| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:110773 |
| - loss:ContrastiveLoss |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| widget: |
| - source_sentence: average monthly net wage/salary, employees, by province and occupation |
| (rupiah), 2018 |
| sentences: |
| - '[Seri 2000] Laju Pertumbuhan PDB Triwulanan Atas Dasar Harga Konstan 2000 Terhadap |
| Triwulan Sebelumnya, 2001-2014' |
| - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), |
| 2012-2014 (2012=100) |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur |
| dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017 |
| - source_sentence: 'data belanja dan konsumsi per orang di jambi, 2020: fokus pada |
| makanan dan tingkat pengeluaran' |
| sentences: |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023 |
| - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi |
| yang Ditamatkan (ribu rupiah), 2017 |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023 |
| - source_sentence: 'ALIRAN DANA RUPIAH: Q1 2008' |
| sentences: |
| - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65) |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan |
| Jenis Pekerjaan Utama, 2024 |
| - Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023 |
| - source_sentence: 'Aliran Wdana Rupiah: Q1 2008' |
| sentences: |
| - Ekspor Karet Remah Menurut Negara Tujuan Utama, 2012-2023 |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur |
| dan Lapangan Pekerjaan Utama di 17 Sektor (Rupiah), 2018 |
| - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65) |
| - source_sentence: 'Aliran dana Rupiah: Q1 2008' |
| sentences: |
| - Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah) |
| - Ringkasan Neraca Arus Dana, 2012 (Miliar Rupiah) |
| - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), |
| 2012-2014 (2012=100) |
| datasets: |
| - yahyaabd/query-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: 0.9678628590683177 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.7482147812843323 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.9677936769237264 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.7444144487380981 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.9595714405290031 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.976158038147139 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.9921512853632306 |
| name: Cosine Ap |
| - type: cosine_mcc |
| value: 0.9358669477790009 |
| 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: 0.9678491772924294 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.7902499437332153 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.9673587968896863 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.7874833345413208 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.9616887529731566 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.9730960976448341 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.9930288231258318 |
| name: Cosine Ap |
| - type: cosine_mcc |
| value: 0.9357491510325107 |
| 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-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-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-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-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-v1-7") |
| # Run inference |
| sentences = [ |
| 'Aliran dana Rupiah: Q1 2008', |
| 'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 2012-2014 (2012=100)', |
| 'Ringkasan Neraca Arus Dana, 2012 (Miliar Rupiah)', |
| ] |
| 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 | 0.9679 | 0.9678 | |
| | cosine_accuracy_threshold | 0.7482 | 0.7902 | |
| | cosine_f1 | 0.9678 | 0.9674 | |
| | cosine_f1_threshold | 0.7444 | 0.7875 | |
| | cosine_precision | 0.9596 | 0.9617 | |
| | cosine_recall | 0.9762 | 0.9731 | |
| | **cosine_ap** | **0.9922** | **0.993** | |
| | cosine_mcc | 0.9359 | 0.9357 | |
| |
| <!-- |
| ## 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-pos-neg-doc-pairs-statictable |
| |
| * Dataset: [query-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-pos-neg-doc-pairs-statictable) at [a31b58d](https://huggingface.co/datasets/yahyaabd/query-pos-neg-doc-pairs-statictable/tree/a31b58d221edcddb16274a04b2fafe56df68801a) |
| * Size: 110,773 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: 9 tokens</li><li>mean: 21.22 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 28.24 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>0: ~43.90%</li><li>1: ~56.10%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Data orang yang naik/turun kapal, di pelabuhan yang dikelola maupun tidak, sekitar 2015</code> | <code>Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)</code> | <code>0</code> | |
| | <code>data orang yang naik/turun kapal, di pelabuhan yang dikelola maupun tidak, sekitar 2015</code> | <code>Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)</code> | <code>0</code> | |
| | <code>DATA ORANG YANG NAIK/TURUN KAPAL, DI PELABUHAN YANG DIKELOLA MAUPUN TIDAK, SEKITAR 2015</code> | <code>Tabel Input-Output Indonesia Transaksi Total Atas Dasar Harga Dasar (185 Produk), 2016 (Juta Rupiah)</code> | <code>0</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### query-pos-neg-doc-pairs-statictable |
| |
| * Dataset: [query-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-pos-neg-doc-pairs-statictable) at [a31b58d](https://huggingface.co/datasets/yahyaabd/query-pos-neg-doc-pairs-statictable/tree/a31b58d221edcddb16274a04b2fafe56df68801a) |
| * Size: 23,763 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: 20.75 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 27.44 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~50.20%</li><li>1: ~49.80%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Cek penghasilan bulanan (gaji bersih) buruh/pegawai, per provinsi dan jenis pekerjaannya, 2019</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021</code> | <code>1</code> | |
| | <code>cek penghasilan bulanan (gaji bersih) buruh/pegawai, per provinsi dan jenis pekerjaannya, 2019</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021</code> | <code>1</code> | |
| | <code>CEK PENGHASILAN BULANAN (GAJI BERSIH) BURUH/PEGAWAI, PER PROVINSI DAN JENIS PEKERJAANNYA, 2019</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama, 2021</code> | <code>1</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `num_train_epochs`: 1 |
| - `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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 0.2 |
| - `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 |
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mini-v1_test_cosine_ap | allstats-semantic-mini-v1_dev_cosine_ap | |
| |:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------:|:---------------------------------------:| |
| | -1 | -1 | - | - | 0.8699 | - | |
| | 0 | 0 | - | 0.0489 | - | 0.8658 | |
| | 0.0578 | 100 | 0.0222 | 0.0101 | - | 0.9458 | |
| | 0.1155 | 200 | 0.0087 | 0.0073 | - | 0.9631 | |
| | 0.1733 | 300 | 0.007 | 0.0059 | - | 0.9710 | |
| | 0.2311 | 400 | 0.0056 | 0.0049 | - | 0.9828 | |
| | 0.2889 | 500 | 0.0045 | 0.0044 | - | 0.9837 | |
| | 0.3466 | 600 | 0.0042 | 0.0041 | - | 0.9862 | |
| | 0.4044 | 700 | 0.0038 | 0.0038 | - | 0.9888 | |
| | 0.4622 | 800 | 0.0037 | 0.0037 | - | 0.9890 | |
| | 0.5199 | 900 | 0.0029 | 0.0036 | - | 0.9889 | |
| | 0.5777 | 1000 | 0.0031 | 0.0034 | - | 0.9907 | |
| | 0.6355 | 1100 | 0.0029 | 0.0033 | - | 0.9923 | |
| | 0.6932 | 1200 | 0.0025 | 0.0034 | - | 0.9922 | |
| | 0.7510 | 1300 | 0.0025 | 0.0033 | - | 0.9929 | |
| | 0.8088 | 1400 | 0.0024 | 0.0033 | - | 0.9928 | |
| | 0.8666 | 1500 | 0.0022 | 0.0033 | - | 0.9926 | |
| | 0.9243 | 1600 | 0.0023 | 0.0033 | - | 0.9929 | |
| | **0.9821** | **1700** | **0.0022** | **0.0032** | **-** | **0.993** | |
| | -1 | -1 | - | - | 0.9922 | - | |
|
|
| * 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", |
| } |
| ``` |
|
|
| #### ContrastiveLoss |
| ```bibtex |
| @inproceedings{hadsell2006dimensionality, |
| author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
| booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
| title={Dimensionality Reduction by Learning an Invariant Mapping}, |
| year={2006}, |
| volume={2}, |
| number={}, |
| pages={1735-1742}, |
| doi={10.1109/CVPR.2006.100} |
| } |
| ``` |
|
|
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