--- 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) - **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) ### 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `bps-statictable-ir` * Evaluated with [InformationRetrievalEvaluator](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 | ## 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: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------| | Jumlah bank dan kantor bank di Indonesia, 2010-2017 | Bank dan Kantor Bank, 2010-2017 | Rata-Rata Pengeluaran per Kapita Sebulan Menurut Kelompok Barang (rupiah), 1998-2012 | | Konsumsi makanan mingguan per orang di Sulteng: beda tingkat pengeluaran (2021) | Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Selatan, 2018-2023 | IHK, Upah Nominal, Indeks Upah Nominal dan Riil Buruh Industri Berstatus di bawah Mandor Menurut Wilayah, 2008-2014 (2007=100) | | Impor semen Indonesia, negara asal utama, 2021 | Impor Semen Menurut Negara Asal Utama, 2017-2023 | Penerimaan dari Wisatawan Mancanegara Menurut Negara Tempat Tinggal (juta US$), 2000-2014 | * Loss: [MultipleNegativesRankingLoss](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: query, pos, and neg * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | pos | neg | |:----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------| | Bagaimana hubungan antara bidang pekerjaan utama dan pendidikan pekerja 15+ di minggu lalu (tahun 2016)? | Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan, 2008 - 2024 | Bank dan Kantor Bank, 2010-2017 | | Tren indikator kondisi perumahan, 2001 | Indikator Perumahan 1993-2023 | Banyaknya Desa/Kelurahan Menurut Keberadaan Kelompok Pertokoan, Pasar, dan Kios Sarana Produksi Pertanian (Saprotan), 2014 & 2018 | | Gaji bersih rata-rata: Per pendidikan & lapangan kerja utama, Indonesia, 2021 | Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan Lapangan Pekerjaan Utama, 2021 | [Seri 2000] Laju Pertumbuhan Kumulatif PDB Menurut Lapangan Usaha (Persen), 2001-2014 | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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
### Training Logs
Click to expand | 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.
### 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} } ```