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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:212940
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Ringkasan data strategis BPS 2012
sentences:
- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
Jenis Pekerjaan Utama, 2021
- Laporan Perekonomian Indonesia 2007
- Statistik Potensi Desa Provinsi Banten 2008
- source_sentence: tahun berapa ekspor naik 2,37% dan impor naik 30,30%?
sentences:
- Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %
- Indeks Harga Konsumen per Kelompok di 82 Kota <sup>1</sup> (2012=100)
- 'Februari 2022: Tingkat Pengangguran Terbuka (TPT) sebesar 5,83 persen dan Rata-rata
upah buruh sebesar 2,89 juta rupiah per bulan'
- source_sentence: akses air bersih di indonesia (2005-2009)
sentences:
- Desember 2016, Rupiah Terapresiasi 0,74 Persen Terhadap Dolar Amerika
- Statistik Air Bersih 2005-2009
- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
yang Ditamatkan dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018
- source_sentence: Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2014-2018, Buku
2 Pulau Jawa dan Bali
sentences:
- Profil Migran Hasil Susenas 2011-2012
- Statistik Gas Kota 2004-2008
- Jumlah kunjungan wisman ke Indonesia melalui pintu masuk utama pada Juni 2022
mencapai 345,44 ribu kunjungan dan Jumlah penumpang angkutan udara internasional
pada Juni 2022 naik 23,28 persen
- source_sentence: perubahan nilai tukar petani bulan mei 2017
sentences:
- Perkembangan Nilai Tukar Petani Mei 2017
- NTP Naik 0,15%, Harga Gabah Kualitas GKG Naik 0,98%
- Statistik Restoran/Rumah Makan Tahun 2014
datasets:
- yahyaabd/allstats-semantic-search-synthetic-dataset-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstats semantic search v1 3 dev
type: allstats-semantic-search-v1-3-dev
metrics:
- type: pearson_cosine
value: 0.9958745183830993
name: Pearson Cosine
- type: spearman_cosine
value: 0.96406478662103
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat semantic search v1 3 test
type: allstat-semantic-search-v1-3-test
metrics:
- type: pearson_cosine
value: 0.9960950217535739
name: Pearson Cosine
- type: spearman_cosine
value: 0.9647914507837114
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) dataset. It maps sentences & paragraphs to a 768-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-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: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, '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-semantic-search-model-v1-3")
# Run inference
sentences = [
'perubahan nilai tukar petani bulan mei 2017',
'Perkembangan Nilai Tukar Petani Mei 2017',
'Statistik Restoran/Rumah Makan Tahun 2014',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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-search-v1-3-dev` and `allstat-semantic-search-v1-3-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | allstats-semantic-search-v1-3-dev | allstat-semantic-search-v1-3-test |
|:--------------------|:----------------------------------|:----------------------------------|
| pearson_cosine | 0.9959 | 0.9961 |
| **spearman_cosine** | **0.9641** | **0.9648** |
<!--
## 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
#### allstats-semantic-search-synthetic-dataset-v1
* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
* Size: 212,940 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 | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.46 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.47 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.05</li></ul> |
* Samples:
| query | doc | label |
|:---------------------------------------------------------------|:-----------------------------------------------------------------------|:------------------|
| <code>aDta industri besar dan sedang Indonesia 2008</code> | <code>Statistik Industri Besar dan Sedang Indonesia 2008</code> | <code>0.9</code> |
| <code>profil bisnis konstruksi individu jawa barat 2022</code> | <code>Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku</code> | <code>0.15</code> |
| <code>data statistik ekonomi indonesia</code> | <code>Nilai Tukar Valuta Asing di Indonesia 2014</code> | <code>0.08</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
#### allstats-semantic-search-synthetic-dataset-v1
* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
* Size: 26,618 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 | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.38 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.63 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------|
| <code>tahun berapa ekspor naik 2,37% dan impor naik 30,30%?</code> | <code>Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %</code> | <code>1.0</code> |
| <code>Berapa produksi padi pada tahun 2023?</code> | <code>Produksi padi tahun lainnya</code> | <code>0.0</code> |
| <code>data statistik solus per aqua 2015</code> | <code>Statistik Solus Per Aqua (SPA) 2015</code> | <code>0.97</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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 16
- `warmup_ratio`: 0.1
- `fp16`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 16
- `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`: 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`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine |
|:-------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-------------------------------------------------:|
| 0.1502 | 500 | 0.0579 | 0.0351 | 0.7132 | - |
| 0.3005 | 1000 | 0.03 | 0.0225 | 0.7589 | - |
| 0.4507 | 1500 | 0.0219 | 0.0185 | 0.7834 | - |
| 0.6010 | 2000 | 0.0181 | 0.0163 | 0.7946 | - |
| 0.7512 | 2500 | 0.0162 | 0.0147 | 0.7941 | - |
| 0.9014 | 3000 | 0.015 | 0.0147 | 0.8050 | - |
| 1.0517 | 3500 | 0.014 | 0.0131 | 0.7946 | - |
| 1.2019 | 4000 | 0.0119 | 0.0126 | 0.8038 | - |
| 1.3522 | 4500 | 0.0121 | 0.0128 | 0.8213 | - |
| 1.5024 | 5000 | 0.0117 | 0.0116 | 0.8268 | - |
| 1.6526 | 5500 | 0.0124 | 0.0117 | 0.8269 | - |
| 1.8029 | 6000 | 0.0111 | 0.0109 | 0.8421 | - |
| 1.9531 | 6500 | 0.0105 | 0.0108 | 0.8278 | - |
| 2.1034 | 7000 | 0.0091 | 0.0093 | 0.8460 | - |
| 2.2536 | 7500 | 0.0085 | 0.0091 | 0.8469 | - |
| 2.4038 | 8000 | 0.0079 | 0.0083 | 0.8595 | - |
| 2.5541 | 8500 | 0.0075 | 0.0085 | 0.8495 | - |
| 2.7043 | 9000 | 0.0073 | 0.0082 | 0.8614 | - |
| 2.8546 | 9500 | 0.0068 | 0.0077 | 0.8696 | - |
| 3.0048 | 10000 | 0.0066 | 0.0076 | 0.8669 | - |
| 3.1550 | 10500 | 0.0058 | 0.0072 | 0.8678 | - |
| 3.3053 | 11000 | 0.0056 | 0.0067 | 0.8703 | - |
| 3.4555 | 11500 | 0.0054 | 0.0067 | 0.8766 | - |
| 3.6058 | 12000 | 0.0054 | 0.0063 | 0.8678 | - |
| 3.7560 | 12500 | 0.0051 | 0.0061 | 0.8786 | - |
| 3.9062 | 13000 | 0.0052 | 0.0077 | 0.8699 | - |
| 4.0565 | 13500 | 0.005 | 0.0055 | 0.8859 | - |
| 4.2067 | 14000 | 0.0041 | 0.0054 | 0.8900 | - |
| 4.3570 | 14500 | 0.0038 | 0.0052 | 0.8892 | - |
| 4.5072 | 15000 | 0.0039 | 0.0050 | 0.8895 | - |
| 4.6575 | 15500 | 0.004 | 0.0052 | 0.8972 | - |
| 4.8077 | 16000 | 0.0042 | 0.0051 | 0.8927 | - |
| 4.9579 | 16500 | 0.0041 | 0.0052 | 0.8930 | - |
| 5.1082 | 17000 | 0.0034 | 0.0053 | 0.8998 | - |
| 5.2584 | 17500 | 0.003 | 0.0047 | 0.9023 | - |
| 5.4087 | 18000 | 0.0032 | 0.0045 | 0.9039 | - |
| 5.5589 | 18500 | 0.0032 | 0.0044 | 0.8996 | - |
| 5.7091 | 19000 | 0.0032 | 0.0041 | 0.9085 | - |
| 5.8594 | 19500 | 0.0032 | 0.0047 | 0.9072 | - |
| 6.0096 | 20000 | 0.0029 | 0.0037 | 0.9104 | - |
| 6.1599 | 20500 | 0.0024 | 0.0037 | 0.9112 | - |
| 6.3101 | 21000 | 0.0026 | 0.0039 | 0.9112 | - |
| 6.4603 | 21500 | 0.0024 | 0.0037 | 0.9157 | - |
| 6.6106 | 22000 | 0.0022 | 0.0038 | 0.9122 | - |
| 6.7608 | 22500 | 0.0025 | 0.0034 | 0.9170 | - |
| 6.9111 | 23000 | 0.0023 | 0.0034 | 0.9179 | - |
| 7.0613 | 23500 | 0.002 | 0.0031 | 0.9244 | - |
| 7.2115 | 24000 | 0.0019 | 0.0030 | 0.9250 | - |
| 7.3618 | 24500 | 0.0018 | 0.0032 | 0.9249 | - |
| 7.5120 | 25000 | 0.0022 | 0.0031 | 0.9162 | - |
| 7.6623 | 25500 | 0.0019 | 0.0030 | 0.9266 | - |
| 7.8125 | 26000 | 0.0019 | 0.0028 | 0.9297 | - |
| 7.9627 | 26500 | 0.0018 | 0.0028 | 0.9282 | - |
| 8.1130 | 27000 | 0.0015 | 0.0025 | 0.9324 | - |
| 8.2632 | 27500 | 0.0014 | 0.0027 | 0.9337 | - |
| 8.4135 | 28000 | 0.0015 | 0.0027 | 0.9327 | - |
| 8.5637 | 28500 | 0.0016 | 0.0027 | 0.9313 | - |
| 8.7139 | 29000 | 0.0016 | 0.0027 | 0.9333 | - |
| 8.8642 | 29500 | 0.0015 | 0.0025 | 0.9382 | - |
| 9.0144 | 30000 | 0.0014 | 0.0025 | 0.9375 | - |
| 9.1647 | 30500 | 0.0011 | 0.0024 | 0.9398 | - |
| 9.3149 | 31000 | 0.0012 | 0.0025 | 0.9384 | - |
| 9.4651 | 31500 | 0.0014 | 0.0025 | 0.9383 | - |
| 9.6154 | 32000 | 0.0013 | 0.0023 | 0.9410 | - |
| 9.7656 | 32500 | 0.0011 | 0.0023 | 0.9409 | - |
| 9.9159 | 33000 | 0.0012 | 0.0021 | 0.9432 | - |
| 10.0661 | 33500 | 0.0011 | 0.0021 | 0.9432 | - |
| 10.2163 | 34000 | 0.001 | 0.0021 | 0.9442 | - |
| 10.3666 | 34500 | 0.0009 | 0.0022 | 0.9436 | - |
| 10.5168 | 35000 | 0.001 | 0.0021 | 0.9468 | - |
| 10.6671 | 35500 | 0.001 | 0.0020 | 0.9471 | - |
| 10.8173 | 36000 | 0.001 | 0.0021 | 0.9467 | - |
| 10.9675 | 36500 | 0.0011 | 0.0021 | 0.9478 | - |
| 11.1178 | 37000 | 0.0008 | 0.0020 | 0.9493 | - |
| 11.2680 | 37500 | 0.0008 | 0.0019 | 0.9509 | - |
| 11.4183 | 38000 | 0.0008 | 0.0019 | 0.9504 | - |
| 11.5685 | 38500 | 0.0008 | 0.0019 | 0.9512 | - |
| 11.7188 | 39000 | 0.0008 | 0.0019 | 0.9516 | - |
| 11.8690 | 39500 | 0.0007 | 0.0019 | 0.9534 | - |
| 12.0192 | 40000 | 0.0007 | 0.0018 | 0.9539 | - |
| 12.1695 | 40500 | 0.0006 | 0.0018 | 0.9555 | - |
| 12.3197 | 41000 | 0.0006 | 0.0019 | 0.9551 | - |
| 12.4700 | 41500 | 0.0007 | 0.0019 | 0.9550 | - |
| 12.6202 | 42000 | 0.0008 | 0.0018 | 0.9552 | - |
| 12.7704 | 42500 | 0.0006 | 0.0017 | 0.9559 | - |
| 12.9207 | 43000 | 0.0006 | 0.0017 | 0.9568 | - |
| 13.0709 | 43500 | 0.0006 | 0.0017 | 0.9577 | - |
| 13.2212 | 44000 | 0.0005 | 0.0017 | 0.9581 | - |
| 13.3714 | 44500 | 0.0006 | 0.0017 | 0.9586 | - |
| 13.5216 | 45000 | 0.0005 | 0.0017 | 0.9587 | - |
| 13.6719 | 45500 | 0.0005 | 0.0017 | 0.9591 | - |
| 13.8221 | 46000 | 0.0006 | 0.0016 | 0.9600 | - |
| 13.9724 | 46500 | 0.0005 | 0.0016 | 0.9603 | - |
| 14.1226 | 47000 | 0.0005 | 0.0016 | 0.9609 | - |
| 14.2728 | 47500 | 0.0005 | 0.0016 | 0.9612 | - |
| 14.4231 | 48000 | 0.0005 | 0.0016 | 0.9611 | - |
| 14.5733 | 48500 | 0.0005 | 0.0016 | 0.9616 | - |
| 14.7236 | 49000 | 0.0004 | 0.0015 | 0.9625 | - |
| 14.8738 | 49500 | 0.0004 | 0.0016 | 0.9628 | - |
| 15.0240 | 50000 | 0.0004 | 0.0016 | 0.9631 | - |
| 15.1743 | 50500 | 0.0004 | 0.0016 | 0.9632 | - |
| 15.3245 | 51000 | 0.0004 | 0.0016 | 0.9633 | - |
| 15.4748 | 51500 | 0.0004 | 0.0016 | 0.9635 | - |
| 15.625 | 52000 | 0.0004 | 0.0015 | 0.9638 | - |
| 15.7752 | 52500 | 0.0004 | 0.0015 | 0.9640 | - |
| 15.9255 | 53000 | 0.0004 | 0.0015 | 0.9641 | - |
| 16.0 | 53248 | - | - | - | 0.9648 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.2.2+cu121
- Accelerate: 1.2.1
- 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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