> |
+  | <code>Kontribusi sektor UMKM terhadap PDRB Kota Malang pada tahun 2023 sebesar 60%.</code>                                 | <code>Usaha Mikro, Kecil, dan Menengah menyumbang 60 persen terhadap total Produk Domestik Regional Bruto di kota pendidikan Malang pada tahun 2023.</code> | <code>1.0</code> |
+  | <code>Jumlah Industri Kecil dan Menengah (IKM) di Kabupaten Tegal, Jawa Tengah, bertambah 200 unit pada tahun 2024.</code> | <code>Di Tegal, sebuah kabupaten di Jateng, terjadi penambahan 200 unit IKM sepanjang tahun 2024.</code>                                                    | <code>1.0</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`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 6
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 6
+- `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}
+- `tp_size`: 0
+- `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
+- `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       | -             | 0.0588          | 0.7404                  | -                        |
+| 0.0654     | 10      | 0.0541        | 0.0586          | 0.7412                  | -                        |
+| 0.1307     | 20      | 0.0546        | 0.0579          | 0.7444                  | -                        |
+| 0.1961     | 30      | 0.0441        | 0.0565          | 0.7500                  | -                        |
+| 0.2614     | 40      | 0.0503        | 0.0546          | 0.7580                  | -                        |
+| 0.3268     | 50      | 0.0546        | 0.0528          | 0.7648                  | -                        |
+| 0.3922     | 60      | 0.0538        | 0.0509          | 0.7739                  | -                        |
+| 0.4575     | 70      | 0.0455        | 0.0490          | 0.7834                  | -                        |
+| 0.5229     | 80      | 0.0471        | 0.0472          | 0.7925                  | -                        |
+| 0.5882     | 90      | 0.0417        | 0.0455          | 0.8017                  | -                        |
+| 0.6536     | 100     | 0.0427        | 0.0441          | 0.8095                  | -                        |
+| 0.7190     | 110     | 0.0445        | 0.0432          | 0.8138                  | -                        |
+| 0.7843     | 120     | 0.0382        | 0.0425          | 0.8168                  | -                        |
+| 0.8497     | 130     | 0.0443        | 0.0413          | 0.8220                  | -                        |
+| 0.9150     | 140     | 0.0449        | 0.0405          | 0.8264                  | -                        |
+| 0.9804     | 150     | 0.0407        | 0.0401          | 0.8287                  | -                        |
+| 1.0458     | 160     | 0.0377        | 0.0400          | 0.8312                  | -                        |
+| 1.1111     | 170     | 0.0285        | 0.0392          | 0.8327                  | -                        |
+| 1.1765     | 180     | 0.033         | 0.0389          | 0.8329                  | -                        |
+| 1.2418     | 190     | 0.0299        | 0.0388          | 0.8331                  | -                        |
+| 1.3072     | 200     | 0.029         | 0.0387          | 0.8333                  | -                        |
+| 1.3725     | 210     | 0.031         | 0.0384          | 0.8340                  | -                        |
+| 1.4379     | 220     | 0.0274        | 0.0384          | 0.8351                  | -                        |
+| 1.5033     | 230     | 0.0312        | 0.0382          | 0.8367                  | -                        |
+| 1.5686     | 240     | 0.0301        | 0.0378          | 0.8383                  | -                        |
+| 1.6340     | 250     | 0.0304        | 0.0375          | 0.8390                  | -                        |
+| 1.6993     | 260     | 0.0226        | 0.0374          | 0.8389                  | -                        |
+| 1.7647     | 270     | 0.0264        | 0.0373          | 0.8399                  | -                        |
+| 1.8301     | 280     | 0.0295        | 0.0370          | 0.8418                  | -                        |
+| 1.8954     | 290     | 0.0298        | 0.0368          | 0.8419                  | -                        |
+| 1.9608     | 300     | 0.0291        | 0.0366          | 0.8422                  | -                        |
+| 2.0261     | 310     | 0.0279        | 0.0365          | 0.8426                  | -                        |
+| 2.0915     | 320     | 0.0231        | 0.0363          | 0.8432                  | -                        |
+| 2.1569     | 330     | 0.0249        | 0.0361          | 0.8446                  | -                        |
+| 2.2222     | 340     | 0.0253        | 0.0359          | 0.8454                  | -                        |
+| 2.2876     | 350     | 0.024         | 0.0358          | 0.8463                  | -                        |
+| 2.3529     | 360     | 0.0239        | 0.0357          | 0.8471                  | -                        |
+| 2.4183     | 370     | 0.0222        | 0.0355          | 0.8473                  | -                        |
+| 2.4837     | 380     | 0.0284        | 0.0354          | 0.8476                  | -                        |
+| 2.5490     | 390     | 0.0176        | 0.0353          | 0.8486                  | -                        |
+| 2.6144     | 400     | 0.0184        | 0.0352          | 0.8489                  | -                        |
+| 2.6797     | 410     | 0.023         | 0.0351          | 0.8495                  | -                        |
+| 2.7451     | 420     | 0.0201        | 0.0351          | 0.8494                  | -                        |
+| 2.8105     | 430     | 0.0252        | 0.0351          | 0.8499                  | -                        |
+| 2.8758     | 440     | 0.0206        | 0.0350          | 0.8503                  | -                        |
+| 2.9412     | 450     | 0.0188        | 0.0350          | 0.8499                  | -                        |
+| 3.0065     | 460     | 0.017         | 0.0348          | 0.8501                  | -                        |
+| 3.0719     | 470     | 0.0174        | 0.0347          | 0.8505                  | -                        |
+| 3.1373     | 480     | 0.0171        | 0.0345          | 0.8515                  | -                        |
+| 3.2026     | 490     | 0.0226        | 0.0344          | 0.8520                  | -                        |
+| 3.2680     | 500     | 0.0233        | 0.0344          | 0.8520                  | -                        |
+| 3.3333     | 510     | 0.0177        | 0.0344          | 0.8523                  | -                        |
+| 3.3987     | 520     | 0.0155        | 0.0343          | 0.8522                  | -                        |
+| 3.4641     | 530     | 0.0155        | 0.0344          | 0.8522                  | -                        |
+| 3.5294     | 540     | 0.0249        | 0.0343          | 0.8523                  | -                        |
+| 3.5948     | 550     | 0.0177        | 0.0343          | 0.8522                  | -                        |
+| 3.6601     | 560     | 0.0149        | 0.0343          | 0.8520                  | -                        |
+| 3.7255     | 570     | 0.0178        | 0.0343          | 0.8517                  | -                        |
+| 3.7908     | 580     | 0.0181        | 0.0343          | 0.8520                  | -                        |
+| 3.8562     | 590     | 0.018         | 0.0342          | 0.8525                  | -                        |
+| 3.9216     | 600     | 0.0178        | 0.0341          | 0.8525                  | -                        |
+| 3.9869     | 610     | 0.0225        | 0.0340          | 0.8530                  | -                        |
+| 4.0523     | 620     | 0.0194        | 0.0339          | 0.8541                  | -                        |
+| 4.1176     | 630     | 0.0145        | 0.0338          | 0.8548                  | -                        |
+| 4.1830     | 640     | 0.0151        | 0.0337          | 0.8554                  | -                        |
+| 4.2484     | 650     | 0.0187        | 0.0336          | 0.8560                  | -                        |
+| 4.3137     | 660     | 0.0142        | 0.0336          | 0.8561                  | -                        |
+| 4.3791     | 670     | 0.0162        | 0.0336          | 0.8557                  | -                        |
+| 4.4444     | 680     | 0.0167        | 0.0335          | 0.8558                  | -                        |
+| 4.5098     | 690     | 0.013         | 0.0335          | 0.8555                  | -                        |
+| 4.5752     | 700     | 0.0174        | 0.0336          | 0.8555                  | -                        |
+| 4.6405     | 710     | 0.0156        | 0.0336          | 0.8556                  | -                        |
+| 4.7059     | 720     | 0.0155        | 0.0336          | 0.8555                  | -                        |
+| 4.7712     | 730     | 0.0179        | 0.0336          | 0.8553                  | -                        |
+| 4.8366     | 740     | 0.0158        | 0.0335          | 0.8553                  | -                        |
+| 4.9020     | 750     | 0.0143        | 0.0335          | 0.8553                  | -                        |
+| 4.9673     | 760     | 0.019         | 0.0335          | 0.8557                  | -                        |
+| 5.0327     | 770     | 0.0143        | 0.0334          | 0.8559                  | -                        |
+| 5.0980     | 780     | 0.0136        | 0.0334          | 0.8559                  | -                        |
+| 5.1634     | 790     | 0.0138        | 0.0334          | 0.8560                  | -                        |
+| 5.2288     | 800     | 0.0134        | 0.0333          | 0.8561                  | -                        |
+| 5.2941     | 810     | 0.0173        | 0.0333          | 0.8563                  | -                        |
+| 5.3595     | 820     | 0.0128        | 0.0333          | 0.8562                  | -                        |
+| 5.4248     | 830     | 0.0145        | 0.0333          | 0.8564                  | -                        |
+| 5.4902     | 840     | 0.0153        | 0.0333          | 0.8566                  | -                        |
+| 5.5556     | 850     | 0.0166        | 0.0333          | 0.8566                  | -                        |
+| 5.6209     | 860     | 0.0179        | 0.0332          | 0.8569                  | -                        |
+| 5.6863     | 870     | 0.0151        | 0.0332          | 0.8569                  | -                        |
+| 5.7516     | 880     | 0.0168        | 0.0332          | 0.8570                  | -                        |
+| 5.8170     | 890     | 0.0129        | 0.0332          | 0.8570                  | -                        |
+| 5.8824     | 900     | 0.015         | 0.0332          | 0.8569                  | -                        |
+| **5.9477** | **910** | **0.0148**    | **0.0332**      | **0.8569**              | **-**                    |
+| -1         | -1      | -             | -               | -                       | 0.8818                   |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 3.4.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.2.0
+- 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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:34cdffa42f793993b94f4d58b8d3346f650525997d2a01db8cf4e2f5e806aa19
+size 470637416

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

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+version https://git-lfs.github.com/spec/v1
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+size 14763260