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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:123637
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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
datasets:
- yahyaabd/allstats-semantic-synthetic-dataset-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstats semantic 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",
}
```
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