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allstats-search-mini-v1-1-mnrl-1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2436
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Model card Files
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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",
}
```
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