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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:73392
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum
Nasional pada bulan April 2021?
sentences:
- Statistik Kriminal 2023
- Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y)
- Survei Biaya Hidup (SBH) 2018 Ambon dan Tual
- source_sentence: Usaha pertanian sampingan di Indonesia tahun 2022
sentences:
- Analisis Hasil Survei Dampak Covid-19 Terhadap Pelaku Usaha
- Direktori Usaha Pertanian Lainnya 2022
- EksporImpor September 2018
- source_sentence: Pertumbuhan industri Indonesia 2006-2009
sentences:
- Pertumbuhan Produksi IBS Triwulan III 2019 Naik 4,35 Persen
- Indikator Ekonomi April 2000
- Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009
- source_sentence: 'Sensus ekonomi Kalbar 2016: data usaha'
sentences:
- Pertumbuhan ekonomi Indonesia tahun 2022
- Buletin Statistik Perdagangan Luar Negeri Impor November 2017
- Data jumlah wisatawan mancanegara 2019
- source_sentence: Direktori perusahaan pengelola hutan 2015
sentences:
- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
Negara, April 2017
- Direktori Perusahaan Kehutanan 2015
- Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat
0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.
datasets:
- yahyaabd/bps-semantic-pairs-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 mpnet v1 eval
type: allstats-semantic-mpnet-v1-eval
metrics:
- type: pearson_cosine
value: 0.9721680353379998
name: Pearson Cosine
- type: spearman_cosine
value: 0.8769707416598509
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat semantic mpnet v1 test
type: allstat-semantic-mpnet-v1-test
metrics:
- type: pearson_cosine
value: 0.9714701009323166
name: Pearson Cosine
- type: spearman_cosine
value: 0.8696530606326947
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-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-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:**
- [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-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-mpnet-v1")
# Run inference
sentences = [
'Direktori perusahaan pengelola hutan 2015',
'Direktori Perusahaan Kehutanan 2015',
'Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.',
]
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-mpnet-v1-eval` and `allstat-semantic-mpnet-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | allstats-semantic-mpnet-v1-eval | allstat-semantic-mpnet-v1-test |
|:--------------------|:--------------------------------|:-------------------------------|
| pearson_cosine | 0.9722 | 0.9715 |
| **spearman_cosine** | **0.877** | **0.8697** |
<!--
## 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-semantic-pairs-synthetic-dataset-v1
* Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
* Size: 73,392 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: 11.28 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.71 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>Data bisnis Kalbar sensus 2016</code> | <code>Indikator Ekonomi Oktober 2012</code> | <code>0.1</code> |
| <code>Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan?</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023</code> | <code>0.88</code> |
| <code>Laopran keuagnan lmebaga non proft 20112-013</code> | <code>Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013</code> | <code>0.93</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-semantic-pairs-synthetic-dataset-v1
* Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
* Size: 15,726 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: 4 tokens</li><li>mean: 11.52 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.38 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
| query | doc | label |
|:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------|
| <code>Data transportasi bulan Februari 2021</code> | <code>Tenaga Kerja Februari 2023</code> | <code>0.08</code> |
| <code>Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202?</code> | <code>Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen</code> | <code>1.0</code> |
| <code>Data ekspor dan moda transportasi tahun 2018-2019</code> | <code>Indikator Pasar Tenaga Kerja Indonesia Agustus 2012</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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 24
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 4
- `load_best_model_at_end`: True
- `label_smoothing_factor`: 0.01
- `eval_on_start`: 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`: 24
- `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`: 4
- `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`: 0.01
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mpnet-v1-eval_spearman_cosine | allstat-semantic-mpnet-v1-test_spearman_cosine |
|:-----------:|:---------:|:-------------:|:---------------:|:-----------------------------------------------:|:----------------------------------------------:|
| 0 | 0 | - | 0.1031 | 0.6244 | - |
| 0.2180 | 250 | 0.064 | 0.0413 | 0.6958 | - |
| 0.4359 | 500 | 0.0381 | 0.0305 | 0.7301 | - |
| 0.6539 | 750 | 0.0284 | 0.0243 | 0.7651 | - |
| 0.8718 | 1000 | 0.025 | 0.0213 | 0.7656 | - |
| 1.0898 | 1250 | 0.0207 | 0.0201 | 0.7822 | - |
| 1.3078 | 1500 | 0.0188 | 0.0194 | 0.7805 | - |
| 1.5257 | 1750 | 0.0182 | 0.0177 | 0.7918 | - |
| 1.7437 | 2000 | 0.0177 | 0.0168 | 0.8098 | - |
| 1.9616 | 2250 | 0.0173 | 0.0173 | 0.7979 | - |
| 2.1796 | 2500 | 0.0151 | 0.0174 | 0.8010 | - |
| 2.3976 | 2750 | 0.014 | 0.0163 | 0.8005 | - |
| 2.6155 | 3000 | 0.0142 | 0.0159 | 0.8027 | - |
| 2.8335 | 3250 | 0.0137 | 0.0154 | 0.8074 | - |
| 3.0514 | 3500 | 0.013 | 0.0146 | 0.8173 | - |
| 3.2694 | 3750 | 0.0099 | 0.0138 | 0.8179 | - |
| 3.4874 | 4000 | 0.0105 | 0.0135 | 0.8138 | - |
| 3.7053 | 4250 | 0.0109 | 0.0145 | 0.8138 | - |
| 3.9233 | 4500 | 0.011 | 0.0145 | 0.8244 | - |
| 4.1412 | 4750 | 0.0086 | 0.0132 | 0.8327 | - |
| 4.3592 | 5000 | 0.0077 | 0.0129 | 0.8307 | - |
| 4.5772 | 5250 | 0.0081 | 0.0124 | 0.8380 | - |
| 4.7951 | 5500 | 0.0087 | 0.0128 | 0.8358 | - |
| 5.0131 | 5750 | 0.0076 | 0.0135 | 0.8280 | - |
| 5.2310 | 6000 | 0.0061 | 0.0122 | 0.8399 | - |
| 5.4490 | 6250 | 0.0062 | 0.0119 | 0.8344 | - |
| 5.6670 | 6500 | 0.007 | 0.0113 | 0.8432 | - |
| 5.8849 | 6750 | 0.0069 | 0.0117 | 0.8353 | - |
| 6.1029 | 7000 | 0.0056 | 0.0117 | 0.8333 | - |
| 6.3208 | 7250 | 0.0047 | 0.0114 | 0.8438 | - |
| 6.5388 | 7500 | 0.0059 | 0.0114 | 0.8429 | - |
| 6.7568 | 7750 | 0.0054 | 0.0113 | 0.8452 | - |
| 6.9747 | 8000 | 0.0059 | 0.0118 | 0.8477 | - |
| 7.1927 | 8250 | 0.0045 | 0.0109 | 0.8474 | - |
| 7.4106 | 8500 | 0.0042 | 0.0111 | 0.8532 | - |
| 7.6286 | 8750 | 0.0045 | 0.0114 | 0.8385 | - |
| 7.8466 | 9000 | 0.005 | 0.0111 | 0.8502 | - |
| 8.0645 | 9250 | 0.0045 | 0.0111 | 0.8496 | - |
| 8.2825 | 9500 | 0.0035 | 0.0109 | 0.8490 | - |
| 8.5004 | 9750 | 0.0038 | 0.0112 | 0.8519 | - |
| 8.7184 | 10000 | 0.0038 | 0.0112 | 0.8463 | - |
| 8.9364 | 10250 | 0.0039 | 0.0109 | 0.8556 | - |
| 9.1543 | 10500 | 0.0035 | 0.0110 | 0.8534 | - |
| 9.3723 | 10750 | 0.003 | 0.0111 | 0.8525 | - |
| 9.5902 | 11000 | 0.0039 | 0.0108 | 0.8593 | - |
| 9.8082 | 11250 | 0.0038 | 0.0112 | 0.8537 | - |
| 10.0262 | 11500 | 0.0033 | 0.0108 | 0.8553 | - |
| 10.2441 | 11750 | 0.0023 | 0.0104 | 0.8601 | - |
| 10.4621 | 12000 | 0.0025 | 0.0104 | 0.8571 | - |
| 10.6800 | 12250 | 0.0026 | 0.0106 | 0.8594 | - |
| 10.8980 | 12500 | 0.0026 | 0.0106 | 0.8627 | - |
| 11.1160 | 12750 | 0.0024 | 0.0105 | 0.8623 | - |
| 11.3339 | 13000 | 0.002 | 0.0104 | 0.8614 | - |
| 11.5519 | 13250 | 0.0021 | 0.0103 | 0.8622 | - |
| 11.7698 | 13500 | 0.0025 | 0.0106 | 0.8580 | - |
| 11.9878 | 13750 | 0.0023 | 0.0108 | 0.8613 | - |
| 12.2058 | 14000 | 0.0019 | 0.0106 | 0.8618 | - |
| 12.4237 | 14250 | 0.0017 | 0.0104 | 0.8641 | - |
| 12.6417 | 14500 | 0.0019 | 0.0103 | 0.8620 | - |
| 12.8596 | 14750 | 0.002 | 0.0104 | 0.8649 | - |
| 13.0776 | 15000 | 0.002 | 0.0102 | 0.8620 | - |
| 13.2956 | 15250 | 0.0014 | 0.0103 | 0.8631 | - |
| 13.5135 | 15500 | 0.0018 | 0.0104 | 0.8635 | - |
| 13.7315 | 15750 | 0.0018 | 0.0102 | 0.8661 | - |
| 13.9494 | 16000 | 0.0018 | 0.0104 | 0.8683 | - |
| 14.1674 | 16250 | 0.0014 | 0.0104 | 0.8691 | - |
| 14.3854 | 16500 | 0.0014 | 0.0103 | 0.8668 | - |
| 14.6033 | 16750 | 0.0015 | 0.0102 | 0.8673 | - |
| 14.8213 | 17000 | 0.0016 | 0.0102 | 0.8679 | - |
| 15.0392 | 17250 | 0.0016 | 0.0101 | 0.8688 | - |
| 15.2572 | 17500 | 0.0012 | 0.0102 | 0.8676 | - |
| 15.4752 | 17750 | 0.0012 | 0.0102 | 0.8712 | - |
| 15.6931 | 18000 | 0.0014 | 0.0102 | 0.8702 | - |
| 15.9111 | 18250 | 0.0013 | 0.0101 | 0.8718 | - |
| 16.1290 | 18500 | 0.0011 | 0.0100 | 0.8727 | - |
| 16.3470 | 18750 | 0.001 | 0.0101 | 0.8729 | - |
| 16.5650 | 19000 | 0.0012 | 0.0099 | 0.8714 | - |
| 16.7829 | 19250 | 0.0011 | 0.0101 | 0.8723 | - |
| 17.0009 | 19500 | 0.0012 | 0.0101 | 0.8679 | - |
| 17.2188 | 19750 | 0.0009 | 0.0103 | 0.8706 | - |
| 17.4368 | 20000 | 0.0009 | 0.0101 | 0.8722 | - |
| 17.6548 | 20250 | 0.0009 | 0.0100 | 0.8710 | - |
| 17.8727 | 20500 | 0.001 | 0.0101 | 0.8719 | - |
| 18.0907 | 20750 | 0.0009 | 0.0100 | 0.8728 | - |
| 18.3086 | 21000 | 0.0009 | 0.0100 | 0.8738 | - |
| 18.5266 | 21250 | 0.0008 | 0.0100 | 0.8720 | - |
| 18.7446 | 21500 | 0.0009 | 0.0100 | 0.8731 | - |
| **18.9625** | **21750** | **0.0009** | **0.0098** | **0.8738** | **-** |
| 19.1805 | 22000 | 0.0007 | 0.0100 | 0.8750 | - |
| 19.3984 | 22250 | 0.0007 | 0.0099 | 0.8730 | - |
| 19.6164 | 22500 | 0.0007 | 0.0100 | 0.8753 | - |
| 19.8344 | 22750 | 0.0007 | 0.0099 | 0.8753 | - |
| 20.0523 | 23000 | 0.0008 | 0.0100 | 0.8755 | - |
| 20.2703 | 23250 | 0.0006 | 0.0100 | 0.8747 | - |
| 20.4882 | 23500 | 0.0006 | 0.0101 | 0.8753 | - |
| 20.7062 | 23750 | 0.0007 | 0.0101 | 0.8738 | - |
| 20.9241 | 24000 | 0.0007 | 0.0101 | 0.8750 | - |
| 21.1421 | 24250 | 0.0006 | 0.0101 | 0.8760 | - |
| 21.3601 | 24500 | 0.0006 | 0.0101 | 0.8753 | - |
| 21.5780 | 24750 | 0.0006 | 0.0101 | 0.8759 | - |
| 21.7960 | 25000 | 0.0006 | 0.0100 | 0.8759 | - |
| 22.0139 | 25250 | 0.0006 | 0.0100 | 0.8762 | - |
| 22.2319 | 25500 | 0.0005 | 0.0100 | 0.8767 | - |
| 22.4499 | 25750 | 0.0005 | 0.0100 | 0.8772 | - |
| 22.6678 | 26000 | 0.0005 | 0.0099 | 0.8771 | - |
| 22.8858 | 26250 | 0.0005 | 0.0100 | 0.8769 | - |
| 23.1037 | 26500 | 0.0005 | 0.0100 | 0.8770 | - |
| 23.3217 | 26750 | 0.0005 | 0.0100 | 0.8769 | - |
| 23.5397 | 27000 | 0.0004 | 0.0100 | 0.8769 | - |
| 23.7576 | 27250 | 0.0005 | 0.0100 | 0.8769 | - |
| 23.9756 | 27500 | 0.0005 | 0.0100 | 0.8770 | - |
| 24.0 | 27528 | - | - | - | 0.8697 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- 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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