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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25551
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/paraphrase-MiniLM-L12-v2
widget:
- source_sentence: Berapa gaji ratarata buruhkaryawan di Indonesia lihat dari umur
dan lapangan pekerjaannya 2019
sentences:
- Rasio laju peningkatan konsumsi tanah dengan laju pertumbuhan penduduk
- Rata-rata UpahGaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Kelompok Umur
dan lapangan pekerjaan utama, 2019
- Ringkasan Neraca Arus Dana, Triwulan Pertama, 2005, (Miliar Rupiah)
- source_sentence: Average monthly net wage/salary of employees by age group and type
of work (Rupiah), 2018
sentences:
- Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)
- Nilai Produksi dan Biaya Produksi Rumah Tangga Usaha Peternakan Menurut Jenis
Ternak, 2014
- Rekapitulasi Laporan Posisi Keuangan Perusahaan Penyelenggara Program Asuransi
Wajib dan BPJS Per 31 Desember (miliar rupiah) 2000-2021
- source_sentence: jumlah pembangunan fasilitas sekolah baru
sentences:
- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017
- Posisi Kredit Perbankan1dalam Rupiah dan Valuta Asing Menurut Sektor Ekonomi (miliar
rupiah), 2016-2018
- Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut
Provinsi/Kabupaten/Kota, 2020
- source_sentence: Data Pendapatan Rata-rata Orang Yang Berusaha Sendiri Per Provinsi,
Berdasarkan Lapangan Pekerjaan Utama (2020)
sentences:
- Nilai Pendapatan Disposabel Menurut Golongan Rumah Tangga (miliar rupiah), 2000,
2005, dan 2008
- IHK dan Rata-rata Upah per Bulan Buruh Pertambangan di Bawah Mandor (Supervisor),
1996-2014 (1996=100)
- Ringkasan Neraca Arus Dana Tahun 2004 (Miliar Rupiah)
- source_sentence: Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar
tayun 2000?
sentences:
- Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai
yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011
- Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut
Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024
- IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor),
1996-2014 (1996=100)
datasets:
- yahyaabd/query-hard-pos-neg-doc-pairs-statictable
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstats semantic mini v1 eval
type: allstats-semantic-mini-v1-eval
metrics:
- type: pearson_cosine
value: 0.8479971660039509
name: Pearson Cosine
- type: spearman_cosine
value: 0.7745638757528484
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat search mini v1 test
type: allstat-search-mini-v1-test
metrics:
- type: pearson_cosine
value: 0.8538445733470035
name: Pearson Cosine
- type: spearman_cosine
value: 0.7767623851780713
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) on the [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) 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:** [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) <!-- at revision 3f21b01a41e265ecb43cef6afeef20b7e578b637 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable)
<!-- - **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': 256, '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-miniLM-v1")
# Run inference
sentences = [
'Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar tayun 2000?',
'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)',
'Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011',
]
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: `allstats-semantic-mini-v1-eval` and `allstat-search-mini-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | allstats-semantic-mini-v1-eval | allstat-search-mini-v1-test |
|:--------------------|:-------------------------------|:----------------------------|
| pearson_cosine | 0.848 | 0.8538 |
| **spearman_cosine** | **0.7746** | **0.7768** |
<!--
## 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
#### query-hard-pos-neg-doc-pairs-statictable
* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
* Size: 25,551 training samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 9 tokens</li><li>mean: 28.64 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.67 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>0: ~65.80%</li><li>1: ~34.20%</li></ul> |
* Samples:
| query | doc | label |
|:-----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------|
| <code>Gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
| <code>gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
| <code>GAJI NOMINAL, INDEKS UPAH: NOMINAL & RIIL PEKERJA MANUFAKTUR NON-MANDOR (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### query-hard-pos-neg-doc-pairs-statictable
* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
* Size: 5,463 evaluation samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 29.3 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 37.1 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>0: ~73.20%</li><li>1: ~26.80%</li></ul> |
* Samples:
| query | doc | label |
|:------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Bagaimana penghasilan wirausahawan di Indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
| <code>bagaimana penghasilan wirausahawan di indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
| <code>BAGAIMANA PENGHASILAN WIRAUSAHAWAN DI INDONESIA BERVARIASI PER PROVINSI DAN JENIS PEKERJAAN UTAMA DI TAHUN 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 4
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mini-v1-eval_spearman_cosine | allstat-search-mini-v1-test_spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:-------------------------------------------:|
| 0 | 0 | - | 1.0797 | 0.5314 | - |
| 0.0250 | 20 | 1.2823 | 0.9331 | 0.5510 | - |
| 0.0501 | 40 | 0.9562 | 0.6159 | 0.6492 | - |
| 0.0751 | 60 | 0.5872 | 0.4629 | 0.6913 | - |
| 0.1001 | 80 | 0.4101 | 0.3605 | 0.7221 | - |
| 0.1252 | 100 | 0.419 | 0.3919 | 0.7301 | - |
| 0.1502 | 120 | 0.1517 | 0.2565 | 0.7457 | - |
| 0.1752 | 140 | 0.2678 | 0.2503 | 0.7484 | - |
| 0.2003 | 160 | 0.225 | 0.2010 | 0.7546 | - |
| 0.2253 | 180 | 0.2846 | 0.3203 | 0.7420 | - |
| 0.2503 | 200 | 0.2086 | 0.1981 | 0.7589 | - |
| 0.2753 | 220 | 0.1255 | 0.1982 | 0.7610 | - |
| 0.3004 | 240 | 0.1182 | 0.2328 | 0.7583 | - |
| 0.3254 | 260 | 0.1328 | 0.2218 | 0.7561 | - |
| 0.3504 | 280 | 0.1228 | 0.4583 | 0.7343 | - |
| 0.3755 | 300 | 0.1394 | 0.1785 | 0.7705 | - |
| 0.4005 | 320 | 0.2577 | 0.1800 | 0.7650 | - |
| 0.4255 | 340 | 0.1903 | 0.2680 | 0.7557 | - |
| 0.4506 | 360 | 0.1164 | 0.1761 | 0.7616 | - |
| 0.4756 | 380 | 0.0779 | 0.3318 | 0.7453 | - |
| 0.5006 | 400 | 0.1563 | 0.2209 | 0.7582 | - |
| 0.5257 | 420 | 0.1835 | 0.1683 | 0.7662 | - |
| 0.5507 | 440 | 0.1171 | 0.1537 | 0.7658 | - |
| 0.5757 | 460 | 0.0973 | 0.1381 | 0.7710 | - |
| 0.6008 | 480 | 0.0578 | 0.2303 | 0.7618 | - |
| 0.6258 | 500 | 0.1343 | 0.1431 | 0.7710 | - |
| 0.6508 | 520 | 0.1274 | 0.1646 | 0.7695 | - |
| 0.6758 | 540 | 0.057 | 0.1775 | 0.7606 | - |
| 0.7009 | 560 | 0.0392 | 0.1425 | 0.7689 | - |
| 0.7259 | 580 | 0.0434 | 0.1399 | 0.7712 | - |
| 0.7509 | 600 | 0.1311 | 0.1747 | 0.7670 | - |
| 0.7760 | 620 | 0.0475 | 0.1375 | 0.7709 | - |
| 0.8010 | 640 | 0.0183 | 0.1465 | 0.7685 | - |
| 0.8260 | 660 | 0.024 | 0.1666 | 0.7669 | - |
| 0.8511 | 680 | 0.0249 | 0.1728 | 0.7656 | - |
| 0.8761 | 700 | 0.041 | 0.1624 | 0.7711 | - |
| 0.9011 | 720 | 0.0835 | 0.1397 | 0.7716 | - |
| 0.9262 | 740 | 0.0404 | 0.1507 | 0.7693 | - |
| 0.9512 | 760 | 0.0141 | 0.1369 | 0.7723 | - |
| 0.9762 | 780 | 0.0513 | 0.1555 | 0.7687 | - |
| 1.0013 | 800 | 0.0387 | 0.1306 | 0.7717 | - |
| 1.0263 | 820 | 0.0393 | 0.1420 | 0.7707 | - |
| 1.0513 | 840 | 0.0153 | 0.1656 | 0.7700 | - |
| 1.0763 | 860 | 0.0263 | 0.1525 | 0.7694 | - |
| 1.1014 | 880 | 0.0503 | 0.1947 | 0.7638 | - |
| 1.1264 | 900 | 0.0215 | 0.2202 | 0.7615 | - |
| 1.1514 | 920 | 0.0217 | 0.1542 | 0.7696 | - |
| 1.1765 | 940 | 0.007 | 0.1394 | 0.7713 | - |
| 1.2015 | 960 | 0.018 | 0.1573 | 0.7706 | - |
| 1.2265 | 980 | 0.0446 | 0.1504 | 0.7686 | - |
| 1.2516 | 1000 | 0.026 | 0.1573 | 0.7661 | - |
| 1.2766 | 1020 | 0.0098 | 0.1429 | 0.7683 | - |
| 1.3016 | 1040 | 0.0196 | 0.1374 | 0.7702 | - |
| 1.3267 | 1060 | 0.021 | 0.1594 | 0.7685 | - |
| 1.3517 | 1080 | 0.0499 | 0.1378 | 0.7724 | - |
| 1.3767 | 1100 | 0.0165 | 0.1335 | 0.7729 | - |
| 1.4018 | 1120 | 0.0294 | 0.1451 | 0.7713 | - |
| 1.4268 | 1140 | 0.0114 | 0.1338 | 0.7717 | - |
| 1.4518 | 1160 | 0.0192 | 0.1327 | 0.7719 | - |
| 1.4768 | 1180 | 0.0335 | 0.1618 | 0.7646 | - |
| 1.5019 | 1200 | 0.0546 | 0.1389 | 0.7711 | - |
| 1.5269 | 1220 | 0.0069 | 0.1239 | 0.7738 | - |
| 1.5519 | 1240 | 0.0094 | 0.1180 | 0.7739 | - |
| 1.5770 | 1260 | 0.0074 | 0.1238 | 0.7733 | - |
| 1.6020 | 1280 | 0.0557 | 0.1428 | 0.7720 | - |
| 1.6270 | 1300 | 0.056 | 0.1159 | 0.7751 | - |
| 1.6521 | 1320 | 0.0 | 0.1244 | 0.7758 | - |
| 1.6771 | 1340 | 0.0066 | 0.1185 | 0.7735 | - |
| 1.7021 | 1360 | 0.0178 | 0.1016 | 0.7757 | - |
| 1.7272 | 1380 | 0.0156 | 0.0939 | 0.7776 | - |
| 1.7522 | 1400 | 0.0 | 0.1138 | 0.7761 | - |
| 1.7772 | 1420 | 0.0436 | 0.0980 | 0.7775 | - |
| 1.8023 | 1440 | 0.0626 | 0.1096 | 0.7763 | - |
| 1.8273 | 1460 | 0.0222 | 0.0968 | 0.7774 | - |
| 1.8523 | 1480 | 0.0101 | 0.1021 | 0.7762 | - |
| 1.8773 | 1500 | 0.0171 | 0.1076 | 0.7754 | - |
| 1.9024 | 1520 | 0.0064 | 0.1279 | 0.7730 | - |
| 1.9274 | 1540 | 0.0068 | 0.1237 | 0.7729 | - |
| 1.9524 | 1560 | 0.0066 | 0.1229 | 0.7733 | - |
| 1.9775 | 1580 | 0.0 | 0.1263 | 0.7731 | - |
| 2.0025 | 1600 | 0.0065 | 0.1152 | 0.7746 | - |
| 2.0275 | 1620 | 0.0147 | 0.1021 | 0.7773 | - |
| 2.0526 | 1640 | 0.0 | 0.1021 | 0.7773 | - |
| 2.0776 | 1660 | 0.0209 | 0.1017 | 0.7774 | - |
| 2.1026 | 1680 | 0.0 | 0.0993 | 0.7773 | - |
| 2.1277 | 1700 | 0.0067 | 0.0922 | 0.7784 | - |
| 2.1527 | 1720 | 0.0333 | 0.1158 | 0.7749 | - |
| 2.1777 | 1740 | 0.0 | 0.1397 | 0.7721 | - |
| 2.2028 | 1760 | 0.0158 | 0.1248 | 0.7751 | - |
| 2.2278 | 1780 | 0.0201 | 0.1021 | 0.7767 | - |
| 2.2528 | 1800 | 0.0 | 0.1029 | 0.7768 | - |
| 2.2778 | 1820 | 0.0107 | 0.1007 | 0.7767 | - |
| 2.3029 | 1840 | 0.0156 | 0.0923 | 0.7767 | - |
| 2.3279 | 1860 | 0.0 | 0.1012 | 0.7754 | - |
| 2.3529 | 1880 | 0.0131 | 0.1184 | 0.7731 | - |
| 2.3780 | 1900 | 0.0072 | 0.1113 | 0.7752 | - |
| 2.4030 | 1920 | 0.0337 | 0.0952 | 0.7775 | - |
| 2.4280 | 1940 | 0.0068 | 0.1086 | 0.7754 | - |
| 2.4531 | 1960 | 0.0 | 0.1194 | 0.7740 | - |
| 2.4781 | 1980 | 0.0176 | 0.1184 | 0.7747 | - |
| 2.5031 | 2000 | 0.0188 | 0.1123 | 0.7745 | - |
| 2.5282 | 2020 | 0.0 | 0.1138 | 0.7742 | - |
| 2.5532 | 2040 | 0.0 | 0.1141 | 0.7742 | - |
| 2.5782 | 2060 | 0.0269 | 0.1126 | 0.7743 | - |
| 2.6033 | 2080 | 0.0193 | 0.1470 | 0.7707 | - |
| 2.6283 | 2100 | 0.0074 | 0.1333 | 0.7726 | - |
| 2.6533 | 2120 | 0.0253 | 0.1004 | 0.7756 | - |
| 2.6783 | 2140 | 0.0 | 0.0980 | 0.7758 | - |
| 2.7034 | 2160 | 0.0 | 0.0984 | 0.7758 | - |
| 2.7284 | 2180 | 0.0 | 0.0984 | 0.7758 | - |
| 2.7534 | 2200 | 0.0 | 0.0984 | 0.7758 | - |
| 2.7785 | 2220 | 0.007 | 0.0971 | 0.7766 | - |
| 2.8035 | 2240 | 0.0 | 0.0998 | 0.7766 | - |
| 2.8285 | 2260 | 0.015 | 0.0988 | 0.7760 | - |
| 2.8536 | 2280 | 0.0 | 0.1020 | 0.7757 | - |
| 2.8786 | 2300 | 0.0 | 0.1023 | 0.7756 | - |
| 2.9036 | 2320 | 0.0 | 0.1023 | 0.7756 | - |
| 2.9287 | 2340 | 0.0 | 0.1023 | 0.7756 | - |
| 2.9537 | 2360 | 0.0075 | 0.1043 | 0.7751 | - |
| 2.9787 | 2380 | 0.0067 | 0.1125 | 0.7749 | - |
| 3.0038 | 2400 | 0.0 | 0.1083 | 0.7752 | - |
| 3.0288 | 2420 | 0.0 | 0.1083 | 0.7752 | - |
| 3.0538 | 2440 | 0.0 | 0.1083 | 0.7752 | - |
| 3.0788 | 2460 | 0.0063 | 0.1018 | 0.7755 | - |
| 3.1039 | 2480 | 0.0 | 0.1012 | 0.7756 | - |
| **3.1289** | **2500** | **0.0162** | **0.092** | **0.7768** | **-** |
| 3.1539 | 2520 | 0.01 | 0.0941 | 0.7768 | - |
| 3.1790 | 2540 | 0.0069 | 0.0946 | 0.7761 | - |
| 3.2040 | 2560 | 0.0 | 0.0956 | 0.7759 | - |
| 3.2290 | 2580 | 0.0 | 0.0956 | 0.7758 | - |
| 3.2541 | 2600 | 0.0 | 0.0956 | 0.7758 | - |
| 3.2791 | 2620 | 0.0 | 0.0956 | 0.7758 | - |
| 3.3041 | 2640 | 0.0131 | 0.0981 | 0.7756 | - |
| 3.3292 | 2660 | 0.0195 | 0.1142 | 0.7748 | - |
| 3.3542 | 2680 | 0.0 | 0.1172 | 0.7746 | - |
| 3.3792 | 2700 | 0.0065 | 0.1186 | 0.7748 | - |
| 3.4043 | 2720 | 0.0169 | 0.1184 | 0.7750 | - |
| 3.4293 | 2740 | 0.0 | 0.1175 | 0.7749 | - |
| 3.4543 | 2760 | 0.0 | 0.1165 | 0.7748 | - |
| 3.4793 | 2780 | 0.0105 | 0.1173 | 0.7747 | - |
| 3.5044 | 2800 | 0.0066 | 0.1123 | 0.7751 | - |
| 3.5294 | 2820 | 0.0 | 0.1103 | 0.7753 | - |
| 3.5544 | 2840 | 0.0 | 0.1106 | 0.7753 | - |
| 3.5795 | 2860 | 0.0139 | 0.1158 | 0.7745 | - |
| 3.6045 | 2880 | 0.0 | 0.1183 | 0.7741 | - |
| 3.6295 | 2900 | 0.0 | 0.1181 | 0.7741 | - |
| 3.6546 | 2920 | 0.0 | 0.1179 | 0.7741 | - |
| 3.6796 | 2940 | 0.0 | 0.1179 | 0.7741 | - |
| 3.7046 | 2960 | 0.0119 | 0.1172 | 0.7742 | - |
| 3.7297 | 2980 | 0.0068 | 0.1183 | 0.7742 | - |
| 3.7547 | 3000 | 0.0 | 0.1193 | 0.7741 | - |
| 3.7797 | 3020 | 0.0 | 0.1193 | 0.7741 | - |
| 3.8048 | 3040 | 0.0 | 0.1193 | 0.7741 | - |
| 3.8298 | 3060 | 0.0 | 0.1191 | 0.7741 | - |
| 3.8548 | 3080 | 0.0 | 0.1193 | 0.7741 | - |
| 3.8798 | 3100 | 0.0 | 0.1193 | 0.7741 | - |
| 3.9049 | 3120 | 0.0131 | 0.1165 | 0.7745 | - |
| 3.9299 | 3140 | 0.0 | 0.1159 | 0.7745 | - |
| 3.9549 | 3160 | 0.0 | 0.1158 | 0.7746 | - |
| 3.9800 | 3180 | 0.0 | 0.1153 | 0.7746 | - |
| -1 | -1 | - | - | - | 0.7768 |
* The bold row denotes the saved checkpoint.
</details>
### 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",
}
```
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