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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1432
- loss:MultipleNegativesRankingLoss
base_model: denaya/indoSBERT-large
widget:
- source_sentence: 'Input-output domestik Indonesia: 17 sektor usaha, harga produsen,
data tahun 2016 (juta Rp)'
sentences:
- 'Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023 '
- 'IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 1996-2014
(1996=100) '
- 'Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17
Lapangan Usaha), 2016 (Juta Rupiah) '
- source_sentence: 'Gaji bulanan: beda umur, beda jenis pekerjaan (9 sektor), 2017'
sentences:
- 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017 '
- 'Ekspor Rumput Laut dan Ganggang Lainnya menurut Negara Tujuan Utama, 2012-2023 '
- 'Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017 '
- source_sentence: Ringkasan aliran dana kuartal terakhir 2009 dalam Rupiah
sentences:
- 'Jumlah Perahu/Kapal, Luas Usaha Budidaya dan Produksi menurut Sub Sektor Perikanan,
2002-2016 '
- 'Jumlah Pendapatan Menurut Golongan Rumah Tangga (miliar rupiah) 2000, 2005, dan
2008 '
- 'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah) '
- source_sentence: Berapa total transaksi (harga pembeli) untuk 9 sektor ekonomi di
Indonesia tahun 2005? (miliar rupiah)
sentences:
- 'Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016 '
- 'Transaksi Total Atas Dasar Harga Pembeli 9 Sektor Ekonomi (miliar rupiah), 2005 '
- 'Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau
Jawa dan Sumatera dengan Nasional (2018=100) '
- source_sentence: Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk
usia 15+ pada tahun 2022?
sentences:
- 'Persentase Perkembangan Distribusi Pengeluaran '
- 'Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan
Utama (ribu rupiah), 2018 '
- 'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan
dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 '
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
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on denaya/indoSBERT-large
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy@1
value: 0.9120521172638436
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.990228013029316
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.993485342019544
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.996742671009772
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9120521172638436
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3572204125950054
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23778501628664495
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13745928338762217
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7097252402956855
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7867346590488319
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8052359035035943
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8221312325947948
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8348212945928647
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9497052892818366
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7729410950742827
name: Cosine Map@100
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora duplicates dev
type: quora_duplicates_dev
metrics:
- type: cosine_accuracy
value: 0.9914529914529915
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.31953397393226624
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9850953206239168
name: Cosine F1
- type: cosine_f1_threshold
value: 0.30364981293678284
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.988865692414753
name: Cosine Precision
- type: cosine_recall
value: 0.981353591160221
name: Cosine Recall
- type: cosine_ap
value: 0.9956970583311449
name: Cosine Ap
- type: cosine_mcc
value: 0.9791180702139771
name: Cosine Mcc
---
# SentenceTransformer based on denaya/indoSBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large). It maps sentences & paragraphs to a 256-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:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 256 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 1024, '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})
(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## 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-large-bpstable-v1")
# Run inference
sentences = [
'Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk usia 15+ pada tahun 2022?',
'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ',
'Persentase Perkembangan Distribusi Pengeluaran ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# 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: `eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9121 |
| cosine_accuracy@3 | 0.9902 |
| cosine_accuracy@5 | 0.9935 |
| cosine_accuracy@10 | 0.9967 |
| cosine_precision@1 | 0.9121 |
| cosine_precision@3 | 0.3572 |
| cosine_precision@5 | 0.2378 |
| cosine_precision@10 | 0.1375 |
| cosine_recall@1 | 0.7097 |
| cosine_recall@3 | 0.7867 |
| cosine_recall@5 | 0.8052 |
| cosine_recall@10 | 0.8221 |
| **cosine_ndcg@10** | **0.8348** |
| cosine_mrr@10 | 0.9497 |
| cosine_map@100 | 0.7729 |
#### Binary Classification
* Dataset: `quora_duplicates_dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.9915 |
| cosine_accuracy_threshold | 0.3195 |
| cosine_f1 | 0.9851 |
| cosine_f1_threshold | 0.3036 |
| cosine_precision | 0.9889 |
| cosine_recall | 0.9814 |
| **cosine_ap** | **0.9957** |
| cosine_mcc | 0.9791 |
<!--
## 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
#### Unnamed Dataset
* Size: 1,432 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 16.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.88 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Average monthly net wage/salary of employees by age group and type of work (Rupiah), 2018</code> | <code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur dan Jenis Pekerjaan (Rupiah), 2018 </code> | <code>1</code> |
| <code>Cek average real wage buruh industri pengolahan (level bawah) sekitar tahun 2009</code> | <code>Rata-rata Upah Riil Per Bulan Buruh Industri Pengolahan di Bawah Mandor, 2005-2014 (1996=100) </code> | <code>1</code> |
| <code>Dimana saya bisa lihat rekapitulasi dokumen RPB kabupaten/kota?</code> | <code>Rekap Dokumen RPB Kabupaten/Kota </code> | <code>1</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`: 30
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### 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
- `num_train_epochs`: 30
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | eval_cosine_ndcg@10 | quora_duplicates_dev_cosine_ap |
|:-------:|:----:|:-------------:|:-------------------:|:------------------------------:|
| 0.2222 | 20 | - | 0.7769 | - |
| 0.4444 | 40 | - | 0.8167 | - |
| 0.6667 | 60 | - | 0.8221 | - |
| 0.8889 | 80 | - | 0.8282 | - |
| 1.0 | 90 | - | 0.8256 | - |
| 1.1111 | 100 | - | 0.8278 | - |
| 1.3333 | 120 | - | 0.8388 | - |
| 1.5556 | 140 | - | 0.8347 | - |
| 1.7778 | 160 | - | 0.8351 | - |
| 2.0 | 180 | - | 0.8407 | - |
| 2.2222 | 200 | - | 0.8302 | - |
| 2.4444 | 220 | - | 0.8261 | - |
| 2.6667 | 240 | - | 0.8217 | - |
| 2.8889 | 260 | - | 0.8161 | - |
| 3.0 | 270 | - | 0.8143 | - |
| 3.1111 | 280 | - | 0.8133 | - |
| 3.3333 | 300 | - | 0.8259 | - |
| 3.5556 | 320 | - | 0.8342 | - |
| 3.7778 | 340 | - | 0.8267 | - |
| 4.0 | 360 | - | 0.8190 | - |
| 4.2222 | 380 | - | 0.8193 | - |
| 4.4444 | 400 | - | 0.8281 | - |
| 4.6667 | 420 | - | 0.8283 | - |
| 4.8889 | 440 | - | 0.8197 | - |
| 5.0 | 450 | - | 0.8211 | - |
| 5.1111 | 460 | - | 0.8118 | - |
| 5.3333 | 480 | - | 0.8298 | - |
| 5.5556 | 500 | 0.0412 | 0.8283 | - |
| 5.7778 | 520 | - | 0.8264 | - |
| 6.0 | 540 | - | 0.8271 | - |
| 6.2222 | 560 | - | 0.8243 | - |
| 6.4444 | 580 | - | 0.8256 | - |
| 6.6667 | 600 | - | 0.8356 | - |
| 6.8889 | 620 | - | 0.8332 | - |
| 7.0 | 630 | - | 0.8250 | - |
| 7.1111 | 640 | - | 0.8179 | - |
| 7.3333 | 660 | - | 0.8356 | - |
| 7.5556 | 680 | - | 0.8400 | - |
| 7.7778 | 700 | - | 0.8349 | - |
| 8.0 | 720 | - | 0.8281 | - |
| 8.2222 | 740 | - | 0.8330 | - |
| 8.4444 | 760 | - | 0.8338 | - |
| 8.6667 | 780 | - | 0.8338 | - |
| 8.8889 | 800 | - | 0.8344 | - |
| 9.0 | 810 | - | 0.8319 | - |
| 9.1111 | 820 | - | 0.8328 | - |
| 9.3333 | 840 | - | 0.8325 | - |
| 9.5556 | 860 | - | 0.8375 | - |
| 9.7778 | 880 | - | 0.8306 | - |
| 10.0 | 900 | - | 0.8263 | - |
| 10.2222 | 920 | - | 0.8280 | - |
| 10.4444 | 940 | - | 0.8272 | - |
| 10.6667 | 960 | - | 0.8280 | - |
| 10.8889 | 980 | - | 0.8313 | - |
| 11.0 | 990 | - | 0.8307 | - |
| 11.1111 | 1000 | 0.0198 | 0.8324 | - |
| 11.3333 | 1020 | - | 0.8303 | - |
| 11.5556 | 1040 | - | 0.8262 | - |
| 11.7778 | 1060 | - | 0.8294 | - |
| 12.0 | 1080 | - | 0.8309 | - |
| 12.2222 | 1100 | - | 0.8274 | - |
| 12.4444 | 1120 | - | 0.8312 | - |
| 12.6667 | 1140 | - | 0.8371 | - |
| 12.8889 | 1160 | - | 0.8408 | - |
| 13.0 | 1170 | - | 0.8374 | - |
| 13.1111 | 1180 | - | 0.8344 | - |
| 13.3333 | 1200 | - | 0.8341 | - |
| 13.5556 | 1220 | - | 0.8333 | - |
| 13.7778 | 1240 | - | 0.8388 | - |
| 14.0 | 1260 | - | 0.8414 | - |
| 14.2222 | 1280 | - | 0.8344 | - |
| 14.4444 | 1300 | - | 0.8328 | - |
| 14.6667 | 1320 | - | 0.8340 | - |
| 14.8889 | 1340 | - | 0.8317 | - |
| 15.0 | 1350 | - | 0.8260 | - |
| 15.1111 | 1360 | - | 0.8252 | - |
| 15.3333 | 1380 | - | 0.8244 | - |
| 15.5556 | 1400 | - | 0.8269 | - |
| 15.7778 | 1420 | - | 0.8275 | - |
| 16.0 | 1440 | - | 0.8281 | - |
| 16.2222 | 1460 | - | 0.8294 | - |
| 16.4444 | 1480 | - | 0.8299 | - |
| 16.6667 | 1500 | 0.0136 | 0.8318 | - |
| 16.8889 | 1520 | - | 0.8320 | - |
| 17.0 | 1530 | - | 0.8332 | - |
| 17.1111 | 1540 | - | 0.8337 | - |
| 17.3333 | 1560 | - | 0.8299 | - |
| 17.5556 | 1580 | - | 0.8283 | - |
| 17.7778 | 1600 | - | 0.8309 | - |
| 18.0 | 1620 | - | 0.8329 | - |
| 18.2222 | 1640 | - | 0.8317 | - |
| 18.4444 | 1660 | - | 0.8313 | - |
| 18.6667 | 1680 | - | 0.8317 | - |
| 18.8889 | 1700 | - | 0.8356 | - |
| 19.0 | 1710 | - | 0.8345 | - |
| 19.1111 | 1720 | - | 0.8358 | - |
| 19.3333 | 1740 | - | 0.8334 | - |
| 19.5556 | 1760 | - | 0.8335 | - |
| 19.7778 | 1780 | - | 0.8318 | - |
| 20.0 | 1800 | - | 0.8326 | - |
| 20.2222 | 1820 | - | 0.8318 | - |
| 20.4444 | 1840 | - | 0.8335 | - |
| 20.6667 | 1860 | - | 0.8333 | - |
| 20.8889 | 1880 | - | 0.8335 | - |
| 21.0 | 1890 | - | 0.8341 | - |
| 21.1111 | 1900 | - | 0.8341 | - |
| 21.3333 | 1920 | - | 0.8355 | - |
| 21.5556 | 1940 | - | 0.8360 | - |
| 21.7778 | 1960 | - | 0.8343 | - |
| 22.0 | 1980 | - | 0.8351 | - |
| 22.2222 | 2000 | 0.015 | 0.8342 | - |
| 22.4444 | 2020 | - | 0.8342 | - |
| 22.6667 | 2040 | - | 0.8339 | - |
| 22.8889 | 2060 | - | 0.8342 | - |
| 23.0 | 2070 | - | 0.8345 | - |
| 23.1111 | 2080 | - | 0.8354 | - |
| 23.3333 | 2100 | - | 0.8366 | - |
| 23.5556 | 2120 | - | 0.8379 | - |
| 23.7778 | 2140 | - | 0.8386 | - |
| 24.0 | 2160 | - | 0.8367 | - |
| 24.2222 | 2180 | - | 0.8357 | - |
| 24.4444 | 2200 | - | 0.8372 | - |
| 24.6667 | 2220 | - | 0.8377 | - |
| 24.8889 | 2240 | - | 0.8373 | - |
| 25.0 | 2250 | - | 0.8367 | - |
| 25.1111 | 2260 | - | 0.8366 | - |
| 25.3333 | 2280 | - | 0.8369 | - |
| 25.5556 | 2300 | - | 0.8373 | - |
| 25.7778 | 2320 | - | 0.8366 | - |
| 26.0 | 2340 | - | 0.8354 | - |
| 26.2222 | 2360 | - | 0.8347 | - |
| 26.4444 | 2380 | - | 0.8344 | - |
| 26.6667 | 2400 | - | 0.8341 | - |
| 26.8889 | 2420 | - | 0.8343 | - |
| 27.0 | 2430 | - | 0.8344 | - |
| 27.1111 | 2440 | - | 0.8345 | - |
| 27.3333 | 2460 | - | 0.8344 | - |
| 27.5556 | 2480 | - | 0.8347 | - |
| 27.7778 | 2500 | 0.0136 | 0.8342 | - |
| 28.0 | 2520 | - | 0.8347 | - |
| 28.2222 | 2540 | - | 0.8346 | - |
| 28.4444 | 2560 | - | 0.8346 | - |
| 28.6667 | 2580 | - | 0.8347 | - |
| 28.8889 | 2600 | - | 0.8348 | - |
| 29.0 | 2610 | - | 0.8348 | - |
| 29.1111 | 2620 | - | 0.8348 | - |
| 29.3333 | 2640 | - | 0.8348 | - |
| 29.5556 | 2660 | - | 0.8348 | - |
| 29.7778 | 2680 | - | 0.8348 | - |
| 30.0 | 2700 | - | 0.8348 | - |
| -1 | -1 | - | - | 0.9957 |
</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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