| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:967831 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| widget: |
| - source_sentence: Gaji pekerja berdasarkan jenis pekerjaan dan umur, 2016 |
| sentences: |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur |
| dan Jenis Pekerjaan (Rupiah), 2016 |
| - '[Seri 2010] PDRB Triwulanan Atas Dasar Harga Berlaku Menurut Lapangan Usaha di |
| Provinsi Seluruh Indonesia (Miliar Rupiah), 2010-2024' |
| - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi |
| yang Ditamatkan, 2019 |
| - source_sentence: Ke negara mana saja ekspor tanaman obat Indonesia tahun 2018? |
| sentences: |
| - Jumlah Rumah Tangga Perikanan Tangkap Menurut Provinsi dan Jenis Penangkapan, |
| 2000-2016 |
| - Perolehan Suara dan Kursi Dewan Perwakilan Rakyat (DPR) Menurut Partai Politik |
| Hasil Pemilu Tahun 2009 dan 2014 |
| - Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama, |
| 2012-2023 |
| - source_sentence: Negara asal impor soybean 2023 |
| sentences: |
| - Ringkasan Neraca Arus Dana, Triwulan III, 2010, (Miliar Rupiah) |
| - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur |
| (ribu rupiah), 2018 |
| - Impor Kedelai menurut Negara Asal Utama, 2017-2023 |
| - source_sentence: Cek penghasilan bersih rata-rata yang didapat wiraswasta di Indonesia |
| tahun 2021, bedakan per provinsi dan ijazah terakhir |
| sentences: |
| - Rata-rata Pendapatan bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang |
| Ditamatkan, 2021 |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sumatera Selatan, 2018-2023 |
| - Impor Daging Sejenis Lembu menurut Negara Asal Utama, 2018-2023 |
| - source_sentence: Status pernikahan penduduk (10+) tiap provinsi, data 2012 |
| sentences: |
| - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) |
| - Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023 |
| - Persentase Penduduk Berumur 10 Tahun ke Atas menurut Provinsi, Jenis Kelamin, |
| dan Status Perkawinan, 2009-2018 |
| datasets: |
| - yahyaabd/statictable-triplets-all |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: bps statictable ir |
| type: bps-statictable-ir |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.8990228013029316 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.9739413680781759 |
| name: Cosine Accuracy@3 |
| - 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: 0.8990228013029316 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.3517915309446254 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.2299674267100977 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.13420195439739416 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.7037534704802675 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.777408879373005 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.7896378239472596 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.8147874661605627 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.8242104501990923 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.9361834961997827 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7641191235697605 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-2") |
| # Run inference |
| sentences = [ |
| 'Status pernikahan penduduk (10+) tiap provinsi, data 2012', |
| 'Persentase Penduduk Berumur 10 Tahun ke Atas menurut Provinsi, Jenis Kelamin, dan Status Perkawinan, 2009-2018', |
| 'Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities.shape) |
| # [3, 3] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### 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 | 0.899 | |
| | cosine_accuracy@3 | 0.9739 | |
| | cosine_accuracy@5 | 0.9805 | |
| | cosine_accuracy@10 | 0.987 | |
| | cosine_precision@1 | 0.899 | |
| | cosine_precision@3 | 0.3518 | |
| | cosine_precision@5 | 0.23 | |
| | cosine_precision@10 | 0.1342 | |
| | cosine_recall@1 | 0.7038 | |
| | cosine_recall@3 | 0.7774 | |
| | cosine_recall@5 | 0.7896 | |
| | cosine_recall@10 | 0.8148 | |
| | **cosine_ndcg@10** | **0.8242** | |
| | cosine_mrr@10 | 0.9362 | |
| | cosine_map@100 | 0.7641 | |
| |
| <!-- |
| ## 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 [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: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.22 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.78 tokens</li><li>max: 58 tokens</li></ul> | |
| * Samples: |
| | query | pos | neg | |
| |:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Jumlah bank dan kantor bank di Indonesia, 2010-2017</code> | <code>Bank dan Kantor Bank, 2010-2017</code> | <code>Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 1998-2012</code> | |
| | <code>Konsumsi makanan mingguan per orang di Sulteng: beda tingkat pengeluaran (2021)</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Selatan, 2018-2023</code> | <code>IHK, Upah Nominal, Indeks Upah Nominal dan Riil Buruh Industri Berstatus di bawah Mandor Menurut Wilayah, 2008-2014 (2007=100)</code> | |
| | <code>Impor semen Indonesia, negara asal utama, 2021</code> | <code>Impor Semen Menurut Negara Asal Utama, 2017-2023</code> | <code>Penerimaan dari Wisatawan Mancanegara Menurut Negara Tempat Tinggal (juta US$), 2000-2014</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### statictable-triplets-all |
| |
| * Dataset: [statictable-triplets-all](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all) at [24979b4](https://huggingface.co/datasets/yahyaabd/statictable-triplets-all/tree/24979b4f0d8269377aca975e20d52e69c3b5a030) |
| * Size: 967,831 evaluation samples |
| * Columns: <code>query</code>, <code>pos</code>, and <code>neg</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | pos | neg | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 18.39 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.22 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 58 tokens</li></ul> | |
| * Samples: |
| | query | pos | neg | |
| |:----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Bagaimana hubungan antara bidang pekerjaan utama dan pendidikan pekerja 15+ di minggu lalu (tahun 2016)?</code> | <code>Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 2008 - 2024</code> | <code>Bank dan Kantor Bank, 2010-2017</code> | |
| | <code>Tren indikator kondisi perumahan, 2001</code> | <code>Indikator Perumahan 1993-2023</code> | <code>Banyaknya Desa/Kelurahan Menurut Keberadaan Kelompok Pertokoan, Pasar, dan Kios Sarana Produksi Pertanian (Saprotan), 2014 & 2018</code> | |
| | <code>Gaji bersih rata-rata: Per pendidikan & lapangan kerja utama, Indonesia, 2021</code> | <code>Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021</code> | <code>[Seri 2000] Laju Pertumbuhan Kumulatif PDB Menurut Lapangan Usaha (Persen), 2001-2014</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 |
| <details><summary>Click to expand</summary> |
| |
| | Epoch | Step | Training Loss | Validation Loss | bps-statictable-ir_cosine_ndcg@10 | |
| |:----------:|:--------:|:-------------:|:---------------:|:---------------------------------:| |
| | 0 | 0 | - | 1.1084 | 0.4644 | |
| | 0.0070 | 20 | 1.0801 | 0.8303 | 0.5117 | |
| | 0.0139 | 40 | 0.6994 | 0.4459 | 0.6310 | |
| | 0.0209 | 60 | 0.3674 | 0.2510 | 0.7155 | |
| | 0.0278 | 80 | 0.2814 | 0.1829 | 0.7521 | |
| | 0.0348 | 100 | 0.1746 | 0.1303 | 0.7751 | |
| | 0.0418 | 120 | 0.1867 | 0.1001 | 0.7772 | |
| | 0.0487 | 140 | 0.1047 | 0.0819 | 0.7857 | |
| | 0.0557 | 160 | 0.1032 | 0.0739 | 0.7960 | |
| | 0.0626 | 180 | 0.0783 | 0.0645 | 0.7861 | |
| | 0.0696 | 200 | 0.0575 | 0.0567 | 0.7849 | |
| | 0.0765 | 220 | 0.0969 | 0.0454 | 0.7945 | |
| | 0.0835 | 240 | 0.0769 | 0.0433 | 0.7890 | |
| | 0.0905 | 260 | 0.0864 | 0.0507 | 0.7848 | |
| | 0.0974 | 280 | 0.0495 | 0.0347 | 0.8052 | |
| | 0.1044 | 300 | 0.0429 | 0.0398 | 0.7955 | |
| | 0.1113 | 320 | 0.0432 | 0.0343 | 0.7915 | |
| | 0.1183 | 340 | 0.0392 | 0.0295 | 0.8177 | |
| | 0.1253 | 360 | 0.0211 | 0.0298 | 0.8052 | |
| | 0.1322 | 380 | 0.043 | 0.0339 | 0.8052 | |
| | 0.1392 | 400 | 0.0453 | 0.0322 | 0.8050 | |
| | 0.1461 | 420 | 0.0309 | 0.0286 | 0.8120 | |
| | 0.1531 | 440 | 0.0147 | 0.0321 | 0.8181 | |
| | 0.1601 | 460 | 0.0491 | 0.0273 | 0.8178 | |
| | 0.1670 | 480 | 0.0229 | 0.0232 | 0.8176 | |
| | 0.1740 | 500 | 0.0317 | 0.0210 | 0.8198 | |
| | 0.1809 | 520 | 0.0193 | 0.0207 | 0.8159 | |
| | 0.1879 | 540 | 0.034 | 0.0175 | 0.8191 | |
| | 0.1949 | 560 | 0.0292 | 0.0168 | 0.8166 | |
| | 0.2018 | 580 | 0.0431 | 0.0184 | 0.8228 | |
| | 0.2088 | 600 | 0.0306 | 0.0183 | 0.7963 | |
| | 0.2157 | 620 | 0.0134 | 0.0147 | 0.8216 | |
| | 0.2227 | 640 | 0.0155 | 0.0161 | 0.8166 | |
| | 0.2296 | 660 | 0.0201 | 0.0187 | 0.8170 | |
| | 0.2366 | 680 | 0.0301 | 0.0133 | 0.8272 | |
| | 0.2436 | 700 | 0.0164 | 0.0119 | 0.8274 | |
| | 0.2505 | 720 | 0.0254 | 0.0119 | 0.8223 | |
| | 0.2575 | 740 | 0.0129 | 0.0146 | 0.8165 | |
| | 0.2644 | 760 | 0.0208 | 0.0136 | 0.8162 | |
| | 0.2714 | 780 | 0.0157 | 0.0138 | 0.8120 | |
| | 0.2784 | 800 | 0.0169 | 0.0143 | 0.8248 | |
| | 0.2853 | 820 | 0.0158 | 0.0119 | 0.8166 | |
| | 0.2923 | 840 | 0.0227 | 0.0115 | 0.8153 | |
| | 0.2992 | 860 | 0.0196 | 0.0117 | 0.8163 | |
| | 0.3062 | 880 | 0.0137 | 0.0112 | 0.8225 | |
| | 0.3132 | 900 | 0.0299 | 0.0090 | 0.8155 | |
| | 0.3201 | 920 | 0.0073 | 0.0106 | 0.8157 | |
| | 0.3271 | 940 | 0.0248 | 0.0088 | 0.8174 | |
| | 0.3340 | 960 | 0.0179 | 0.0087 | 0.8215 | |
| | 0.3410 | 980 | 0.0171 | 0.0077 | 0.8285 | |
| | 0.3479 | 1000 | 0.0123 | 0.0096 | 0.8175 | |
| | 0.3549 | 1020 | 0.0081 | 0.0098 | 0.8152 | |
| | 0.3619 | 1040 | 0.0097 | 0.0094 | 0.8139 | |
| | 0.3688 | 1060 | 0.0379 | 0.0107 | 0.8236 | |
| | 0.3758 | 1080 | 0.0104 | 0.0078 | 0.8208 | |
| | 0.3827 | 1100 | 0.0067 | 0.0065 | 0.8189 | |
| | 0.3897 | 1120 | 0.0128 | 0.0080 | 0.8221 | |
| | 0.3967 | 1140 | 0.0049 | 0.0078 | 0.8181 | |
| | 0.4036 | 1160 | 0.0084 | 0.0092 | 0.8218 | |
| | 0.4106 | 1180 | 0.0173 | 0.0081 | 0.8248 | |
| | 0.4175 | 1200 | 0.0144 | 0.0080 | 0.8272 | |
| | 0.4245 | 1220 | 0.0025 | 0.0077 | 0.8260 | |
| | 0.4315 | 1240 | 0.0086 | 0.0072 | 0.8312 | |
| | 0.4384 | 1260 | 0.0114 | 0.0073 | 0.8242 | |
| | 0.4454 | 1280 | 0.0065 | 0.0067 | 0.8245 | |
| | 0.4523 | 1300 | 0.0132 | 0.0069 | 0.8248 | |
| | 0.4593 | 1320 | 0.003 | 0.0066 | 0.8233 | |
| | 0.4662 | 1340 | 0.0125 | 0.0066 | 0.8245 | |
| | 0.4732 | 1360 | 0.0016 | 0.0070 | 0.8281 | |
| | 0.4802 | 1380 | 0.0041 | 0.0066 | 0.8418 | |
| | 0.4871 | 1400 | 0.0117 | 0.0073 | 0.8361 | |
| | 0.4941 | 1420 | 0.0095 | 0.0073 | 0.8337 | |
| | 0.5010 | 1440 | 0.0184 | 0.0071 | 0.8282 | |
| | 0.5080 | 1460 | 0.0042 | 0.0069 | 0.8259 | |
| | 0.5150 | 1480 | 0.0077 | 0.0065 | 0.8235 | |
| | 0.5219 | 1500 | 0.0213 | 0.0059 | 0.8209 | |
| | 0.5289 | 1520 | 0.0037 | 0.0059 | 0.8277 | |
| | 0.5358 | 1540 | 0.0053 | 0.0053 | 0.8186 | |
| | 0.5428 | 1560 | 0.0045 | 0.0071 | 0.8238 | |
| | 0.5498 | 1580 | 0.0013 | 0.0101 | 0.8257 | |
| | 0.5567 | 1600 | 0.017 | 0.0051 | 0.8292 | |
| | 0.5637 | 1620 | 0.0053 | 0.0045 | 0.8234 | |
| | 0.5706 | 1640 | 0.0077 | 0.0044 | 0.8235 | |
| | 0.5776 | 1660 | 0.0135 | 0.0046 | 0.8200 | |
| | 0.5846 | 1680 | 0.0013 | 0.0045 | 0.8242 | |
| | 0.5915 | 1700 | 0.0067 | 0.0048 | 0.8266 | |
| | 0.5985 | 1720 | 0.0154 | 0.0049 | 0.8232 | |
| | 0.6054 | 1740 | 0.0037 | 0.0048 | 0.8222 | |
| | 0.6124 | 1760 | 0.0012 | 0.0049 | 0.8232 | |
| | 0.6193 | 1780 | 0.0112 | 0.0051 | 0.8212 | |
| | 0.6263 | 1800 | 0.0173 | 0.0056 | 0.8228 | |
| | 0.6333 | 1820 | 0.0044 | 0.0059 | 0.8177 | |
| | 0.6402 | 1840 | 0.0193 | 0.0059 | 0.8197 | |
| | 0.6472 | 1860 | 0.0028 | 0.0060 | 0.8203 | |
| | 0.6541 | 1880 | 0.005 | 0.0054 | 0.8278 | |
| | 0.6611 | 1900 | 0.0077 | 0.0049 | 0.8227 | |
| | 0.6681 | 1920 | 0.0126 | 0.0040 | 0.8267 | |
| | 0.6750 | 1940 | 0.008 | 0.0039 | 0.8258 | |
| | 0.6820 | 1960 | 0.0131 | 0.0039 | 0.8251 | |
| | 0.6889 | 1980 | 0.0114 | 0.0042 | 0.8310 | |
| | 0.6959 | 2000 | 0.0083 | 0.0041 | 0.8314 | |
| | 0.7029 | 2020 | 0.006 | 0.0037 | 0.8303 | |
| | 0.7098 | 2040 | 0.0048 | 0.0036 | 0.8269 | |
| | 0.7168 | 2060 | 0.0165 | 0.0040 | 0.8262 | |
| | 0.7237 | 2080 | 0.0093 | 0.0035 | 0.8158 | |
| | 0.7307 | 2100 | 0.007 | 0.0031 | 0.8167 | |
| | 0.7376 | 2120 | 0.0065 | 0.0030 | 0.8248 | |
| | 0.7446 | 2140 | 0.0042 | 0.0029 | 0.8274 | |
| | 0.7516 | 2160 | 0.0111 | 0.0026 | 0.8258 | |
| | 0.7585 | 2180 | 0.0066 | 0.0028 | 0.8249 | |
| | 0.7655 | 2200 | 0.0034 | 0.0034 | 0.8244 | |
| | 0.7724 | 2220 | 0.0013 | 0.0033 | 0.8238 | |
| | 0.7794 | 2240 | 0.0025 | 0.0034 | 0.8253 | |
| | 0.7864 | 2260 | 0.0065 | 0.0034 | 0.8240 | |
| | 0.7933 | 2280 | 0.0049 | 0.0035 | 0.8258 | |
| | 0.8003 | 2300 | 0.0007 | 0.0035 | 0.8277 | |
| | 0.8072 | 2320 | 0.004 | 0.0034 | 0.8298 | |
| | 0.8142 | 2340 | 0.0013 | 0.0033 | 0.8293 | |
| | 0.8212 | 2360 | 0.0122 | 0.0032 | 0.8300 | |
| | 0.8281 | 2380 | 0.0008 | 0.0033 | 0.8285 | |
| | 0.8351 | 2400 | 0.0019 | 0.0032 | 0.8266 | |
| | 0.8420 | 2420 | 0.0033 | 0.0032 | 0.8266 | |
| | 0.8490 | 2440 | 0.0078 | 0.0024 | 0.8284 | |
| | 0.8559 | 2460 | 0.0087 | 0.0022 | 0.8272 | |
| | 0.8629 | 2480 | 0.003 | 0.0021 | 0.8255 | |
| | 0.8699 | 2500 | 0.0039 | 0.0021 | 0.8232 | |
| | 0.8768 | 2520 | 0.0054 | 0.0021 | 0.8225 | |
| | **0.8838** | **2540** | **0.0015** | **0.0021** | **0.8236** | |
| | 0.8907 | 2560 | 0.0043 | 0.0021 | 0.8245 | |
| | 0.8977 | 2580 | 0.0083 | 0.0022 | 0.8237 | |
| | 0.9047 | 2600 | 0.0029 | 0.0024 | 0.8233 | |
| | 0.9116 | 2620 | 0.0095 | 0.0025 | 0.8257 | |
| | 0.9186 | 2640 | 0.0013 | 0.0025 | 0.8263 | |
| | 0.9255 | 2660 | 0.0025 | 0.0025 | 0.8268 | |
| | 0.9325 | 2680 | 0.006 | 0.0025 | 0.8264 | |
| | 0.9395 | 2700 | 0.0078 | 0.0026 | 0.8247 | |
| | 0.9464 | 2720 | 0.0061 | 0.0025 | 0.8248 | |
| | 0.9534 | 2740 | 0.001 | 0.0025 | 0.8238 | |
| | 0.9603 | 2760 | 0.0041 | 0.0025 | 0.8233 | |
| | 0.9673 | 2780 | 0.0157 | 0.0024 | 0.8249 | |
| | 0.9743 | 2800 | 0.0039 | 0.0024 | 0.8248 | |
| | 0.9812 | 2820 | 0.0047 | 0.0024 | 0.8242 | |
| | 0.9882 | 2840 | 0.0058 | 0.0024 | 0.8243 | |
| | 0.9951 | 2860 | 0.0018 | 0.0024 | 0.8242 | |
| |
| * The bold row denotes the saved checkpoint. |
| </details> |
| |
| ### 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", |
| } |
| ``` |
| |
| #### 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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