sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:123637 loss:CosineSimilarityLoss dataset:yahyaabd/allstats-semantic-synthetic-dataset-v1 arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
datasets:
- yahyaabd/allstats-semantic-synthetic-dataset-v1
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:123637
- loss:CosineSimilarityLoss
widget:
- source_sentence: Analisis biaya hidup di tiga kota Banten thn 2018
  sentences:
  - Indikator Konstruksi Triwulan I-2007
  - Survei Biaya Hidup (SBH) 2018 Bengkulu
  - Indikator Ekonomi Februari 2002
- source_sentence: Grafik ekspor hasil minyak Indonesia ke berbagai negara dari tahun
    2000 hingga 2023.
  sentences:
  - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65)
  - Harga Produsen Gabah dan Beras Januari 2020
  - Profil Usaha Konstruksi Perorangan Provinsi Papua 2016
- source_sentence: Tren konstruksi Indonesia tahun 2007 Q4
  sentences:
  - Laporan Bulanan Data Sosial Ekonomi Desember 2018
  - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2023
  - Inflasi Februari 2008 sebesar 0,5 persen
- source_sentence: Informasi tentang kepemilikan dan penggunaan AC di rumah tangga
    Indonesia tahun 2013?
  sentences:
  - Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2014
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
    dan Jenis Pekerjaan, 2022-2023
  - Indikator Konstruksi, Triwulan II-2022
- source_sentence: Statistik harga Ternate 2012
  sentences:
  - Statistik Perhubungan 2005
  - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019
  - Indikator Ekonomi Agustus 2002
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 base v1 eval
      type: allstats-semantic-base-v1-eval
    metrics:
    - type: pearson_cosine
      value: 0.9868927327091045
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9277441071536588
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat semantic base v1 test
      type: allstat-semantic-base-v1-test
    metrics:
    - type: pearson_cosine
      value: 0.9867639981224826
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9256998894451143
      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-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-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-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-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-base-v1-2")
# Run inference
sentences = [
    'Statistik harga Ternate 2012',
    'Indikator Ekonomi Agustus 2002',
    'Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019',
]
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-base-v1-eval` and `allstat-semantic-base-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test |
|:--------------------|:-------------------------------|:------------------------------|
| pearson_cosine      | 0.9869                         | 0.9868                        |
| **spearman_cosine** | **0.9277**                     | **0.9257**                    |

<!--
## 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-synthetic-dataset-v1

* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
* Size: 123,637 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: 10.59 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.29 tokens</li><li>max: 56 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>Analisis upah tenaga kerja ekonomi kreatif</code>                                                             | <code>Upah Tenaga Kerja Ekonomi Kreatif 2011-2016</code>                                                                        | <code>0.88</code> |
  | <code>cari data persentase rumah tangga yang menggunakan listrik pln menurut provinsi dari 1993 sampai 2022.</code> | <code>Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022</code>                              | <code>0.93</code> |
  | <code>apakah ada tabel yang menunjukkan ekspor minyak mentah ke negara tujuan utama tahun 2000-2023?</code>         | <code>IHK dan Rata-rata Upah per Bulan Buruh Peternakan dan Perikanan di Bawah Mandor (Supervisor), 2012-2014 (2012=100)</code> | <code>0.13</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-synthetic-dataset-v1

* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
* Size: 26,494 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: 10.66 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.94 tokens</li><li>max: 70 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>SBH Aceh 2018: Meulaboh, Banda Aceh, Lhokseumawe</code> | <code>Survei Biaya Hidup (SBH) 2018 Meulaboh, Banda Aceh, dan Lhokseumawe</code> | <code>0.9</code>  |
  | <code>ekspor produk indonesia juli 2018 per negara</code>     | <code>Direktori Perusahaan Pertambangan Besar 2013</code>                        | <code>0.07</code> |
  | <code>peternakan sapi di jawa tengah 2011</code>              | <code>Laporan Bulanan Data Sosial Ekonomi Juli 2024</code>                       | <code>0.07</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`: 0.1
- `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`: 0.1
- `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
| Epoch       | Step      | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
|:-----------:|:---------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:|
| 0           | 0         | -             | 0.0942          | 0.6574                                         | -                                             |
| 0.2588      | 500       | 0.0449        | 0.0262          | 0.7353                                         | -                                             |
| 0.5176      | 1000      | 0.0232        | 0.0185          | 0.7592                                         | -                                             |
| 0.7764      | 1500      | 0.0172        | 0.0154          | 0.7760                                         | -                                             |
| 1.0352      | 2000      | 0.0153        | 0.0137          | 0.7905                                         | -                                             |
| 1.2940      | 2500      | 0.0124        | 0.0130          | 0.7920                                         | -                                             |
| 1.5528      | 3000      | 0.0119        | 0.0120          | 0.8048                                         | -                                             |
| 1.8116      | 3500      | 0.0121        | 0.0121          | 0.8021                                         | -                                             |
| 2.0704      | 4000      | 0.0114        | 0.0112          | 0.8018                                         | -                                             |
| 2.3292      | 4500      | 0.0093        | 0.0117          | 0.7996                                         | -                                             |
| 2.5880      | 5000      | 0.0097        | 0.0105          | 0.8133                                         | -                                             |
| 2.8468      | 5500      | 0.0092        | 0.0103          | 0.8137                                         | -                                             |
| 3.1056      | 6000      | 0.0085        | 0.0094          | 0.8247                                         | -                                             |
| 3.3644      | 6500      | 0.0068        | 0.0090          | 0.8326                                         | -                                             |
| 3.6232      | 7000      | 0.0073        | 0.0092          | 0.8273                                         | -                                             |
| 3.8820      | 7500      | 0.007         | 0.0084          | 0.8404                                         | -                                             |
| 4.1408      | 8000      | 0.0061        | 0.0083          | 0.8381                                         | -                                             |
| 4.3996      | 8500      | 0.0057        | 0.0082          | 0.8382                                         | -                                             |
| 4.6584      | 9000      | 0.0056        | 0.0074          | 0.8458                                         | -                                             |
| 4.9172      | 9500      | 0.0057        | 0.0073          | 0.8468                                         | -                                             |
| 5.1760      | 10000     | 0.0045        | 0.0071          | 0.8508                                         | -                                             |
| 5.4348      | 10500     | 0.0041        | 0.0069          | 0.8579                                         | -                                             |
| 5.6936      | 11000     | 0.0047        | 0.0069          | 0.8471                                         | -                                             |
| 5.9524      | 11500     | 0.0046        | 0.0067          | 0.8554                                         | -                                             |
| 6.2112      | 12000     | 0.0034        | 0.0062          | 0.8616                                         | -                                             |
| 6.4700      | 12500     | 0.0034        | 0.0063          | 0.8636                                         | -                                             |
| 6.7288      | 13000     | 0.0036        | 0.0062          | 0.8649                                         | -                                             |
| 6.9876      | 13500     | 0.0037        | 0.0063          | 0.8641                                         | -                                             |
| 7.2464      | 14000     | 0.0027        | 0.0059          | 0.8691                                         | -                                             |
| 7.5052      | 14500     | 0.0027        | 0.0060          | 0.8733                                         | -                                             |
| 7.7640      | 15000     | 0.0031        | 0.0060          | 0.8748                                         | -                                             |
| 8.0228      | 15500     | 0.0028        | 0.0058          | 0.8736                                         | -                                             |
| 8.2816      | 16000     | 0.0023        | 0.0055          | 0.8785                                         | -                                             |
| 8.5404      | 16500     | 0.0025        | 0.0054          | 0.8801                                         | -                                             |
| 8.7992      | 17000     | 0.0024        | 0.0058          | 0.8809                                         | -                                             |
| 9.0580      | 17500     | 0.0026        | 0.0058          | 0.8811                                         | -                                             |
| 9.3168      | 18000     | 0.002         | 0.0055          | 0.8824                                         | -                                             |
| 9.5756      | 18500     | 0.002         | 0.0053          | 0.8859                                         | -                                             |
| 9.8344      | 19000     | 0.0021        | 0.0053          | 0.8851                                         | -                                             |
| 10.0932     | 19500     | 0.0019        | 0.0055          | 0.8904                                         | -                                             |
| 10.3520     | 20000     | 0.0016        | 0.0052          | 0.8946                                         | -                                             |
| 10.6108     | 20500     | 0.0017        | 0.0057          | 0.8884                                         | -                                             |
| 10.8696     | 21000     | 0.0019        | 0.0055          | 0.8889                                         | -                                             |
| 11.1284     | 21500     | 0.0016        | 0.0052          | 0.8942                                         | -                                             |
| 11.3872     | 22000     | 0.0014        | 0.0053          | 0.8961                                         | -                                             |
| 11.6460     | 22500     | 0.0016        | 0.0053          | 0.8928                                         | -                                             |
| 11.9048     | 23000     | 0.0017        | 0.0051          | 0.8947                                         | -                                             |
| 12.1636     | 23500     | 0.0013        | 0.0050          | 0.9015                                         | -                                             |
| 12.4224     | 24000     | 0.0012        | 0.0059          | 0.8886                                         | -                                             |
| 12.6812     | 24500     | 0.0014        | 0.0051          | 0.9030                                         | -                                             |
| 12.9400     | 25000     | 0.0014        | 0.0051          | 0.9012                                         | -                                             |
| 13.1988     | 25500     | 0.0011        | 0.0050          | 0.9037                                         | -                                             |
| 13.4576     | 26000     | 0.0011        | 0.0050          | 0.9053                                         | -                                             |
| 13.7164     | 26500     | 0.0011        | 0.0049          | 0.9060                                         | -                                             |
| 13.9752     | 27000     | 0.0011        | 0.0049          | 0.9086                                         | -                                             |
| 14.2340     | 27500     | 0.001         | 0.0048          | 0.9063                                         | -                                             |
| 14.4928     | 28000     | 0.001         | 0.0051          | 0.9056                                         | -                                             |
| 14.7516     | 28500     | 0.001         | 0.0051          | 0.9079                                         | -                                             |
| 15.0104     | 29000     | 0.0011        | 0.0049          | 0.9080                                         | -                                             |
| 15.2692     | 29500     | 0.0008        | 0.0048          | 0.9126                                         | -                                             |
| 15.5280     | 30000     | 0.0008        | 0.0049          | 0.9112                                         | -                                             |
| 15.7867     | 30500     | 0.0008        | 0.0049          | 0.9123                                         | -                                             |
| 16.0455     | 31000     | 0.0008        | 0.0048          | 0.9133                                         | -                                             |
| 16.3043     | 31500     | 0.0006        | 0.0048          | 0.9103                                         | -                                             |
| 16.5631     | 32000     | 0.0007        | 0.0049          | 0.9144                                         | -                                             |
| 16.8219     | 32500     | 0.0008        | 0.0048          | 0.9143                                         | -                                             |
| 17.0807     | 33000     | 0.0007        | 0.0048          | 0.9159                                         | -                                             |
| 17.3395     | 33500     | 0.0007        | 0.0047          | 0.9174                                         | -                                             |
| 17.5983     | 34000     | 0.0006        | 0.0048          | 0.9175                                         | -                                             |
| 17.8571     | 34500     | 0.0007        | 0.0047          | 0.9163                                         | -                                             |
| 18.1159     | 35000     | 0.0006        | 0.0046          | 0.9195                                         | -                                             |
| 18.3747     | 35500     | 0.0006        | 0.0047          | 0.9190                                         | -                                             |
| 18.6335     | 36000     | 0.0006        | 0.0047          | 0.9192                                         | -                                             |
| 18.8923     | 36500     | 0.0006        | 0.0047          | 0.9204                                         | -                                             |
| 19.1511     | 37000     | 0.0005        | 0.0047          | 0.9219                                         | -                                             |
| 19.4099     | 37500     | 0.0004        | 0.0046          | 0.9218                                         | -                                             |
| 19.6687     | 38000     | 0.0005        | 0.0047          | 0.9221                                         | -                                             |
| 19.9275     | 38500     | 0.0005        | 0.0046          | 0.9230                                         | -                                             |
| 20.1863     | 39000     | 0.0005        | 0.0046          | 0.9233                                         | -                                             |
| 20.4451     | 39500     | 0.0004        | 0.0046          | 0.9240                                         | -                                             |
| 20.7039     | 40000     | 0.0005        | 0.0047          | 0.9234                                         | -                                             |
| 20.9627     | 40500     | 0.0004        | 0.0047          | 0.9241                                         | -                                             |
| 21.2215     | 41000     | 0.0004        | 0.0046          | 0.9253                                         | -                                             |
| 21.4803     | 41500     | 0.0004        | 0.0046          | 0.9259                                         | -                                             |
| 21.7391     | 42000     | 0.0004        | 0.0046          | 0.9262                                         | -                                             |
| **21.9979** | **42500** | **0.0004**    | **0.0046**      | **0.9263**                                     | **-**                                         |
| 22.2567     | 43000     | 0.0003        | 0.0046          | 0.9266                                         | -                                             |
| 22.5155     | 43500     | 0.0003        | 0.0046          | 0.9266                                         | -                                             |
| 22.7743     | 44000     | 0.0003        | 0.0046          | 0.9273                                         | -                                             |
| 23.0331     | 44500     | 0.0003        | 0.0046          | 0.9273                                         | -                                             |
| 23.2919     | 45000     | 0.0003        | 0.0046          | 0.9274                                         | -                                             |
| 23.5507     | 45500     | 0.0003        | 0.0046          | 0.9277                                         | -                                             |
| 23.8095     | 46000     | 0.0003        | 0.0046          | 0.9277                                         | -                                             |
| 24.0        | 46368     | -             | -               | -                                              | 0.9257                                        |

* The bold row denotes the saved checkpoint.

### 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",
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->