| --- |
| 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) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c --> |
| - **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) |
| <!-- - **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-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] |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Binary Classification |
|
|
| * Datasets: `allstats-semantic-mini-v1_test` and `allstats-semantic-mini-v1_dev` |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](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 | |
| |
| <!-- |
| ## 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 |
| |
| #### 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: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.9 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~70.80%</li><li>1: ~29.20%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-------------------------------------------------------------------------|:----------------------------------------------|:---------------| |
| | <code>Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| | <code>status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| | <code>STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](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: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 20.78 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.28 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~71.50%</li><li>1: ~28.50%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| | <code>bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| | <code>BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](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 |
| <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`: 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 |
| |
| </details> |
| |
| ### Training Logs |
| <details><summary>Click to expand</summary> |
| |
| | 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. |
| </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", |
| } |
| ``` |
|
|
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