sentence-transformers safetensors xlm-roberta 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-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/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: Penghasilan wirausaha per provinsi & kelompok umur, 2021
  sentences:
  - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur,
    2021
  - Impor Semen Menurut Negara Asal Utama, 2017-2023
  - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur,
    2019
- source_sentence: Sungai-sungai dengan catchment area > 1000 km2, berapa luas dan
    debit airnya, 2003
  sentences:
  - Ringkasan Neraca Arus Dana, Triwulan Keempat, 2005, (Miliar Rupiah)
  - Realisasi Pendapatan dan Belanja Pemerintah Desa ( juta rupiah), 2012-2016
  - Luas Daerah Pengaliran dan Debit dari Beberapa Sungai yang Daerah Pengalirannya
    Lebih dari 1.000 km2, 2000-2011
- source_sentence: Cari data GDP Indonesia tahun 2014 berdasarkan harga berlaku dan
    kelompok lapangan usaha (miliar Rupiah)
  sentences:
  - Luas Kawasan Hutan dan Kawasan Konservasi Perairan Indonesia Berdasarkan Surat
    Keputusan Menteri Lingkungan Hidup dan Kehutanan, 2017-2022
  - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
    yang Ditamatkan dan Jenis Pekerjaan Utama (rupiah), 2016
  - '[Seri 2000] PDB Atas Dasar Harga Berlaku Menurut Lapangan Usaha (Miliar Rupiah),
    2000-2014'
- source_sentence: Bagaimana indeks harga yang diterima dan dibayar petani, serta
    NTP, di berbagai provinsi tahun 2016?
  sentences:
  - Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib),
    dan Nilai Tukar Petani (NTP) Menurut Provinsi, 2008-2016
  - 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
  - Rekap Dokumen RPB Kabupaten/Kota
- source_sentence: Seperti apa rincian inflasi di Indonesia, termasuk inflasi inti
    dan harga diatur, pada 2020?
  sentences:
  - Produk Domestik Regional Bruto Tanpa Migas per Kapita Atas Dasar Harga Konstan
    2000 Menurut Provinsi (ribu rupiah), 2000-2013
  - Angka Kematian Ibu/AKI (Maternal Mortality Rate/MMR) Hasil Long Form SP2020 Menurut
    Provinsi, 2020
  - Inflasi Umum, Inti, Harga Yang Diatur Pemerintah, dan Barang Bergejolak Inflasi
    Indonesia, 2009-2024
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: bps statictable ir
      type: bps-statictable-ir
    metrics:
    - type: cosine_accuracy@1
      value: 0.9609120521172638
      name: Cosine Accuracy@1
    - type: cosine_accuracy@5
      value: 0.996742671009772
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9609120521172638
      name: Cosine Precision@1
    - type: cosine_precision@5
      value: 0.23387622149837137
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.13485342019543972
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7591280633141436
      name: Cosine Recall@1
    - type: cosine_recall@5
      value: 0.8053443778409052
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8250162100215035
      name: Cosine Recall@10
    - type: cosine_ndcg@1
      value: 0.9609120521172638
      name: Cosine Ndcg@1
    - type: cosine_ndcg@5
      value: 0.8585919595410421
      name: Cosine Ndcg@5
    - type: cosine_ndcg@10
      value: 0.854016214804543
      name: Cosine Ndcg@10
    - type: cosine_mrr@1
      value: 0.9609120521172638
      name: Cosine Mrr@1
    - type: cosine_mrr@5
      value: 0.9769272529858848
      name: Cosine Mrr@5
    - type: cosine_mrr@10
      value: 0.9772529858849075
      name: Cosine Mrr@10
    - type: cosine_map@1
      value: 0.9609120521172638
      name: Cosine Map@1
    - type: cosine_map@5
      value: 0.8187260224393775
      name: Cosine Map@5
    - type: cosine_map@10
      value: 0.808252804922186
      name: Cosine Map@10
---

# 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 [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) 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 84fccfe766bcfd679e39efefe4ebf45af190ad2d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 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: 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/paraphrase-multilingual-mpnet-base-v2-mnrl")
# Run inference
sentences = [
    'Seperti apa rincian inflasi di Indonesia, termasuk inflasi inti dan harga diatur, pada 2020?',
    'Inflasi Umum, Inti, Harga Yang Diatur Pemerintah, dan Barang Bergejolak Inflasi Indonesia, 2009-2024',
    'Angka Kematian Ibu/AKI (Maternal Mortality Rate/MMR) Hasil Long Form SP2020 Menurut Provinsi, 2020',
]
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>
-->

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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.*
-->

## 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   | 0.9609    |
| cosine_accuracy@5   | 0.9967    |
| cosine_accuracy@10  | 1.0       |
| cosine_precision@1  | 0.9609    |
| cosine_precision@5  | 0.2339    |
| cosine_precision@10 | 0.1349    |
| cosine_recall@1     | 0.7591    |
| cosine_recall@5     | 0.8053    |
| cosine_recall@10    | 0.825     |
| cosine_ndcg@1       | 0.9609    |
| cosine_ndcg@5       | 0.8586    |
| **cosine_ndcg@10**  | **0.854** |
| cosine_mrr@1        | 0.9609    |
| cosine_mrr@5        | 0.9769    |
| cosine_mrr@10       | 0.9773    |
| cosine_map@1        | 0.9609    |
| cosine_map@5        | 0.8187    |
| cosine_map@10       | 0.8083    |

<!--
## 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: 18.38 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.39 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.6 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
  | query                                                                                    | pos                                                                                                          | neg                                                                                                                                                                     |
  |:-----------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Neraca arus dana triwulan I tahun 2004 (ringkasan, miliar)</code>                  | <code>Ringkasan Neraca Arus Dana Triwulan I 2004 (Miliar Rupiah)</code>                                      | <code>Proporsi Penduduk Berumur 10 Tahun ke Atas yang Membaca Surat Kabar/Majalah Selama Seminggu Terakhir menurut Provinsi, Tipe Daerah dan Jenis Kelamin, 2012</code> |
  | <code>Kumpulan dokumen Rencana Pengurangan Bencana level kabupaten dan kota</code>       | <code>Rekap Dokumen RPB Kabupaten/Kota</code>                                                                | <code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 1986-1996</code>                  |
  | <code>IHK dan gaji bulanan buruh hotel, di bawah supervisor, 2007=100, tahun 2009</code> | <code>IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2007-2014 (2007=100)</code> | <code>Rata-Rata Bulanan Konsentrasi Partikel Terlarut di Udara Beberapa Kota Menurut Bulan dan Kota (&#956;gr/m3), 2006-2015</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: 4 tokens</li><li>mean: 18.39 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.42 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.6 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | query                                                                                                                 | pos                                                                                                                                                 | neg                                                                                                                 |
  |:----------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
  | <code>Bagaimana hubungan IHK dan rata-rata upah buruh hotel (bukan supervisor), acuan 2012, sekitar tahun 2012</code> | <code>IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 2012-2014 (2012=100)</code>                                        | <code>Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar Harga Berlaku, 2010-2016</code> |
  | <code>Kegiatan mingguan penduduk 15+ (berdasarkan pendidikan terakhir), 1990</code>                                   | <code>Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 1986-1996</code> | <code>Transaksi Total Atas Dasar Harga Dasar, 2010</code>                                                           |
  | <code>Bandingkan indeks harga konsumen (inflasi) di kota-kota Sumatera vs nasional, Desember 2023</code>              | <code>Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Pulau Sumatera dengan Nasional (2018=100)</code>                           | <code>Persentase Penduduk Berumur 15 tahun Ke Atas menurut Jenis Kegiatan Seminggu Yang Lalu, 2009-2012</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
- `fp16`: True
- `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`: 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`: 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        | -             | 0.7057          | 0.5598                            |
| 0.0348     | 100      | 0.2794        | 0.0554          | 0.8089                            |
| 0.0696     | 200      | 0.0545        | 0.0389          | 0.8189                            |
| 0.1044     | 300      | 0.041         | 0.0407          | 0.8194                            |
| 0.1392     | 400      | 0.0381        | 0.0366          | 0.8186                            |
| 0.1740     | 500      | 0.0441        | 0.0283          | 0.8250                            |
| 0.2088     | 600      | 0.0235        | 0.0212          | 0.8405                            |
| 0.2436     | 700      | 0.0216        | 0.0175          | 0.8256                            |
| 0.2784     | 800      | 0.0175        | 0.0119          | 0.8269                            |
| 0.3132     | 900      | 0.0144        | 0.0131          | 0.8086                            |
| 0.3479     | 1000     | 0.008         | 0.0111          | 0.8269                            |
| 0.3827     | 1100     | 0.01          | 0.0106          | 0.8251                            |
| 0.4175     | 1200     | 0.0238        | 0.0138          | 0.8296                            |
| 0.4523     | 1300     | 0.0218        | 0.0074          | 0.8360                            |
| 0.4871     | 1400     | 0.0126        | 0.0077          | 0.8257                            |
| 0.5219     | 1500     | 0.0082        | 0.0101          | 0.8447                            |
| 0.5567     | 1600     | 0.01          | 0.0057          | 0.8513                            |
| 0.5915     | 1700     | 0.0057        | 0.0060          | 0.8500                            |
| 0.6263     | 1800     | 0.0069        | 0.0051          | 0.8522                            |
| 0.6611     | 1900     | 0.0062        | 0.0053          | 0.8477                            |
| 0.6959     | 2000     | 0.0056        | 0.0057          | 0.8541                            |
| 0.7307     | 2100     | 0.0081        | 0.0051          | 0.8492                            |
| 0.7655     | 2200     | 0.0048        | 0.0049          | 0.8455                            |
| 0.8003     | 2300     | 0.004         | 0.0047          | 0.8493                            |
| 0.8351     | 2400     | 0.0068        | 0.0041          | 0.8522                            |
| 0.8699     | 2500     | 0.003         | 0.0036          | 0.8530                            |
| 0.9047     | 2600     | 0.0029        | 0.0035          | 0.8509                            |
| 0.9395     | 2700     | 0.0031        | 0.0035          | 0.8518                            |
| **0.9743** | **2800** | **0.002**     | **0.0034**      | **0.854**                         |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.4.0
- 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",
}
```

#### 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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