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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:212917
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: statistik neraca arus dana indonesia
sentences:
- Statistik Kelapa Sawit Indonesia 2012
- Distribusi Perdagangan Komoditas Kedelai Indonesia 2023
- Data Runtun Statistik Konstruksi 1990-2010
- source_sentence: Seberapa besar kenaikan produksi IBS pada Triwulan IV Tahun 2013
dibandingkan Triwulan IV Tahun Sebelumnya?
sentences:
- Pertumbuhan PDB 2013 Mencapai 5,78 Persen
- Statistik Komuter Gerbangkertosusila Hasil Survei Komuter Gerbangkertosusila 2017
- Statistik Penduduk Lanjut Usia Provinsi Jawa Timur 2010-Hasil Sensus Penduduk
2010
- source_sentence: 'Penduduk Papua: migrasi 2015'
sentences:
- Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi
dan jenis pekerjaan utama, 2019
- Statistik Pemotongan Ternak 2010 dan 2011
- Statistik Harga Produsen Pertanian Sub Sektor Tanaman Pangan, Hortikultura dan
Perkebunan Rakyat 2010
- source_sentence: statistik konstruksi 2022
sentences:
- Studi Modal Sosial 2006
- BRS upah buruh agustus 2018
- Statistik Perdagangan Luar Negeri Indonesia Ekspor 2006 vol 1
- source_sentence: Statistik ekspor Indonesia Maret 2202
sentences:
- Produk Domestik Bruto Indonesia Triwulanan 2006-2010
- Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan
IPAK 2022
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023
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 dev
type: allstats-semantic-search-v1-dev
metrics:
- type: pearson_cosine
value: 0.9894566758405579
name: Pearson Cosine
- type: spearman_cosine
value: 0.9072484378842124
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat semantic search v1 test
type: allstat-semantic-search-v1-test
metrics:
- type: pearson_cosine
value: 0.9895284407960067
name: Pearson Cosine
- type: spearman_cosine
value: 0.9074137706349162
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")
# Run inference
sentences = [
'Statistik ekspor Indonesia Maret 2202',
'Produk Domestik Bruto Indonesia Triwulanan 2006-2010',
'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023',
]
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-dev` and `allstat-semantic-search-v1-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-dev | allstat-semantic-search-v1-test |
|:--------------------|:--------------------------------|:--------------------------------|
| pearson_cosine | 0.9895 | 0.9895 |
| **spearman_cosine** | **0.9072** | **0.9074** |
<!--
## 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 [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de)
* Size: 212,917 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: 11.48 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.89 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:------------------|
| <code>ringkasan aktivitas badan pusat statistik tahun 2018</code> | <code>Statistik Harga Produsen sektor pertanian di indonesia 2008</code> | <code>0.1</code> |
| <code>indikator kesejahteraan petani rejang lebong 2015</code> | <code>Diagram Timbang Nilai Tukar Petani Kabupaten Rejang Lebong 2015</code> | <code>0.82</code> |
| <code>Berapa persen kenaikan kunjungan wisatawan mancanegara pada April 2024?</code> | <code>Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan IPAK 2022</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### 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 [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de)
* Size: 26,614 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: 11.21 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.41 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:------------------|
| <code>Laporan bulanan ekonomi Indonesia bulan November 201</code> | <code>Laporan Bulanan Data Sosial Ekonomi November 2021</code> | <code>0.92</code> |
| <code>pekerjaan layak di indonesia tahun 2022: data dan analisis</code> | <code>Statistik Penduduk Lanjut Usia Provinsi Papua Barat 2010-Hasil Sensus Penduduk 2010</code> | <code>0.09</code> |
| <code>Tabel pendapatan rata-rata pekerja lepas berdasarkan provinsi dan pendidikan tahun 2021</code> | <code>Nilai Impor Kendaraan Bermotor Menurut Negara Asal Utama (Nilai CIF:juta US$), 2018-2023</code> | <code>0.1</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 4
- `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`: 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`: 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-dev_spearman_cosine | allstat-semantic-search-v1-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------:|:-----------------------------------------------:|
| 0.0376 | 250 | 0.0683 | 0.0432 | 0.6955 | - |
| 0.0751 | 500 | 0.0393 | 0.0322 | 0.7230 | - |
| 0.1127 | 750 | 0.0321 | 0.0270 | 0.7476 | - |
| 0.1503 | 1000 | 0.0255 | 0.0226 | 0.7789 | - |
| 0.1879 | 1250 | 0.024 | 0.0213 | 0.7683 | - |
| 0.2254 | 1500 | 0.022 | 0.0199 | 0.7727 | - |
| 0.2630 | 1750 | 0.0219 | 0.0195 | 0.7853 | - |
| 0.3006 | 2000 | 0.0202 | 0.0188 | 0.7795 | - |
| 0.3381 | 2250 | 0.0191 | 0.0187 | 0.7943 | - |
| 0.3757 | 2500 | 0.0198 | 0.0178 | 0.7842 | - |
| 0.4133 | 2750 | 0.0179 | 0.0184 | 0.7974 | - |
| 0.4509 | 3000 | 0.0179 | 0.0194 | 0.7810 | - |
| 0.4884 | 3250 | 0.0182 | 0.0168 | 0.8080 | - |
| 0.5260 | 3500 | 0.0174 | 0.0164 | 0.8131 | - |
| 0.5636 | 3750 | 0.0174 | 0.0154 | 0.8113 | - |
| 0.6011 | 4000 | 0.0169 | 0.0157 | 0.7981 | - |
| 0.6387 | 4250 | 0.0152 | 0.0146 | 0.8099 | - |
| 0.6763 | 4500 | 0.0148 | 0.0147 | 0.8091 | - |
| 0.7139 | 4750 | 0.0145 | 0.0145 | 0.8178 | - |
| 0.7514 | 5000 | 0.014 | 0.0139 | 0.8184 | - |
| 0.7890 | 5250 | 0.0145 | 0.0130 | 0.8166 | - |
| 0.8266 | 5500 | 0.0134 | 0.0129 | 0.8306 | - |
| 0.8641 | 5750 | 0.013 | 0.0122 | 0.8251 | - |
| 0.9017 | 6000 | 0.0136 | 0.0130 | 0.8265 | - |
| 0.9393 | 6250 | 0.0123 | 0.0126 | 0.8224 | - |
| 0.9769 | 6500 | 0.0113 | 0.0120 | 0.8305 | - |
| 1.0144 | 6750 | 0.0129 | 0.0117 | 0.8204 | - |
| 1.0520 | 7000 | 0.0106 | 0.0116 | 0.8284 | - |
| 1.0896 | 7250 | 0.01 | 0.0116 | 0.8303 | - |
| 1.1271 | 7500 | 0.0096 | 0.0110 | 0.8303 | - |
| 1.1647 | 7750 | 0.01 | 0.0113 | 0.8305 | - |
| 1.2023 | 8000 | 0.0116 | 0.0108 | 0.8300 | - |
| 1.2399 | 8250 | 0.0095 | 0.0104 | 0.8432 | - |
| 1.2774 | 8500 | 0.009 | 0.0104 | 0.8370 | - |
| 1.3150 | 8750 | 0.0101 | 0.0102 | 0.8434 | - |
| 1.3526 | 9000 | 0.01 | 0.0097 | 0.8450 | - |
| 1.3901 | 9250 | 0.0097 | 0.0103 | 0.8286 | - |
| 1.4277 | 9500 | 0.0092 | 0.0096 | 0.8393 | - |
| 1.4653 | 9750 | 0.0093 | 0.0089 | 0.8480 | - |
| 1.5029 | 10000 | 0.0088 | 0.0090 | 0.8439 | - |
| 1.5404 | 10250 | 0.0087 | 0.0089 | 0.8569 | - |
| 1.5780 | 10500 | 0.0082 | 0.0088 | 0.8488 | - |
| 1.6156 | 10750 | 0.009 | 0.0089 | 0.8493 | - |
| 1.6531 | 11000 | 0.0086 | 0.0086 | 0.8499 | - |
| 1.6907 | 11250 | 0.0076 | 0.0083 | 0.8600 | - |
| 1.7283 | 11500 | 0.0076 | 0.0081 | 0.8621 | - |
| 1.7659 | 11750 | 0.0079 | 0.0081 | 0.8611 | - |
| 1.8034 | 12000 | 0.0082 | 0.0085 | 0.8540 | - |
| 1.8410 | 12250 | 0.0074 | 0.0081 | 0.8620 | - |
| 1.8786 | 12500 | 0.007 | 0.0080 | 0.8639 | - |
| 1.9161 | 12750 | 0.0071 | 0.0083 | 0.8450 | - |
| 1.9537 | 13000 | 0.007 | 0.0076 | 0.8585 | - |
| 1.9913 | 13250 | 0.0072 | 0.0074 | 0.8640 | - |
| 2.0289 | 13500 | 0.0055 | 0.0069 | 0.8699 | - |
| 2.0664 | 13750 | 0.0056 | 0.0068 | 0.8673 | - |
| 2.1040 | 14000 | 0.0052 | 0.0066 | 0.8723 | - |
| 2.1416 | 14250 | 0.0059 | 0.0069 | 0.8644 | - |
| 2.1791 | 14500 | 0.0055 | 0.0068 | 0.8670 | - |
| 2.2167 | 14750 | 0.005 | 0.0065 | 0.8723 | - |
| 2.2543 | 15000 | 0.0053 | 0.0066 | 0.8766 | - |
| 2.2919 | 15250 | 0.0057 | 0.0065 | 0.8782 | - |
| 2.3294 | 15500 | 0.0053 | 0.0064 | 0.8749 | - |
| 2.3670 | 15750 | 0.0056 | 0.0070 | 0.8708 | - |
| 2.4046 | 16000 | 0.0058 | 0.0065 | 0.8731 | - |
| 2.4421 | 16250 | 0.0047 | 0.0064 | 0.8793 | - |
| 2.4797 | 16500 | 0.0049 | 0.0063 | 0.8801 | - |
| 2.5173 | 16750 | 0.0051 | 0.0063 | 0.8782 | - |
| 2.5549 | 17000 | 0.0053 | 0.0060 | 0.8799 | - |
| 2.5924 | 17250 | 0.0051 | 0.0059 | 0.8825 | - |
| 2.6300 | 17500 | 0.0048 | 0.0060 | 0.8761 | - |
| 2.6676 | 17750 | 0.0055 | 0.0055 | 0.8773 | - |
| 2.7051 | 18000 | 0.0045 | 0.0053 | 0.8833 | - |
| 2.7427 | 18250 | 0.0041 | 0.0053 | 0.8868 | - |
| 2.7803 | 18500 | 0.0051 | 0.0054 | 0.8811 | - |
| 2.8179 | 18750 | 0.004 | 0.0052 | 0.8881 | - |
| 2.8554 | 19000 | 0.0043 | 0.0053 | 0.8764 | - |
| 2.8930 | 19250 | 0.0047 | 0.0051 | 0.8874 | - |
| 2.9306 | 19500 | 0.0038 | 0.0051 | 0.8922 | - |
| 2.9681 | 19750 | 0.0047 | 0.0050 | 0.8821 | - |
| 3.0057 | 20000 | 0.0037 | 0.0048 | 0.8911 | - |
| 3.0433 | 20250 | 0.0031 | 0.0048 | 0.8911 | - |
| 3.0809 | 20500 | 0.0032 | 0.0046 | 0.8934 | - |
| 3.1184 | 20750 | 0.0034 | 0.0046 | 0.8942 | - |
| 3.1560 | 21000 | 0.0028 | 0.0045 | 0.8976 | - |
| 3.1936 | 21250 | 0.0034 | 0.0045 | 0.8932 | - |
| 3.2311 | 21500 | 0.003 | 0.0044 | 0.8959 | - |
| 3.2687 | 21750 | 0.0033 | 0.0044 | 0.8961 | - |
| 3.3063 | 22000 | 0.0029 | 0.0043 | 0.8995 | - |
| 3.3439 | 22250 | 0.0029 | 0.0044 | 0.8978 | - |
| 3.3814 | 22500 | 0.0027 | 0.0043 | 0.8998 | - |
| 3.4190 | 22750 | 0.003 | 0.0043 | 0.9019 | - |
| 3.4566 | 23000 | 0.0027 | 0.0042 | 0.8982 | - |
| 3.4941 | 23250 | 0.0027 | 0.0042 | 0.9014 | - |
| 3.5317 | 23500 | 0.0034 | 0.0042 | 0.9025 | - |
| 3.5693 | 23750 | 0.003 | 0.0041 | 0.9027 | - |
| 3.6069 | 24000 | 0.0029 | 0.0041 | 0.9003 | - |
| 3.6444 | 24250 | 0.0027 | 0.0040 | 0.9023 | - |
| 3.6820 | 24500 | 0.0027 | 0.0040 | 0.9035 | - |
| 3.7196 | 24750 | 0.0033 | 0.0040 | 0.9042 | - |
| 3.7571 | 25000 | 0.0028 | 0.0039 | 0.9053 | - |
| 3.7947 | 25250 | 0.0027 | 0.0039 | 0.9049 | - |
| 3.8323 | 25500 | 0.0033 | 0.0039 | 0.9057 | - |
| 3.8699 | 25750 | 0.0025 | 0.0039 | 0.9075 | - |
| 3.9074 | 26000 | 0.003 | 0.0039 | 0.9068 | - |
| 3.9450 | 26250 | 0.0026 | 0.0039 | 0.9073 | - |
| 3.9826 | 26500 | 0.0023 | 0.0038 | 0.9072 | - |
| 4.0 | 26616 | - | - | - | 0.9074 |
</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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