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bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2-mnrl-2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10998
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10998
- loss:MultipleNegativesRankingLoss
base_model: yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: Laporan neraca arus dana dalam Rupiah miliar untuk Q2 2011
sentences:
- Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah)
- Tingkat Inflasi Harga Konsumen Nasional Tahun Kalender (Y-to-D) 1 (2022=100)
- Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah),
2000, 2005, dan 2008
- source_sentence: Pinjaman bank umum Rp valas dan BPR Rp ke pihak nonbank data historis
sampai 2022
sentences:
- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
yang Ditamatkan dan Jenis Pekerjaan Utama, 2023
- Posisi Kredit/Pembiayaan (Rupiah dan Valuta Asing) pada Bank Umum Menurut Kelompok
Bank dan BPR/BPR Syariah (Rupiah) kepada Pihak Bukan Bank (miliar rupiah), 2014-2023
- Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Barat, 2018-2023
- source_sentence: Infant mortality rate Indonesia per kabupaten/kota tahun 2020
sentences:
- Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang
Ditamatkan, 2023
- Rekapitulasi Luas Penutupan Lahan Hutan dan Non Hutan Menurut Provinsi Tahun 2014-2022
(Ribu Ha)
- Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut
Provinsi/Kabupaten/Kota, 2020
- source_sentence: Berapa pendapatan bulanan ratarata pengusaha di Indonesia Data
per lokasi bedah sektor 2024
sentences:
- Bongkar Muat Barang Antar Pulau dan Luar Negeri di Pelabuhan Indonesia Tahun 1988-2022
(Ribu ton)
- Persentase Rumah Tangga Menurut Provinsi dan Kepemilikan Kendaraan Bermotor, 2013-2014
- Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan
Utama, 2024
- source_sentence: Informasi lengkap dan terbaru mengenai statistik edukasi
sentences:
- Struktur Ongkos Riil Usaha Ternak dan Unggas di Rumah Tangga dengan Pola Pemeliharaan
Dikandangkan, 2017
- Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur
(ribu rupiah), 2018
- Statistik Pendidikan Tahunan
datasets:
- yahyaabd/statictable-triplets-all
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on yahyaabd/bps-custom-tokenizer-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: 0.8599348534201955
name: Cosine Accuracy@1
- type: cosine_accuracy@5
value: 0.9804560260586319
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9869706840390879
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8599348534201955
name: Cosine Precision@1
- type: cosine_precision@5
value: 0.23257328990228016
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13908794788273618
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6663188638274197
name: Cosine Recall@1
- type: cosine_recall@5
value: 0.7919003338686821
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8157207271760252
name: Cosine Recall@10
- type: cosine_ndcg@1
value: 0.8599348534201955
name: Cosine Ndcg@1
- type: cosine_ndcg@5
value: 0.8115433718814501
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.8115549383345654
name: Cosine Ndcg@10
- type: cosine_mrr@1
value: 0.8599348534201955
name: Cosine Mrr@1
- type: cosine_mrr@5
value: 0.9118892508143324
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.9127940644227288
name: Cosine Mrr@10
- type: cosine_map@1
value: 0.8599348534201955
name: Cosine Map@1
- type: cosine_map@5
value: 0.7617137169743033
name: Cosine Map@5
- type: cosine_map@10
value: 0.7557582338038363
name: Cosine Map@10
---
# SentenceTransformer based on yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/yahyaabd/bps-custom-tokenizer-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:** [yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 0b56606d410c6cfc4fec402a37ebdea0ffe8bc86 -->
- **Maximum Sequence Length:** 512 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': 512, '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/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2-mnrl-2")
# Run inference
sentences = [
'Informasi lengkap dan terbaru mengenai statistik edukasi',
'Statistik Pendidikan Tahunan',
'Struktur Ongkos Riil Usaha Ternak dan Unggas di Rumah Tangga dengan Pola Pemeliharaan Dikandangkan, 2017',
]
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
#### 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 | 0.8599 |
| cosine_accuracy@5 | 0.9805 |
| cosine_accuracy@10 | 0.987 |
| cosine_precision@1 | 0.8599 |
| cosine_precision@5 | 0.2326 |
| cosine_precision@10 | 0.1391 |
| cosine_recall@1 | 0.6663 |
| cosine_recall@5 | 0.7919 |
| cosine_recall@10 | 0.8157 |
| cosine_ndcg@1 | 0.8599 |
| cosine_ndcg@5 | 0.8115 |
| **cosine_ndcg@10** | **0.8116** |
| cosine_mrr@1 | 0.8599 |
| cosine_mrr@5 | 0.9119 |
| cosine_mrr@10 | 0.9128 |
| cosine_map@1 | 0.8599 |
| cosine_map@5 | 0.7617 |
| cosine_map@10 | 0.7558 |
<!--
## 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
#### statictable-triplets-all
* Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [0ef226c](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/0ef226c8655599b2aeadf57fd488786e3b47f7a1)
* Size: 10,998 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.25 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 25.66 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.64 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
| <code>Neraca arus kas triwulan II 2005 (ringkasan, )</code> | <code>Ringkasan Neraca Arus Dana, Triwulan Kedua, 2005, (Miliar Rupiah)</code> | <code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Jenis Pekerjaan Utama (Rupiah), 2017</code> |
| <code>Hasil tangkapan ikan per provinsi, bedakan jenis penangkapan, 2013</code> | <code>Produksi Perikanan Tangkap Menurut Provinsi dan Jenis Penangkapan, 2000-2020</code> | <code>Ringkasan Neraca Arus Dana, Triwulan II, 2006, (Miliar Rupiah)</code> |
| <code>Bagaimana perubahan distribusi pengeluaran?</code> | <code>Persentase Perkembangan Distribusi Pengeluaran</code> | <code>Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020</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 [0ef226c](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/0ef226c8655599b2aeadf57fd488786e3b47f7a1)
* Size: 10,998 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 17.25 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.44 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 25.23 tokens</li><li>max: 58 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Data total penghasilan berbagai golongan rumah tangga setelah dipotong pajak, tahun 2000 (dalam )</code> | <code>Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008</code> | <code>Indeks Harga Konsumen per Kelompok di 82 Kota 1 (2012=100)</code> |
| <code>Bagaimana perkembangan impor barang modal pada tahun 2020</code> | <code>Impor Barang Modal, 1996-2023</code> | <code>Indeks Harga yang Diterima Petani (It), Indes Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani Subsektor Hortikultura (NTPH) di Indonesia (2007=100), 2008-2016</code> |
| <code>Konsumsi makanan per orang di Kalut: data mingguan, beda kelompok pengeluaran (2018)</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Kalimantan Utara, 2018-2023</code> | <code>Ekspor Kimia Dasar Organik yang Bersumber dari Hasil Pertanian 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`: 32
- `per_device_eval_batch_size`: 32
- `weight_decay`: 0.01
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 0.01
- `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`: 0.1
- `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}
- `tp_size`: 0
- `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
- `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 | - | 2.2208 | 0.3476 |
| 0.0645 | 20 | 1.7839 | 0.9899 | 0.5271 |
| 0.1290 | 40 | 0.7326 | 0.4401 | 0.7019 |
| 0.1935 | 60 | 0.3811 | 0.2612 | 0.7584 |
| 0.2581 | 80 | 0.2068 | 0.2111 | 0.7612 |
| 0.3226 | 100 | 0.2206 | 0.1526 | 0.7748 |
| 0.3871 | 120 | 0.1547 | 0.1065 | 0.7934 |
| 0.4516 | 140 | 0.1196 | 0.0895 | 0.7880 |
| 0.5161 | 160 | 0.1107 | 0.0821 | 0.8045 |
| 0.5806 | 180 | 0.1253 | 0.0737 | 0.7828 |
| 0.6452 | 200 | 0.0915 | 0.0636 | 0.8081 |
| 0.7097 | 220 | 0.0592 | 0.0555 | 0.8140 |
| 0.7742 | 240 | 0.055 | 0.0535 | 0.7992 |
| 0.8387 | 260 | 0.0531 | 0.0487 | 0.8005 |
| 0.9032 | 280 | 0.0626 | 0.0429 | 0.8035 |
| 0.9677 | 300 | 0.0406 | 0.0407 | 0.8033 |
| 1.0323 | 320 | 0.034 | 0.0430 | 0.8058 |
| 1.0968 | 340 | 0.0327 | 0.0392 | 0.8070 |
| 1.1613 | 360 | 0.0385 | 0.0425 | 0.8006 |
| 1.2258 | 380 | 0.0233 | 0.0347 | 0.8053 |
| 1.2903 | 400 | 0.027 | 0.0339 | 0.8111 |
| 1.3548 | 420 | 0.0323 | 0.0300 | 0.8046 |
| 1.4194 | 440 | 0.0308 | 0.0262 | 0.8126 |
| 1.4839 | 460 | 0.0343 | 0.0277 | 0.7961 |
| 1.5484 | 480 | 0.0192 | 0.0232 | 0.8080 |
| 1.6129 | 500 | 0.0248 | 0.0248 | 0.8057 |
| 1.6774 | 520 | 0.0178 | 0.0250 | 0.8062 |
| 1.7419 | 540 | 0.0158 | 0.0228 | 0.8096 |
| 1.8065 | 560 | 0.0171 | 0.0233 | 0.8073 |
| 1.8710 | 580 | 0.0204 | 0.0218 | 0.8178 |
| 1.9355 | 600 | 0.0261 | 0.0214 | 0.8204 |
| 2.0 | 620 | 0.0132 | 0.0215 | 0.8166 |
| 2.0645 | 640 | 0.0174 | 0.0189 | 0.8169 |
| 2.1290 | 660 | 0.0095 | 0.0185 | 0.8202 |
| 2.1935 | 680 | 0.0186 | 0.0173 | 0.8173 |
| 2.2581 | 700 | 0.0241 | 0.0168 | 0.8174 |
| 2.3226 | 720 | 0.0152 | 0.0158 | 0.8163 |
| 2.3871 | 740 | 0.0197 | 0.0158 | 0.8128 |
| 2.4516 | 760 | 0.0119 | 0.0156 | 0.8122 |
| 2.5161 | 780 | 0.0128 | 0.0151 | 0.8118 |
| 2.5806 | 800 | 0.0162 | 0.0148 | 0.8114 |
| 2.6452 | 820 | 0.011 | 0.0143 | 0.8117 |
| 2.7097 | 840 | 0.0098 | 0.0138 | 0.8128 |
| 2.7742 | 860 | 0.0092 | 0.0135 | 0.8111 |
| 2.8387 | 880 | 0.0102 | 0.0127 | 0.8109 |
| 2.9032 | 900 | 0.0118 | 0.0126 | 0.8115 |
| **2.9677** | **920** | **0.0128** | **0.0126** | **0.8116** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.2.0
- Tokenizers: 0.21.1
## 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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