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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:967831
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Gaji pekerja berdasarkan jenis pekerjaan dan umur, 2016
sentences:
- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
dan Jenis Pekerjaan (Rupiah), 2016
- '[Seri 2010] PDRB Triwulanan Atas Dasar Harga Berlaku Menurut Lapangan Usaha di
Provinsi Seluruh Indonesia (Miliar Rupiah), 2010-2024'
- Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi
yang Ditamatkan, 2019
- source_sentence: Ke negara mana saja ekspor tanaman obat Indonesia tahun 2018?
sentences:
- Jumlah Rumah Tangga Perikanan Tangkap Menurut Provinsi dan Jenis Penangkapan,
2000-2016
- Perolehan Suara dan Kursi Dewan Perwakilan Rakyat (DPR) Menurut Partai Politik
Hasil Pemilu Tahun 2009 dan 2014
- Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama,
2012-2023
- source_sentence: Negara asal impor soybean 2023
sentences:
- Ringkasan Neraca Arus Dana, Triwulan III, 2010, (Miliar Rupiah)
- Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur
(ribu rupiah), 2018
- Impor Kedelai menurut Negara Asal Utama, 2017-2023
- source_sentence: Cek penghasilan bersih rata-rata yang didapat wiraswasta di Indonesia
tahun 2021, bedakan per provinsi dan ijazah terakhir
sentences:
- Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang
Ditamatkan, 2021
- Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sumatera Selatan, 2018-2023
- Impor Daging Sejenis Lembu menurut Negara Asal Utama, 2018-2023
- source_sentence: Status pernikahan penduduk (10+) tiap provinsi, data 2012
sentences:
- Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)
- Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023
- Persentase Penduduk Berumur 10 Tahun ke Atas menurut Provinsi, Jenis Kelamin,
dan Status Perkawinan, 2009-2018
datasets:
- yahyaabd/statictable-triplets-all
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: bps statictable ir
type: bps-statictable-ir
metrics:
- type: cosine_accuracy@1
value: 0.8990228013029316
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9739413680781759
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9804560260586319
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9869706840390879
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8990228013029316
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3517915309446254
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2299674267100977
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13420195439739416
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7037534704802675
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.777408879373005
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7896378239472596
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8147874661605627
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8242104501990923
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9361834961997827
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7641191235697605
name: Cosine Map@100
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) 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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all)
<!-- - **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-v1-2")
# Run inference
sentences = [
'Status pernikahan penduduk (10+) tiap provinsi, data 2012',
'Persentase Penduduk Berumur 10 Tahun ke Atas menurut Provinsi, Jenis Kelamin, dan Status Perkawinan, 2009-2018',
'Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023',
]
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
#### Information Retrieval
* Dataset: `bps-statictable-ir`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.899 |
| cosine_accuracy@3 | 0.9739 |
| cosine_accuracy@5 | 0.9805 |
| cosine_accuracy@10 | 0.987 |
| cosine_precision@1 | 0.899 |
| cosine_precision@3 | 0.3518 |
| cosine_precision@5 | 0.23 |
| cosine_precision@10 | 0.1342 |
| cosine_recall@1 | 0.7038 |
| cosine_recall@3 | 0.7774 |
| cosine_recall@5 | 0.7896 |
| cosine_recall@10 | 0.8148 |
| **cosine_ndcg@10** | **0.8242** |
| cosine_mrr@10 | 0.9362 |
| cosine_map@100 | 0.7641 |
<!--
## 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
#### statictable-triplets-all
* Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030)
* Size: 967,831 training samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
| | query | pos | neg |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.22 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.78 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
| query | pos | neg |
|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Jumlah bank dan kantor bank di Indonesia, 2010-2017</code> | <code>Bank dan Kantor Bank, 2010-2017</code> | <code>Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 1998-2012</code> |
| <code>Konsumsi makanan mingguan per orang di Sulteng: beda tingkat pengeluaran (2021)</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Selatan, 2018-2023</code> | <code>IHK, Upah Nominal, Indeks Upah Nominal dan Riil Buruh Industri Berstatus di bawah Mandor Menurut Wilayah, 2008-2014 (2007=100)</code> |
| <code>Impor semen Indonesia, negara asal utama, 2021</code> | <code>Impor Semen Menurut Negara Asal Utama, 2017-2023</code> | <code>Penerimaan dari Wisatawan Mancanegara Menurut Negara Tempat Tinggal (juta US$), 2000-2014</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### statictable-triplets-all
* Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030)
* Size: 967,831 evaluation samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
| | query | pos | neg |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 18.39 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.22 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
| query | pos | neg |
|:----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Bagaimana hubungan antara bidang pekerjaan utama dan pendidikan pekerja 15+ di minggu lalu (tahun 2016)?</code> | <code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 2008 - 2024</code> | <code>Bank dan Kantor Bank, 2010-2017</code> |
| <code>Tren indikator kondisi perumahan, 2001</code> | <code>Indikator Perumahan 1993-2023</code> | <code>Banyaknya Desa/Kelurahan Menurut Keberadaan Kelompok Pertokoan, Pasar, dan Kios Sarana Produksi Pertanian (Saprotan), 2014 & 2018</code> |
| <code>Gaji bersih rata-rata: Per pendidikan & lapangan kerja utama, Indonesia, 2021</code> | <code>Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021</code> | <code>[Seri 2000] Laju Pertumbuhan Kumulatif PDB Menurut Lapangan Usaha (Persen), 2001-2014</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `eval_on_start`: True
- `batch_sampler`: no_duplicates
#### 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`: 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`: 1
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | bps-statictable-ir_cosine_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:---------------------------------:|
| 0 | 0 | - | 1.1084 | 0.4644 |
| 0.0070 | 20 | 1.0801 | 0.8303 | 0.5117 |
| 0.0139 | 40 | 0.6994 | 0.4459 | 0.6310 |
| 0.0209 | 60 | 0.3674 | 0.2510 | 0.7155 |
| 0.0278 | 80 | 0.2814 | 0.1829 | 0.7521 |
| 0.0348 | 100 | 0.1746 | 0.1303 | 0.7751 |
| 0.0418 | 120 | 0.1867 | 0.1001 | 0.7772 |
| 0.0487 | 140 | 0.1047 | 0.0819 | 0.7857 |
| 0.0557 | 160 | 0.1032 | 0.0739 | 0.7960 |
| 0.0626 | 180 | 0.0783 | 0.0645 | 0.7861 |
| 0.0696 | 200 | 0.0575 | 0.0567 | 0.7849 |
| 0.0765 | 220 | 0.0969 | 0.0454 | 0.7945 |
| 0.0835 | 240 | 0.0769 | 0.0433 | 0.7890 |
| 0.0905 | 260 | 0.0864 | 0.0507 | 0.7848 |
| 0.0974 | 280 | 0.0495 | 0.0347 | 0.8052 |
| 0.1044 | 300 | 0.0429 | 0.0398 | 0.7955 |
| 0.1113 | 320 | 0.0432 | 0.0343 | 0.7915 |
| 0.1183 | 340 | 0.0392 | 0.0295 | 0.8177 |
| 0.1253 | 360 | 0.0211 | 0.0298 | 0.8052 |
| 0.1322 | 380 | 0.043 | 0.0339 | 0.8052 |
| 0.1392 | 400 | 0.0453 | 0.0322 | 0.8050 |
| 0.1461 | 420 | 0.0309 | 0.0286 | 0.8120 |
| 0.1531 | 440 | 0.0147 | 0.0321 | 0.8181 |
| 0.1601 | 460 | 0.0491 | 0.0273 | 0.8178 |
| 0.1670 | 480 | 0.0229 | 0.0232 | 0.8176 |
| 0.1740 | 500 | 0.0317 | 0.0210 | 0.8198 |
| 0.1809 | 520 | 0.0193 | 0.0207 | 0.8159 |
| 0.1879 | 540 | 0.034 | 0.0175 | 0.8191 |
| 0.1949 | 560 | 0.0292 | 0.0168 | 0.8166 |
| 0.2018 | 580 | 0.0431 | 0.0184 | 0.8228 |
| 0.2088 | 600 | 0.0306 | 0.0183 | 0.7963 |
| 0.2157 | 620 | 0.0134 | 0.0147 | 0.8216 |
| 0.2227 | 640 | 0.0155 | 0.0161 | 0.8166 |
| 0.2296 | 660 | 0.0201 | 0.0187 | 0.8170 |
| 0.2366 | 680 | 0.0301 | 0.0133 | 0.8272 |
| 0.2436 | 700 | 0.0164 | 0.0119 | 0.8274 |
| 0.2505 | 720 | 0.0254 | 0.0119 | 0.8223 |
| 0.2575 | 740 | 0.0129 | 0.0146 | 0.8165 |
| 0.2644 | 760 | 0.0208 | 0.0136 | 0.8162 |
| 0.2714 | 780 | 0.0157 | 0.0138 | 0.8120 |
| 0.2784 | 800 | 0.0169 | 0.0143 | 0.8248 |
| 0.2853 | 820 | 0.0158 | 0.0119 | 0.8166 |
| 0.2923 | 840 | 0.0227 | 0.0115 | 0.8153 |
| 0.2992 | 860 | 0.0196 | 0.0117 | 0.8163 |
| 0.3062 | 880 | 0.0137 | 0.0112 | 0.8225 |
| 0.3132 | 900 | 0.0299 | 0.0090 | 0.8155 |
| 0.3201 | 920 | 0.0073 | 0.0106 | 0.8157 |
| 0.3271 | 940 | 0.0248 | 0.0088 | 0.8174 |
| 0.3340 | 960 | 0.0179 | 0.0087 | 0.8215 |
| 0.3410 | 980 | 0.0171 | 0.0077 | 0.8285 |
| 0.3479 | 1000 | 0.0123 | 0.0096 | 0.8175 |
| 0.3549 | 1020 | 0.0081 | 0.0098 | 0.8152 |
| 0.3619 | 1040 | 0.0097 | 0.0094 | 0.8139 |
| 0.3688 | 1060 | 0.0379 | 0.0107 | 0.8236 |
| 0.3758 | 1080 | 0.0104 | 0.0078 | 0.8208 |
| 0.3827 | 1100 | 0.0067 | 0.0065 | 0.8189 |
| 0.3897 | 1120 | 0.0128 | 0.0080 | 0.8221 |
| 0.3967 | 1140 | 0.0049 | 0.0078 | 0.8181 |
| 0.4036 | 1160 | 0.0084 | 0.0092 | 0.8218 |
| 0.4106 | 1180 | 0.0173 | 0.0081 | 0.8248 |
| 0.4175 | 1200 | 0.0144 | 0.0080 | 0.8272 |
| 0.4245 | 1220 | 0.0025 | 0.0077 | 0.8260 |
| 0.4315 | 1240 | 0.0086 | 0.0072 | 0.8312 |
| 0.4384 | 1260 | 0.0114 | 0.0073 | 0.8242 |
| 0.4454 | 1280 | 0.0065 | 0.0067 | 0.8245 |
| 0.4523 | 1300 | 0.0132 | 0.0069 | 0.8248 |
| 0.4593 | 1320 | 0.003 | 0.0066 | 0.8233 |
| 0.4662 | 1340 | 0.0125 | 0.0066 | 0.8245 |
| 0.4732 | 1360 | 0.0016 | 0.0070 | 0.8281 |
| 0.4802 | 1380 | 0.0041 | 0.0066 | 0.8418 |
| 0.4871 | 1400 | 0.0117 | 0.0073 | 0.8361 |
| 0.4941 | 1420 | 0.0095 | 0.0073 | 0.8337 |
| 0.5010 | 1440 | 0.0184 | 0.0071 | 0.8282 |
| 0.5080 | 1460 | 0.0042 | 0.0069 | 0.8259 |
| 0.5150 | 1480 | 0.0077 | 0.0065 | 0.8235 |
| 0.5219 | 1500 | 0.0213 | 0.0059 | 0.8209 |
| 0.5289 | 1520 | 0.0037 | 0.0059 | 0.8277 |
| 0.5358 | 1540 | 0.0053 | 0.0053 | 0.8186 |
| 0.5428 | 1560 | 0.0045 | 0.0071 | 0.8238 |
| 0.5498 | 1580 | 0.0013 | 0.0101 | 0.8257 |
| 0.5567 | 1600 | 0.017 | 0.0051 | 0.8292 |
| 0.5637 | 1620 | 0.0053 | 0.0045 | 0.8234 |
| 0.5706 | 1640 | 0.0077 | 0.0044 | 0.8235 |
| 0.5776 | 1660 | 0.0135 | 0.0046 | 0.8200 |
| 0.5846 | 1680 | 0.0013 | 0.0045 | 0.8242 |
| 0.5915 | 1700 | 0.0067 | 0.0048 | 0.8266 |
| 0.5985 | 1720 | 0.0154 | 0.0049 | 0.8232 |
| 0.6054 | 1740 | 0.0037 | 0.0048 | 0.8222 |
| 0.6124 | 1760 | 0.0012 | 0.0049 | 0.8232 |
| 0.6193 | 1780 | 0.0112 | 0.0051 | 0.8212 |
| 0.6263 | 1800 | 0.0173 | 0.0056 | 0.8228 |
| 0.6333 | 1820 | 0.0044 | 0.0059 | 0.8177 |
| 0.6402 | 1840 | 0.0193 | 0.0059 | 0.8197 |
| 0.6472 | 1860 | 0.0028 | 0.0060 | 0.8203 |
| 0.6541 | 1880 | 0.005 | 0.0054 | 0.8278 |
| 0.6611 | 1900 | 0.0077 | 0.0049 | 0.8227 |
| 0.6681 | 1920 | 0.0126 | 0.0040 | 0.8267 |
| 0.6750 | 1940 | 0.008 | 0.0039 | 0.8258 |
| 0.6820 | 1960 | 0.0131 | 0.0039 | 0.8251 |
| 0.6889 | 1980 | 0.0114 | 0.0042 | 0.8310 |
| 0.6959 | 2000 | 0.0083 | 0.0041 | 0.8314 |
| 0.7029 | 2020 | 0.006 | 0.0037 | 0.8303 |
| 0.7098 | 2040 | 0.0048 | 0.0036 | 0.8269 |
| 0.7168 | 2060 | 0.0165 | 0.0040 | 0.8262 |
| 0.7237 | 2080 | 0.0093 | 0.0035 | 0.8158 |
| 0.7307 | 2100 | 0.007 | 0.0031 | 0.8167 |
| 0.7376 | 2120 | 0.0065 | 0.0030 | 0.8248 |
| 0.7446 | 2140 | 0.0042 | 0.0029 | 0.8274 |
| 0.7516 | 2160 | 0.0111 | 0.0026 | 0.8258 |
| 0.7585 | 2180 | 0.0066 | 0.0028 | 0.8249 |
| 0.7655 | 2200 | 0.0034 | 0.0034 | 0.8244 |
| 0.7724 | 2220 | 0.0013 | 0.0033 | 0.8238 |
| 0.7794 | 2240 | 0.0025 | 0.0034 | 0.8253 |
| 0.7864 | 2260 | 0.0065 | 0.0034 | 0.8240 |
| 0.7933 | 2280 | 0.0049 | 0.0035 | 0.8258 |
| 0.8003 | 2300 | 0.0007 | 0.0035 | 0.8277 |
| 0.8072 | 2320 | 0.004 | 0.0034 | 0.8298 |
| 0.8142 | 2340 | 0.0013 | 0.0033 | 0.8293 |
| 0.8212 | 2360 | 0.0122 | 0.0032 | 0.8300 |
| 0.8281 | 2380 | 0.0008 | 0.0033 | 0.8285 |
| 0.8351 | 2400 | 0.0019 | 0.0032 | 0.8266 |
| 0.8420 | 2420 | 0.0033 | 0.0032 | 0.8266 |
| 0.8490 | 2440 | 0.0078 | 0.0024 | 0.8284 |
| 0.8559 | 2460 | 0.0087 | 0.0022 | 0.8272 |
| 0.8629 | 2480 | 0.003 | 0.0021 | 0.8255 |
| 0.8699 | 2500 | 0.0039 | 0.0021 | 0.8232 |
| 0.8768 | 2520 | 0.0054 | 0.0021 | 0.8225 |
| **0.8838** | **2540** | **0.0015** | **0.0021** | **0.8236** |
| 0.8907 | 2560 | 0.0043 | 0.0021 | 0.8245 |
| 0.8977 | 2580 | 0.0083 | 0.0022 | 0.8237 |
| 0.9047 | 2600 | 0.0029 | 0.0024 | 0.8233 |
| 0.9116 | 2620 | 0.0095 | 0.0025 | 0.8257 |
| 0.9186 | 2640 | 0.0013 | 0.0025 | 0.8263 |
| 0.9255 | 2660 | 0.0025 | 0.0025 | 0.8268 |
| 0.9325 | 2680 | 0.006 | 0.0025 | 0.8264 |
| 0.9395 | 2700 | 0.0078 | 0.0026 | 0.8247 |
| 0.9464 | 2720 | 0.0061 | 0.0025 | 0.8248 |
| 0.9534 | 2740 | 0.001 | 0.0025 | 0.8238 |
| 0.9603 | 2760 | 0.0041 | 0.0025 | 0.8233 |
| 0.9673 | 2780 | 0.0157 | 0.0024 | 0.8249 |
| 0.9743 | 2800 | 0.0039 | 0.0024 | 0.8248 |
| 0.9812 | 2820 | 0.0047 | 0.0024 | 0.8242 |
| 0.9882 | 2840 | 0.0058 | 0.0024 | 0.8243 |
| 0.9951 | 2860 | 0.0018 | 0.0024 | 0.8242 |
* 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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