sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:967831 loss:MultipleNegativesRankingLoss dataset:yahyaabd/statictable-triplets-all arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
- yahyaabd/statictable-triplets-all
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@1
- cosine_ndcg@5
- cosine_ndcg@10
- cosine_mrr@1
- cosine_mrr@5
- cosine_mrr@10
- cosine_map@1
- cosine_map@5
- cosine_map@10
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:967831
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Laporan ringkasan aliran dana Indonesia 2007, dalam Rupiah
  sentences:
  - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
    dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kalimantan Selatan, 2018-2023
  - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan
    Utama, 2019
  - Ringkasan Neraca Arus Dana, 2007, (Miliar Rupiah)
- source_sentence: Data transaksi (harga produsen) 9 sektor ekonomi Indonesia (miliar
    rupiah) di tahun 2005
  sentences:
  - Transaksi Total Atas Dasar Harga Produsen 9 Sektor Ekonomi (miliar rupiah), 2005
  - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur,
    2020
  - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi
    yang Ditamatkan, 2023
- source_sentence: Bagaimana tren ekspor teh Indonesia ke berbagai negara tahun 2008?
  sentences:
  - Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi
    dan Jumlah Jam Kerja Utama, 2020
  - Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib),
    dan Nilai Tukar Petani Subsektor Perikanan (NTN) Menurut Provinsi (2007=100),
    2008-2016
  - Ekspor Teh Menurut Negara Tujuan Utama, 2000-2015
- source_sentence: Data bulanan rata-rata partikel terlarut di udara (microgram/m3)
    di berbagai kota, 2011
  sentences:
  - Rata-Rata Bulanan Konsentrasi Partikel Terlarut di Udara Beberapa Kota Menurut
    Bulan dan Kota (&#956;gr/m3), 2006-2015
  - Jumlah Kunjungan Kapal di Pelabuhan Yang Diusahakan dan Tidak Diusahakan Tahun
    1995-2022
  - Persentase RataRata Pengeluaran per Kapita Sebulan Untuk Makanan dan Bukan Makanan
    di Daerah Perkotaan Menurut Provinsi, 2007-2023
- source_sentence: Perbandingan Indeks Harga Konsumen antar negara, data tahun 2011
  sentences:
  - Ekspor Sarang Burung menurut Negara Tujuan Utama, 2012-2023
  - Indeks Harga Konsumen Beberapa Negara (2005=100), 2009-2012
  - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
    dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kepulauan Riau, 2018-2023
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: bps statictable ir
      type: bps-statictable-ir
    metrics:
    - type: cosine_accuracy@1
      value: 0.9120521172638436
      name: Cosine Accuracy@1
    - type: cosine_accuracy@5
      value: 0.9837133550488599
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9837133550488599
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9120521172638436
      name: Cosine Precision@1
    - type: cosine_precision@5
      value: 0.22540716612377845
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.12345276872964168
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7067640321227509
      name: Cosine Recall@1
    - type: cosine_recall@5
      value: 0.7895383471024489
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7966572197631681
      name: Cosine Recall@10
    - type: cosine_ndcg@1
      value: 0.9120521172638436
      name: Cosine Ndcg@1
    - type: cosine_ndcg@5
      value: 0.8250390762686425
      name: Cosine Ndcg@5
    - type: cosine_ndcg@10
      value: 0.8137327953249293
      name: Cosine Ndcg@10
    - type: cosine_mrr@1
      value: 0.9120521172638436
      name: Cosine Mrr@1
    - type: cosine_mrr@5
      value: 0.942128121606949
      name: Cosine Mrr@5
    - type: cosine_mrr@10
      value: 0.942128121606949
      name: Cosine Mrr@10
    - type: cosine_map@1
      value: 0.9120521172638436
      name: Cosine Map@1
    - type: cosine_map@5
      value: 0.7806442272891784
      name: Cosine Map@5
    - type: cosine_map@10
      value: 0.7663176671319993
      name: Cosine Map@10
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all)
<!-- - **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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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/paraphrase-multilingual-miniLM-L12-V2-mnrl-2")
# Run inference
sentences = [
    'Perbandingan Indeks Harga Konsumen antar negara, data tahun 2011',
    'Indeks Harga Konsumen Beberapa Negara (2005=100), 2009-2012',
    'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kepulauan Riau, 2018-2023',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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>
-->

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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `bps-statictable-ir`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9121     |
| cosine_accuracy@5   | 0.9837     |
| cosine_accuracy@10  | 0.9837     |
| cosine_precision@1  | 0.9121     |
| cosine_precision@5  | 0.2254     |
| cosine_precision@10 | 0.1235     |
| cosine_recall@1     | 0.7068     |
| cosine_recall@5     | 0.7895     |
| cosine_recall@10    | 0.7967     |
| cosine_ndcg@1       | 0.9121     |
| cosine_ndcg@5       | 0.825      |
| **cosine_ndcg@10**  | **0.8137** |
| cosine_mrr@1        | 0.9121     |
| cosine_mrr@5        | 0.9421     |
| cosine_mrr@10       | 0.9421     |
| cosine_map@1        | 0.9121     |
| cosine_map@5        | 0.7806     |
| cosine_map@10       | 0.7663     |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### statictable-triplets-all

* Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030)
* Size: 967,831 training samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | pos                                                                               | neg                                                                               |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 18.52 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.18 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.87 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
  | query                                                                                                                                         | pos                                                                                                                                                                        | neg                                                                                                                                              |
  |:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Pekerja 15+ berdasarkan status pekerjaan & pendidikan, 1997-2007</code>                                                                 | <code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Status Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 1997 - 2007</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur, 2023</code>                                               |
  | <code>Output kilang minyak dan gas Indonesia berdasarkan jenis produk, dalam barel, sekitar tahun 2008</code>                                 | <code>Produksi Beberapa Hasil Kilang Minyak dan Gas Menurut Jenis Hasil Kilang (barel), 2000-2018</code>                                                                   | <code>Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021</code>                                       |
  | <code>Berapa rata-rata pendapatan (ribu rupiah) pekerja bebas di Indonesia tahun 2018 berdasarkan provinsi dan pendidikan terakhirnya?</code> | <code>Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2018</code>                                       | <code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### statictable-triplets-all

* Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030)
* Size: 967,831 evaluation samples
* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | pos                                                                               | neg                                                                               |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 18.46 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.44 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.54 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | query                                                                                                         | pos                                                                                                             | neg                                                                                                                                     |
  |:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Data pendapatan rata-rata pengusaha di tiap provinsi berdasarkan tingkat pendidikan (tahun 2022)</code> | <code>Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang Ditamatkan, 2022</code> | <code>Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2011-2015</code>                                                       |
  | <code>Berapa harga rata-rata valuta asing di berbagai provinsi Indonesia pada tahun 2017?</code>              | <code>Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017</code>                                        | <code>Kumulatif Kasus AIDS, Kasus Meninggal, Rate Kumulatif, dan Jumlah Kasus Baru AIDS Menurut Provinsi di Indonesia, 2008-2012</code> |
  | <code>Ikhtisar arus kas triwulan 1, 2004 (miliar)</code>                                                      | <code>Ringkasan Neraca Arus Dana Triwulan I 2004 (Miliar Rupiah)</code>                                         | <code>Ekspor Bijih Tembaga Menurut Negara Tujuan Utama, 2012-2023</code>                                                                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `save_on_each_node`: True
- `fp16`: True
- `dataloader_num_workers`: 2
- `load_best_model_at_end`: True
- `eval_on_start`: True
- `batch_sampler`: no_duplicates

#### 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`: 1
- `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`: True
- `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`: 2
- `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`: 0.0
- `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`: True
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step      | Training Loss | Validation Loss | bps-statictable-ir_cosine_ndcg@10 |
|:----------:|:---------:|:-------------:|:---------------:|:---------------------------------:|
| 0          | 0         | -             | 1.1582          | 0.4643                            |
| 0.0209     | 250       | 0.4825        | 0.0930          | 0.7864                            |
| 0.0417     | 500       | 0.0746        | 0.0339          | 0.8081                            |
| 0.0626     | 750       | 0.0337        | 0.0239          | 0.8064                            |
| 0.0835     | 1000      | 0.0226        | 0.0149          | 0.8140                            |
| 0.1044     | 1250      | 0.0174        | 0.0168          | 0.8267                            |
| 0.1252     | 1500      | 0.0152        | 0.0185          | 0.8015                            |
| 0.1461     | 1750      | 0.0127        | 0.0142          | 0.8269                            |
| 0.1670     | 2000      | 0.0091        | 0.0104          | 0.8394                            |
| 0.1879     | 2250      | 0.0089        | 0.0126          | 0.8255                            |
| 0.2087     | 2500      | 0.0055        | 0.0133          | 0.8299                            |
| 0.2296     | 2750      | 0.008         | 0.0086          | 0.8293                            |
| 0.2505     | 3000      | 0.0087        | 0.0090          | 0.8317                            |
| 0.2714     | 3250      | 0.0056        | 0.0061          | 0.8221                            |
| 0.2922     | 3500      | 0.0057        | 0.0064          | 0.8220                            |
| 0.3131     | 3750      | 0.0074        | 0.0056          | 0.8174                            |
| 0.3340     | 4000      | 0.0033        | 0.0048          | 0.8263                            |
| 0.3548     | 4250      | 0.0028        | 0.0054          | 0.8263                            |
| 0.3757     | 4500      | 0.0036        | 0.0052          | 0.8055                            |
| 0.3966     | 4750      | 0.0037        | 0.0037          | 0.8017                            |
| 0.4175     | 5000      | 0.0045        | 0.0039          | 0.8242                            |
| 0.4383     | 5250      | 0.0018        | 0.0035          | 0.8206                            |
| 0.4592     | 5500      | 0.0013        | 0.0051          | 0.8202                            |
| 0.4801     | 5750      | 0.0033        | 0.0033          | 0.8239                            |
| 0.5010     | 6000      | 0.0017        | 0.0021          | 0.8210                            |
| 0.5218     | 6250      | 0.0055        | 0.0119          | 0.8222                            |
| 0.5427     | 6500      | 0.0019        | 0.0016          | 0.8070                            |
| 0.5636     | 6750      | 0.001         | 0.0015          | 0.8128                            |
| 0.5845     | 7000      | 0.0022        | 0.0013          | 0.8236                            |
| 0.6053     | 7250      | 0.001         | 0.0005          | 0.8201                            |
| 0.6262     | 7500      | 0.0005        | 0.0004          | 0.8226                            |
| 0.6471     | 7750      | 0.001         | 0.0010          | 0.8251                            |
| 0.6679     | 8000      | 0.0009        | 0.0011          | 0.8182                            |
| 0.6888     | 8250      | 0.0015        | 0.0008          | 0.8185                            |
| 0.7097     | 8500      | 0.0006        | 0.0005          | 0.8153                            |
| 0.7306     | 8750      | 0.0012        | 0.0012          | 0.8133                            |
| 0.7514     | 9000      | 0.0016        | 0.0007          | 0.8134                            |
| 0.7723     | 9250      | 0.0011        | 0.0001          | 0.8152                            |
| 0.7932     | 9500      | 0.0006        | 0.0002          | 0.8149                            |
| 0.8141     | 9750      | 0.0011        | 0.0002          | 0.8141                            |
| 0.8349     | 10000     | 0.0006        | 0.0002          | 0.8153                            |
| 0.8558     | 10250     | 0.0002        | 0.0002          | 0.8174                            |
| **0.8767** | **10500** | **0.0009**    | **0.0001**      | **0.8117**                        |
| 0.8976     | 10750     | 0.0006        | 0.0003          | 0.8117                            |
| 0.9184     | 11000     | 0.0006        | 0.0003          | 0.8122                            |
| 0.9393     | 11250     | 0.0008        | 0.0003          | 0.8161                            |
| 0.9602     | 11500     | 0.0005        | 0.0003          | 0.8151                            |
| 0.9810     | 11750     | 0.0004        | 0.0003          | 0.8137                            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.4.1
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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