--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:25580 - loss:OnlineContrastiveLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 widget: - source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar) sentences: - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005 - Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau Jawa dan Sumatera dengan Nasional (2018=100) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023 - source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal kedua tahun 2015? sentences: - Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016 - Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah) - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023 - source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan, per provinsi, 2018? sentences: - Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama, 2012-2023 - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi yang Ditamatkan (ribu rupiah), 2017 - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100) - source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun 2002-2023 sentences: - Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia, 1999, 2002-2023 - Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang Ditamatkan (ribu rupiah), 2016 - Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar Harga Berlaku, 2010-2016 - source_sentence: Arus dana Q3 2006 sentences: - Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik (miliar rupiah), 2005-2018 - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah) - Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012 datasets: - yahyaabd/query-hard-pos-neg-doc-pairs-statictable pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: allstats semantic mini v1 test type: allstats-semantic-mini-v1_test metrics: - type: cosine_accuracy value: 0.9770031027559773 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7470195889472961 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9648633575013944 name: Cosine F1 - type: cosine_f1_threshold value: 0.7452057600021362 name: Cosine F1 Threshold - type: cosine_precision value: 0.9552733296521259 name: Cosine Precision - type: cosine_recall value: 0.9746478873239437 name: Cosine Recall - type: cosine_ap value: 0.9927055758758331 name: Cosine Ap - type: cosine_mcc value: 0.9478797507864009 name: Cosine Mcc - task: type: binary-classification name: Binary Classification dataset: name: allstats semantic mini v1 dev type: allstats-semantic-mini-v1_dev metrics: - type: cosine_accuracy value: 0.9770031027559773 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7470195889472961 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9648633575013944 name: Cosine F1 - type: cosine_f1_threshold value: 0.7452057600021362 name: Cosine F1 Threshold - type: cosine_precision value: 0.9552733296521259 name: Cosine Precision - type: cosine_recall value: 0.9746478873239437 name: Cosine Recall - type: cosine_ap value: 0.9927055758758331 name: Cosine Ap - type: cosine_mcc value: 0.9478797507864009 name: Cosine Mcc --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) ### 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-miniLM-v1-5") # Run inference sentences = [ 'Arus dana Q3 2006', 'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)', 'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Datasets: `allstats-semantic-mini-v1_test` and `allstats-semantic-mini-v1_dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | allstats-semantic-mini-v1_test | allstats-semantic-mini-v1_dev | |:--------------------------|:-------------------------------|:------------------------------| | cosine_accuracy | 0.977 | 0.977 | | cosine_accuracy_threshold | 0.747 | 0.747 | | cosine_f1 | 0.9649 | 0.9649 | | cosine_f1_threshold | 0.7452 | 0.7452 | | cosine_precision | 0.9553 | 0.9553 | | cosine_recall | 0.9746 | 0.9746 | | **cosine_ap** | **0.9927** | **0.9927** | | cosine_mcc | 0.9479 | 0.9479 | ## Training Details ### Training Dataset #### query-hard-pos-neg-doc-pairs-statictable * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) * Size: 25,580 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | doc | label | |:-------------------------------------------------------------------------|:----------------------------------------------|:---------------| | Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020 | Jumlah Penghuni Lapas per Kanwil | 0 | | status pekerjaan utama penduduk usia 15+ yang bekerja, 2020 | Jumlah Penghuni Lapas per Kanwil | 0 | | STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020 | Jumlah Penghuni Lapas per Kanwil | 0 | * Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Evaluation Dataset #### query-hard-pos-neg-doc-pairs-statictable * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) * Size: 5,479 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | query | doc | label | |:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------| | Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014? | Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017 | 0 | | bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014? | Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017 | 0 | | BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014? | Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017 | 0 | * Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 24 - `per_device_eval_batch_size`: 24 - `num_train_epochs`: 2 - `warmup_ratio`: 0.2 - `fp16`: True - `load_best_model_at_end`: True - `eval_on_start`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 24 - `per_device_eval_batch_size`: 24 - `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`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: True - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mini-v1_test_cosine_ap | allstats-semantic-mini-v1_dev_cosine_ap | |:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------:|:---------------------------------------:| | -1 | -1 | - | - | 0.8789 | - | | 0 | 0 | - | 0.7267 | - | 0.8789 | | 0.0188 | 20 | 0.668 | 0.6453 | - | 0.8848 | | 0.0375 | 40 | 0.6117 | 0.4411 | - | 0.9003 | | 0.0563 | 60 | 0.3108 | 0.3592 | - | 0.9130 | | 0.0750 | 80 | 0.3824 | 0.2899 | - | 0.9336 | | 0.0938 | 100 | 0.2118 | 0.2530 | - | 0.9442 | | 0.1126 | 120 | 0.232 | 0.1945 | - | 0.9582 | | 0.1313 | 140 | 0.1233 | 0.1663 | - | 0.9656 | | 0.1501 | 160 | 0.1293 | 0.1655 | - | 0.9654 | | 0.1689 | 180 | 0.0714 | 0.2142 | - | 0.9578 | | 0.1876 | 200 | 0.1198 | 0.1455 | - | 0.9702 | | 0.2064 | 220 | 0.1081 | 0.1258 | - | 0.9766 | | 0.2251 | 240 | 0.0484 | 0.1210 | - | 0.9753 | | 0.2439 | 260 | 0.1463 | 0.1100 | - | 0.9792 | | 0.2627 | 280 | 0.0422 | 0.1228 | - | 0.9777 | | 0.2814 | 300 | 0.1187 | 0.1302 | - | 0.9725 | | 0.3002 | 320 | 0.0635 | 0.1257 | - | 0.9733 | | 0.3189 | 340 | 0.0422 | 0.1125 | - | 0.9736 | | 0.3377 | 360 | 0.0479 | 0.0882 | - | 0.9796 | | 0.3565 | 380 | 0.119 | 0.1319 | - | 0.9697 | | 0.3752 | 400 | 0.099 | 0.1445 | - | 0.9702 | | 0.3940 | 420 | 0.0409 | 0.1434 | - | 0.9706 | | 0.4128 | 440 | 0.1053 | 0.1520 | - | 0.9686 | | 0.4315 | 460 | 0.1035 | 0.1382 | - | 0.9727 | | 0.4503 | 480 | 0.0848 | 0.1150 | - | 0.9789 | | 0.4690 | 500 | 0.0387 | 0.0944 | - | 0.9826 | | 0.4878 | 520 | 0.0097 | 0.1041 | - | 0.9811 | | 0.5066 | 540 | 0.0667 | 0.1041 | - | 0.9783 | | 0.5253 | 560 | 0.1028 | 0.1386 | - | 0.9736 | | 0.5441 | 580 | 0.0543 | 0.1350 | - | 0.9769 | | 0.5629 | 600 | 0.0859 | 0.1254 | - | 0.9776 | | 0.5816 | 620 | 0.0853 | 0.1483 | - | 0.9728 | | 0.6004 | 640 | 0.024 | 0.1159 | - | 0.9781 | | 0.6191 | 660 | 0.0762 | 0.1046 | - | 0.9784 | | 0.6379 | 680 | 0.0433 | 0.1275 | - | 0.9686 | | 0.6567 | 700 | 0.0772 | 0.0592 | - | 0.9882 | | 0.6754 | 720 | 0.0185 | 0.0542 | - | 0.9889 | | 0.6942 | 740 | 0.0376 | 0.1123 | - | 0.9801 | | 0.7129 | 760 | 0.0612 | 0.1002 | - | 0.9817 | | 0.7317 | 780 | 0.0156 | 0.0948 | - | 0.9809 | | 0.7505 | 800 | 0.0474 | 0.0778 | - | 0.9817 | | 0.7692 | 820 | 0.0427 | 0.0824 | - | 0.9828 | | 0.7880 | 840 | 0.0289 | 0.0911 | - | 0.9833 | | 0.8068 | 860 | 0.0175 | 0.0991 | - | 0.9827 | | 0.8255 | 880 | 0.0241 | 0.0951 | - | 0.9824 | | 0.8443 | 900 | 0.0527 | 0.0816 | - | 0.9860 | | 0.8630 | 920 | 0.0535 | 0.0707 | - | 0.9875 | | 0.8818 | 940 | 0.0211 | 0.0767 | - | 0.9868 | | 0.9006 | 960 | 0.013 | 0.0758 | - | 0.9872 | | 0.9193 | 980 | 0.0079 | 0.0781 | - | 0.9848 | | 0.9381 | 1000 | 0.0406 | 0.0820 | - | 0.9845 | | 0.9568 | 1020 | 0.0277 | 0.0685 | - | 0.9874 | | 0.9756 | 1040 | 0.0132 | 0.0760 | - | 0.9859 | | 0.9944 | 1060 | 0.0268 | 0.0881 | - | 0.9833 | | 1.0131 | 1080 | 0.0089 | 0.0772 | - | 0.9857 | | 1.0319 | 1100 | 0.0276 | 0.0773 | - | 0.9850 | | 1.0507 | 1120 | 0.0181 | 0.0729 | - | 0.9860 | | 1.0694 | 1140 | 0.0065 | 0.0683 | - | 0.9867 | | 1.0882 | 1160 | 0.01 | 0.0639 | - | 0.9873 | | 1.1069 | 1180 | 0.0068 | 0.0662 | - | 0.9870 | | 1.1257 | 1200 | 0.0 | 0.0722 | - | 0.9863 | | 1.1445 | 1220 | 0.0067 | 0.0710 | - | 0.9866 | | 1.1632 | 1240 | 0.0069 | 0.0666 | - | 0.9877 | | 1.1820 | 1260 | 0.0 | 0.0639 | - | 0.9880 | | 1.2008 | 1280 | 0.0244 | 0.0610 | - | 0.9882 | | 1.2195 | 1300 | 0.0143 | 0.0630 | - | 0.9877 | | 1.2383 | 1320 | 0.0173 | 0.0530 | - | 0.9896 | | 1.2570 | 1340 | 0.0171 | 0.0496 | - | 0.9907 | | 1.2758 | 1360 | 0.0225 | 0.0521 | - | 0.9909 | | 1.2946 | 1380 | 0.011 | 0.0569 | - | 0.9900 | | 1.3133 | 1400 | 0.0088 | 0.0605 | - | 0.9898 | | 1.3321 | 1420 | 0.0 | 0.0619 | - | 0.9897 | | 1.3508 | 1440 | 0.0135 | 0.0608 | - | 0.9894 | | 1.3696 | 1460 | 0.0 | 0.0593 | - | 0.9892 | | 1.3884 | 1480 | 0.0145 | 0.0578 | - | 0.9894 | | 1.4071 | 1500 | 0.0 | 0.0608 | - | 0.9896 | | 1.4259 | 1520 | 0.0069 | 0.0567 | - | 0.9906 | | 1.4447 | 1540 | 0.0 | 0.0561 | - | 0.9907 | | 1.4634 | 1560 | 0.0224 | 0.0531 | - | 0.9912 | | 1.4822 | 1580 | 0.0 | 0.0523 | - | 0.9911 | | 1.5009 | 1600 | 0.0066 | 0.0503 | - | 0.9912 | | 1.5197 | 1620 | 0.0 | 0.0472 | - | 0.9915 | | 1.5385 | 1640 | 0.018 | 0.0452 | - | 0.9923 | | 1.5572 | 1660 | 0.0117 | 0.0449 | - | 0.9925 | | 1.5760 | 1680 | 0.0 | 0.0456 | - | 0.9925 | | 1.5947 | 1700 | 0.0 | 0.0448 | - | 0.9925 | | 1.6135 | 1720 | 0.0 | 0.0448 | - | 0.9925 | | 1.6323 | 1740 | 0.0072 | 0.0458 | - | 0.9924 | | 1.6510 | 1760 | 0.0 | 0.0456 | - | 0.9923 | | 1.6698 | 1780 | 0.0163 | 0.0482 | - | 0.9925 | | 1.6886 | 1800 | 0.0063 | 0.0463 | - | 0.9926 | | 1.7073 | 1820 | 0.0078 | 0.0482 | - | 0.9925 | | 1.7261 | 1840 | 0.0179 | 0.0472 | - | 0.9927 | | 1.7448 | 1860 | 0.0 | 0.0477 | - | 0.9927 | | 1.7636 | 1880 | 0.0 | 0.0477 | - | 0.9927 | | 1.7824 | 1900 | 0.0065 | 0.0461 | - | 0.9926 | | 1.8011 | 1920 | 0.0077 | 0.0458 | - | 0.9926 | | 1.8199 | 1940 | 0.0065 | 0.0453 | - | 0.9927 | | 1.8386 | 1960 | 0.0 | 0.0451 | - | 0.9927 | | 1.8574 | 1980 | 0.0 | 0.0451 | - | 0.9927 | | 1.8762 | 2000 | 0.0 | 0.0451 | - | 0.9927 | | 1.8949 | 2020 | 0.0 | 0.0451 | - | 0.9927 | | 1.9137 | 2040 | 0.0 | 0.0451 | - | 0.9927 | | 1.9325 | 2060 | 0.0 | 0.0451 | - | 0.9927 | | 1.9512 | 2080 | 0.0 | 0.0451 | - | 0.9927 | | 1.9700 | 2100 | 0.007 | 0.0442 | - | 0.9927 | | **1.9887** | **2120** | **0.0067** | **0.0441** | **-** | **0.9927** | | -1 | -1 | - | - | 0.9927 | - | * 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", } ```