sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:25580 loss:OnlineContrastiveLoss dataset:yahyaabd/query-hard-pos-neg-doc-pairs-statictable arxiv:1908.10084 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/query-hard-pos-neg-doc-pairs-statictable
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:25580
- loss:OnlineContrastiveLoss
widget:
- source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar)
  sentences:
  - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005
  - Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau
    Jawa dan Sumatera dengan Nasional (2018=100)
  - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
    dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023
- source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal
    kedua tahun 2015?
  sentences:
  - Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian
    Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016
  - Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah)
  - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
    dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023
- source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan,
    per provinsi, 2018?
  sentences:
  - Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama,
    2012-2023
  - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi
    yang Ditamatkan (ribu rupiah), 2017
  - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor),
    1996-2014 (1996=100)
- source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun
    2002-2023
  sentences:
  - Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia,
    1999, 2002-2023
  - Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang
    Ditamatkan (ribu rupiah), 2016
  - Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar
    Harga Berlaku, 2010-2016
- source_sentence: Arus dana Q3 2006
  sentences:
  - Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik
    (miliar rupiah), 2005-2018
  - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)
  - Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok
    Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstats semantic mini v1 eval
      type: allstats-semantic-mini-v1-eval
    metrics:
    - type: pearson_cosine
      value: 0.8664940363669927
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8063420000992144
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: allstat search mini v1 test
      type: allstat-search-mini-v1-test
    metrics:
    - type: pearson_cosine
      value: 0.877199276521204
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.809551340542674
      name: Spearman Cosine
---

# 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 [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) 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 8d6b950845285729817bf8e1af1861502c2fed0c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable)
<!-- - **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/allstats-search-multilingual-miniLM-v1")
# Run inference
sentences = [
    'Arus dana Q3 2006',
    'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)',
    'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012',
]
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>
-->

<!--
### 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-mini-v1-eval` and `allstat-search-mini-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | allstats-semantic-mini-v1-eval | allstat-search-mini-v1-test |
|:--------------------|:-------------------------------|:----------------------------|
| pearson_cosine      | 0.8665                         | 0.8772                      |
| **spearman_cosine** | **0.8063**                     | **0.8096**                  |

<!--
## 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

#### query-hard-pos-neg-doc-pairs-statictable

* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f)
* Size: 25,580 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                                                                           | int                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.9 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~70.80%</li><li>1: ~29.20%</li></ul> |
* Samples:
  | query                                                                    | doc                                           | label          |
  |:-------------------------------------------------------------------------|:----------------------------------------------|:---------------|
  | <code>Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> |
  | <code>status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> |
  | <code>STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### query-hard-pos-neg-doc-pairs-statictable

* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f)
* Size: 5,479 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                                                                            | int                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 20.78 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.28 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~71.50%</li><li>1: ~28.50%</li></ul> |
* Samples:
  | query                                                                                    | doc                                                                                                                         | label          |
  |:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> |
  | <code>bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> |
  | <code>BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `warmup_ratio`: 0.05
- `fp16`: True
- `load_best_model_at_end`: True
- `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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step     | Training Loss | Validation Loss | allstats-semantic-mini-v1-eval_spearman_cosine | allstat-search-mini-v1-test_spearman_cosine |
|:-------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:-------------------------------------------:|
| 0       | 0        | -             | 2.3102          | 0.6855                                         | -                                           |
| 0.05    | 20       | 1.7642        | 1.2458          | 0.7253                                         | -                                           |
| 0.1     | 40       | 0.9637        | 0.6870          | 0.7751                                         | -                                           |
| 0.15    | 60       | 0.4319        | 0.4890          | 0.7897                                         | -                                           |
| 0.2     | 80       | 0.3251        | 0.4944          | 0.7899                                         | -                                           |
| 0.25    | 100      | 0.2665        | 0.3988          | 0.7954                                         | -                                           |
| 0.3     | 120      | 0.1938        | 0.3795          | 0.7972                                         | -                                           |
| 0.35    | 140      | 0.1495        | 0.2839          | 0.8014                                         | -                                           |
| 0.4     | 160      | 0.0681        | 0.3011          | 0.8021                                         | -                                           |
| 0.45    | 180      | 0.1775        | 0.3116          | 0.8004                                         | -                                           |
| 0.5     | 200      | 0.0829        | 0.2536          | 0.8028                                         | -                                           |
| 0.55    | 220      | 0.2332        | 0.2887          | 0.8015                                         | -                                           |
| 0.6     | 240      | 0.1171        | 0.2862          | 0.8021                                         | -                                           |
| 0.65    | 260      | 0.1059        | 0.2467          | 0.8023                                         | -                                           |
| 0.7     | 280      | 0.1089        | 0.2240          | 0.8033                                         | -                                           |
| 0.75    | 300      | 0.0445        | 0.1772          | 0.8048                                         | -                                           |
| 0.8     | 320      | 0.0633        | 0.2392          | 0.8030                                         | -                                           |
| 0.85    | 340      | 0.0506        | 0.2440          | 0.8027                                         | -                                           |
| 0.9     | 360      | 0.1086        | 0.1926          | 0.8054                                         | -                                           |
| 0.95    | 380      | 0.064         | 0.2984          | 0.8025                                         | -                                           |
| 1.0     | 400      | 0.0478        | 0.2764          | 0.8025                                         | -                                           |
| 1.05    | 420      | 0.0508        | 0.2393          | 0.8038                                         | -                                           |
| 1.1     | 440      | 0.0266        | 0.2295          | 0.8039                                         | -                                           |
| 1.15    | 460      | 0.0236        | 0.2477          | 0.8032                                         | -                                           |
| 1.2     | 480      | 0.0142        | 0.2077          | 0.8045                                         | -                                           |
| 1.25    | 500      | 0.0128        | 0.1972          | 0.8047                                         | -                                           |
| 1.3     | 520      | 0.0205        | 0.2116          | 0.8042                                         | -                                           |
| 1.35    | 540      | 0.0447        | 0.2425          | 0.8033                                         | -                                           |
| 1.4     | 560      | 0.0           | 0.1999          | 0.8045                                         | -                                           |
| 1.45    | 580      | 0.0284        | 0.1989          | 0.8046                                         | -                                           |
| 1.5     | 600      | 0.0222        | 0.1789          | 0.8049                                         | -                                           |
| 1.55    | 620      | 0.0066        | 0.1957          | 0.8045                                         | -                                           |
| 1.6     | 640      | 0.0187        | 0.1993          | 0.8046                                         | -                                           |
| 1.65    | 660      | 0.0489        | 0.1901          | 0.8051                                         | -                                           |
| 1.7     | 680      | 0.0236        | 0.1556          | 0.8058                                         | -                                           |
| 1.75    | 700      | 0.0186        | 0.1597          | 0.8059                                         | -                                           |
| 1.8     | 720      | 0.0475        | 0.1813          | 0.8053                                         | -                                           |
| 1.85    | 740      | 0.0215        | 0.1689          | 0.8060                                         | -                                           |
| 1.9     | 760      | 0.0066        | 0.1746          | 0.8057                                         | -                                           |
| 1.95    | 780      | 0.0158        | 0.1808          | 0.8054                                         | -                                           |
| 2.0     | 800      | 0.0412        | 0.1799          | 0.8050                                         | -                                           |
| 2.05    | 820      | 0.0           | 0.1809          | 0.8049                                         | -                                           |
| 2.1     | 840      | 0.0072        | 0.1519          | 0.8059                                         | -                                           |
| 2.15    | 860      | 0.032         | 0.1538          | 0.8060                                         | -                                           |
| 2.2     | 880      | 0.0           | 0.1605          | 0.8058                                         | -                                           |
| 2.25    | 900      | 0.016         | 0.1812          | 0.8053                                         | -                                           |
| 2.3     | 920      | 0.0216        | 0.1550          | 0.8060                                         | -                                           |
| 2.35    | 940      | 0.0124        | 0.1533          | 0.8062                                         | -                                           |
| 2.4     | 960      | 0.0087        | 0.1499          | 0.8064                                         | -                                           |
| 2.45    | 980      | 0.0           | 0.1493          | 0.8064                                         | -                                           |
| **2.5** | **1000** | **0.0063**    | **0.1483**      | **0.8063**                                     | **-**                                       |
| 2.55    | 1020     | 0.0           | 0.1505          | 0.8063                                         | -                                           |
| 2.6     | 1040     | 0.0           | 0.1508          | 0.8063                                         | -                                           |
| 2.65    | 1060     | 0.0           | 0.1508          | 0.8063                                         | -                                           |
| 2.7     | 1080     | 0.0           | 0.1508          | 0.8063                                         | -                                           |
| 2.75    | 1100     | 0.0191        | 0.1546          | 0.8062                                         | -                                           |
| 2.8     | 1120     | 0.0073        | 0.1566          | 0.8063                                         | -                                           |
| 2.85    | 1140     | 0.0095        | 0.1529          | 0.8063                                         | -                                           |
| 2.9     | 1160     | 0.0065        | 0.1512          | 0.8064                                         | -                                           |
| 2.95    | 1180     | 0.0           | 0.1508          | 0.8063                                         | -                                           |
| 3.0     | 1200     | 0.0           | 0.1508          | 0.8063                                         | -                                           |
| -1      | -1       | -             | -               | -                                              | 0.8096                                      |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- 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.*
-->