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---
language:
- id
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:42138
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Informasi importir Indonesia 2014 (Jilid Kedua)
sentences:
- Indikator Konstruksi Triwulan IV-2011
- Benchmark Indeks Konstruksi (2010=100), 1990-2013
- Statistik Upah Q-2 2002-Q-2 2004
- source_sentence: Direktori Perusahaan Penggiling Padi Aceh 2012
sentences:
- Direktori Perusahaan Industri Penggilingan Padi Tahun 2012 Provinsi Aceh
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
Negara, Agustus 2024
- Statistik Harga Produsen Pertanian Subsektor Tanaman Pangan, Hortikultura, dan
Tanaman Perkebunan Rakyat 2022
- source_sentence: Neraca pemerintahan pusat triwulanan 2015-2021:2
sentences:
- Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2017- 2021, Buku 1 Pulau Sumatera
- Statistik Tebu Indonesia 2020
- Indikator Pasar Tenaga Kerja Indonesia Agustus 2011
- source_sentence: Data pembangunan kuartal kedua 2014
sentences:
- Katalog Publikasi BPS 2018
- Indikator Konstruksi Triwulan II-2014
- Produk Domestik Regional Bruto Provinsi-provinsi di Indonesia Menurut Penggunaan
2004-2008
- source_sentence: Laporan keuangan pemerintah provinsi periode 2003-2006
sentences:
- Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014
- Statistik Keuangan Provinsi 2003-2006
- Statistik Industri Manufaktur Indonesia 2013
datasets:
- yahyaabd/bps-publication-title-pairs
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: allstat semantic dev
type: allstat-semantic-dev
metrics:
- type: pearson_cosine
value: 0.970895376756816
name: Pearson Cosine
- type: spearman_cosine
value: 0.8818659766397927
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat semantic test
type: allstat-semantic-test
metrics:
- type: pearson_cosine
value: 0.9674313370370765
name: Pearson Cosine
- type: spearman_cosine
value: 0.8746688546004239
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 [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) 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:**
- [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs)
- **Language:** id
<!-- - **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/allstat-semantic-search-mpnet-base-v2-sts")
# Run inference
sentences = [
'Laporan keuangan pemerintah provinsi periode 2003-2006',
'Statistik Keuangan Provinsi 2003-2006',
'Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014',
]
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: `allstat-semantic-dev` and `allstat-semantic-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | allstat-semantic-dev | allstat-semantic-test |
|:--------------------|:---------------------|:----------------------|
| pearson_cosine | 0.9709 | 0.9674 |
| **spearman_cosine** | **0.8819** | **0.8747** |
<!--
## 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
#### bps-publication-title-pairs
* Dataset: [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) at [833f738](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs/tree/833f738c0a143143e2d0e45a8905fee6b262d859)
* Size: 42,138 training samples
* Columns: <code>query</code>, <code>doc_title</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc_title | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.58 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc_title | score |
|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:------------------|
| <code>Hasil riset mobilitas Jabodetabek tahun 2023</code> | <code>Statistik Komuter Jabodetabek Hasil Survei Komuter Jabodetabek 2023</code> | <code>0.85</code> |
| <code>Indeks harga konsumen di Indonesia tahun 2017 (82 kota)</code> | <code>Harga Konsumen Beberapa Barang dan Jasa Kelompok Sandang di 82 Kota di Indonesia 2017</code> | <code>0.15</code> |
| <code>Laporan sektor bangunan Indonesia Q4 2009</code> | <code>Indikator Konstruksi Triwulan IV Tahun 2009</code> | <code>0.91</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
#### bps-publication-title-pairs
* Dataset: [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) at [833f738](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs/tree/833f738c0a143143e2d0e45a8905fee6b262d859)
* Size: 2,634 evaluation samples
* Columns: <code>query</code>, <code>doc_title</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc_title | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.71 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.57 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.55</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc_title | score |
|:----------------------------------------------------|:------------------------------------------------------------------|:-----------------|
| <code>Statistik tebu Indonesia tahun 2018</code> | <code>Direktori Perusahaan Perkebunan Karet Indonesia 2018</code> | <code>0.1</code> |
| <code>Data industri makanan dan minuman 2017</code> | <code>Statistik Upah Buruh Tani di Perdesaan 2018</code> | <code>0.2</code> |
| <code>Biaya hidup di Gorontalo tahun 2018</code> | <code>Survei Biaya Hidup (SBH) 2018 Gorontalo</code> | <code>0.9</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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 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`: 5
- `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 | allstat-semantic-dev_spearman_cosine | allstat-semantic-test_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:------------------------------------:|:-------------------------------------:|
| 0.0380 | 100 | 0.0498 | 0.0301 | 0.7942 | - |
| 0.0759 | 200 | 0.0274 | 0.0231 | 0.8115 | - |
| 0.1139 | 300 | 0.0238 | 0.0194 | 0.8151 | - |
| 0.1519 | 400 | 0.0203 | 0.0181 | 0.8169 | - |
| 0.1898 | 500 | 0.02 | 0.0184 | 0.8188 | - |
| 0.2278 | 600 | 0.0208 | 0.0170 | 0.8229 | - |
| 0.2658 | 700 | 0.0182 | 0.0176 | 0.8209 | - |
| 0.3037 | 800 | 0.0187 | 0.0165 | 0.8260 | - |
| 0.3417 | 900 | 0.0182 | 0.0169 | 0.8237 | - |
| 0.3797 | 1000 | 0.0187 | 0.0166 | 0.8232 | - |
| 0.4176 | 1100 | 0.019 | 0.0170 | 0.8261 | - |
| 0.4556 | 1200 | 0.0186 | 0.0178 | 0.8206 | - |
| 0.4935 | 1300 | 0.0185 | 0.0173 | 0.8190 | - |
| 0.5315 | 1400 | 0.0188 | 0.0183 | 0.8172 | - |
| 0.5695 | 1500 | 0.018 | 0.0166 | 0.8192 | - |
| 0.6074 | 1600 | 0.0193 | 0.0168 | 0.8240 | - |
| 0.6454 | 1700 | 0.016 | 0.0152 | 0.8315 | - |
| 0.6834 | 1800 | 0.0178 | 0.0163 | 0.8263 | - |
| 0.7213 | 1900 | 0.0174 | 0.0150 | 0.8320 | - |
| 0.7593 | 2000 | 0.0172 | 0.0152 | 0.8290 | - |
| 0.7973 | 2100 | 0.0156 | 0.0158 | 0.8284 | - |
| 0.8352 | 2200 | 0.0164 | 0.0143 | 0.8313 | - |
| 0.8732 | 2300 | 0.0169 | 0.0165 | 0.8349 | - |
| 0.9112 | 2400 | 0.0147 | 0.0150 | 0.8368 | - |
| 0.9491 | 2500 | 0.0163 | 0.0148 | 0.8314 | - |
| 0.9871 | 2600 | 0.0149 | 0.0137 | 0.8379 | - |
| 1.0251 | 2700 | 0.0117 | 0.0134 | 0.8415 | - |
| 1.0630 | 2800 | 0.0124 | 0.0129 | 0.8375 | - |
| 1.1010 | 2900 | 0.0109 | 0.0124 | 0.8459 | - |
| 1.1390 | 3000 | 0.0109 | 0.0123 | 0.8445 | - |
| 1.1769 | 3100 | 0.0107 | 0.0126 | 0.8433 | - |
| 1.2149 | 3200 | 0.0105 | 0.0131 | 0.8427 | - |
| 1.2528 | 3300 | 0.0117 | 0.0130 | 0.8434 | - |
| 1.2908 | 3400 | 0.0107 | 0.0126 | 0.8448 | - |
| 1.3288 | 3500 | 0.0116 | 0.0119 | 0.8490 | - |
| 1.3667 | 3600 | 0.0114 | 0.0124 | 0.8394 | - |
| 1.4047 | 3700 | 0.011 | 0.0127 | 0.8408 | - |
| 1.4427 | 3800 | 0.0116 | 0.0128 | 0.8400 | - |
| 1.4806 | 3900 | 0.0117 | 0.0121 | 0.8451 | - |
| 1.5186 | 4000 | 0.0129 | 0.0125 | 0.8443 | - |
| 1.5566 | 4100 | 0.0117 | 0.0122 | 0.8464 | - |
| 1.5945 | 4200 | 0.012 | 0.0117 | 0.8468 | - |
| 1.6325 | 4300 | 0.011 | 0.0122 | 0.8485 | - |
| 1.6705 | 4400 | 0.0121 | 0.0112 | 0.8557 | - |
| 1.7084 | 4500 | 0.0119 | 0.0110 | 0.8570 | - |
| 1.7464 | 4600 | 0.0105 | 0.0113 | 0.8519 | - |
| 1.7844 | 4700 | 0.0101 | 0.0113 | 0.8479 | - |
| 1.8223 | 4800 | 0.0111 | 0.0116 | 0.8499 | - |
| 1.8603 | 4900 | 0.0108 | 0.0117 | 0.8520 | - |
| 1.8983 | 5000 | 0.0111 | 0.0111 | 0.8509 | - |
| 1.9362 | 5100 | 0.0112 | 0.0111 | 0.8546 | - |
| 1.9742 | 5200 | 0.0104 | 0.0115 | 0.8507 | - |
| 2.0121 | 5300 | 0.0095 | 0.0105 | 0.8553 | - |
| 2.0501 | 5400 | 0.0077 | 0.0106 | 0.8562 | - |
| 2.0881 | 5500 | 0.007 | 0.0104 | 0.8575 | - |
| 2.1260 | 5600 | 0.0075 | 0.0101 | 0.8619 | - |
| 2.1640 | 5700 | 0.0077 | 0.0104 | 0.8568 | - |
| 2.2020 | 5800 | 0.0073 | 0.0103 | 0.8588 | - |
| 2.2399 | 5900 | 0.0076 | 0.0101 | 0.8598 | - |
| 2.2779 | 6000 | 0.0072 | 0.0101 | 0.8602 | - |
| 2.3159 | 6100 | 0.0076 | 0.0104 | 0.8589 | - |
| 2.3538 | 6200 | 0.007 | 0.0101 | 0.8592 | - |
| 2.3918 | 6300 | 0.0084 | 0.0104 | 0.8547 | - |
| 2.4298 | 6400 | 0.0077 | 0.0102 | 0.8594 | - |
| 2.4677 | 6500 | 0.008 | 0.0102 | 0.8606 | - |
| 2.5057 | 6600 | 0.0075 | 0.0101 | 0.8596 | - |
| 2.5437 | 6700 | 0.0072 | 0.0105 | 0.8587 | - |
| 2.5816 | 6800 | 0.0079 | 0.0105 | 0.8588 | - |
| 2.6196 | 6900 | 0.0078 | 0.0098 | 0.8605 | - |
| 2.6576 | 7000 | 0.0075 | 0.0100 | 0.8593 | - |
| 2.6955 | 7100 | 0.008 | 0.0097 | 0.8649 | - |
| 2.7335 | 7200 | 0.0074 | 0.0100 | 0.8602 | - |
| 2.7715 | 7300 | 0.0069 | 0.0098 | 0.8628 | - |
| 2.8094 | 7400 | 0.008 | 0.0097 | 0.8615 | - |
| 2.8474 | 7500 | 0.007 | 0.0097 | 0.8639 | - |
| 2.8853 | 7600 | 0.0071 | 0.0093 | 0.8642 | - |
| 2.9233 | 7700 | 0.0077 | 0.0102 | 0.8605 | - |
| 2.9613 | 7800 | 0.008 | 0.0094 | 0.8623 | - |
| 2.9992 | 7900 | 0.0076 | 0.0094 | 0.8658 | - |
| 3.0372 | 8000 | 0.005 | 0.0091 | 0.8673 | - |
| 3.0752 | 8100 | 0.005 | 0.0088 | 0.8688 | - |
| 3.1131 | 8200 | 0.0051 | 0.0088 | 0.8705 | - |
| 3.1511 | 8300 | 0.0052 | 0.0089 | 0.8701 | - |
| 3.1891 | 8400 | 0.0047 | 0.0088 | 0.8711 | - |
| 3.2270 | 8500 | 0.0046 | 0.0086 | 0.8723 | - |
| 3.2650 | 8600 | 0.0051 | 0.0086 | 0.8733 | - |
| 3.3030 | 8700 | 0.0053 | 0.0088 | 0.8736 | - |
| 3.3409 | 8800 | 0.0049 | 0.0086 | 0.8733 | - |
| 3.3789 | 8900 | 0.0051 | 0.0087 | 0.8721 | - |
| 3.4169 | 9000 | 0.0051 | 0.0086 | 0.8716 | - |
| 3.4548 | 9100 | 0.005 | 0.0087 | 0.8717 | - |
| 3.4928 | 9200 | 0.0055 | 0.0088 | 0.8709 | - |
| 3.5308 | 9300 | 0.0046 | 0.0085 | 0.8738 | - |
| 3.5687 | 9400 | 0.0052 | 0.0085 | 0.8738 | - |
| 3.6067 | 9500 | 0.0052 | 0.0089 | 0.8706 | - |
| 3.6446 | 9600 | 0.0049 | 0.0085 | 0.8722 | - |
| 3.6826 | 9700 | 0.0051 | 0.0088 | 0.8720 | - |
| 3.7206 | 9800 | 0.0046 | 0.0088 | 0.8721 | - |
| 3.7585 | 9900 | 0.0051 | 0.0083 | 0.8757 | - |
| 3.7965 | 10000 | 0.005 | 0.0084 | 0.8744 | - |
| 3.8345 | 10100 | 0.005 | 0.0084 | 0.8754 | - |
| 3.8724 | 10200 | 0.0054 | 0.0087 | 0.8737 | - |
| 3.9104 | 10300 | 0.0054 | 0.0083 | 0.8757 | - |
| 3.9484 | 10400 | 0.005 | 0.0082 | 0.8754 | - |
| 3.9863 | 10500 | 0.0049 | 0.0083 | 0.8746 | - |
| 4.0243 | 10600 | 0.0041 | 0.0081 | 0.8757 | - |
| 4.0623 | 10700 | 0.0034 | 0.0082 | 0.8760 | - |
| 4.1002 | 10800 | 0.003 | 0.0083 | 0.8751 | - |
| 4.1382 | 10900 | 0.0033 | 0.0082 | 0.8770 | - |
| 4.1762 | 11000 | 0.0034 | 0.0083 | 0.8772 | - |
| 4.2141 | 11100 | 0.0033 | 0.0082 | 0.8773 | - |
| 4.2521 | 11200 | 0.0031 | 0.0082 | 0.8787 | - |
| 4.2901 | 11300 | 0.0033 | 0.0080 | 0.8805 | - |
| 4.3280 | 11400 | 0.0029 | 0.0082 | 0.8787 | - |
| 4.3660 | 11500 | 0.0035 | 0.0079 | 0.8796 | - |
| 4.4039 | 11600 | 0.0034 | 0.0079 | 0.8799 | - |
| 4.4419 | 11700 | 0.0032 | 0.0079 | 0.8794 | - |
| 4.4799 | 11800 | 0.0035 | 0.0079 | 0.8807 | - |
| 4.5178 | 11900 | 0.0035 | 0.0080 | 0.8798 | - |
| 4.5558 | 12000 | 0.0031 | 0.0079 | 0.8806 | - |
| 4.5938 | 12100 | 0.0034 | 0.0078 | 0.8812 | - |
| 4.6317 | 12200 | 0.0031 | 0.0078 | 0.8811 | - |
| 4.6697 | 12300 | 0.0032 | 0.0078 | 0.8813 | - |
| 4.7077 | 12400 | 0.0032 | 0.0079 | 0.8809 | - |
| 4.7456 | 12500 | 0.0032 | 0.0078 | 0.8815 | - |
| 4.7836 | 12600 | 0.0034 | 0.0077 | 0.8818 | - |
| 4.8216 | 12700 | 0.0035 | 0.0078 | 0.8817 | - |
| 4.8595 | 12800 | 0.0032 | 0.0078 | 0.8818 | - |
| 4.8975 | 12900 | 0.0032 | 0.0078 | 0.8818 | - |
| 4.9355 | 13000 | 0.0032 | 0.0078 | 0.8820 | - |
| 4.9734 | 13100 | 0.0031 | 0.0078 | 0.8819 | - |
| 5.0 | 13170 | - | - | - | 0.8747 |
</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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