--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1432 - loss:MultipleNegativesRankingLoss base_model: denaya/indoSBERT-large widget: - source_sentence: 'Input-output domestik Indonesia: 17 sektor usaha, harga produsen, data tahun 2016 (juta Rp)' sentences: - 'Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023 ' - 'IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 1996-2014 (1996=100) ' - 'Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17 Lapangan Usaha), 2016 (Juta Rupiah) ' - source_sentence: 'Gaji bulanan: beda umur, beda jenis pekerjaan (9 sektor), 2017' sentences: - 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017 ' - 'Ekspor Rumput Laut dan Ganggang Lainnya menurut Negara Tujuan Utama, 2012-2023 ' - 'Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017 ' - source_sentence: Ringkasan aliran dana kuartal terakhir 2009 dalam Rupiah sentences: - 'Jumlah Perahu/Kapal, Luas Usaha Budidaya dan Produksi menurut Sub Sektor Perikanan, 2002-2016 ' - 'Jumlah Pendapatan Menurut Golongan Rumah Tangga (miliar rupiah) 2000, 2005, dan 2008 ' - 'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah) ' - source_sentence: Berapa total transaksi (harga pembeli) untuk 9 sektor ekonomi di Indonesia tahun 2005? (miliar rupiah) sentences: - 'Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016 ' - 'Transaksi Total Atas Dasar Harga Pembeli 9 Sektor Ekonomi (miliar rupiah), 2005 ' - 'Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau Jawa dan Sumatera dengan Nasional (2018=100) ' - source_sentence: Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk usia 15+ pada tahun 2022? sentences: - 'Persentase Perkembangan Distribusi Pengeluaran ' - 'Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2018 ' - 'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ' 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 - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on denaya/indoSBERT-large results: - task: type: information-retrieval name: Information Retrieval dataset: name: eval type: eval metrics: - type: cosine_accuracy@1 value: 0.9120521172638436 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.990228013029316 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.993485342019544 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.996742671009772 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9120521172638436 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3572204125950054 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23778501628664495 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13745928338762217 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7097252402956855 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7867346590488319 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8052359035035943 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8221312325947948 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8348212945928647 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9497052892818366 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7729410950742827 name: Cosine Map@100 - task: type: binary-classification name: Binary Classification dataset: name: quora duplicates dev type: quora_duplicates_dev metrics: - type: cosine_accuracy value: 0.9914529914529915 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.31953397393226624 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9850953206239168 name: Cosine F1 - type: cosine_f1_threshold value: 0.30364981293678284 name: Cosine F1 Threshold - type: cosine_precision value: 0.988865692414753 name: Cosine Precision - type: cosine_recall value: 0.981353591160221 name: Cosine Recall - type: cosine_ap value: 0.9956970583311449 name: Cosine Ap - type: cosine_mcc value: 0.9791180702139771 name: Cosine Mcc --- # SentenceTransformer based on denaya/indoSBERT-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large). It maps sentences & paragraphs to a 256-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:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 256 dimensions - **Similarity Function:** Cosine Similarity ### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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}) (2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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-large-bpstable-v1") # Run inference sentences = [ 'Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk usia 15+ pada tahun 2022?', 'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ', 'Persentase Perkembangan Distribusi Pengeluaran ', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 256] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `eval` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9121 | | cosine_accuracy@3 | 0.9902 | | cosine_accuracy@5 | 0.9935 | | cosine_accuracy@10 | 0.9967 | | cosine_precision@1 | 0.9121 | | cosine_precision@3 | 0.3572 | | cosine_precision@5 | 0.2378 | | cosine_precision@10 | 0.1375 | | cosine_recall@1 | 0.7097 | | cosine_recall@3 | 0.7867 | | cosine_recall@5 | 0.8052 | | cosine_recall@10 | 0.8221 | | **cosine_ndcg@10** | **0.8348** | | cosine_mrr@10 | 0.9497 | | cosine_map@100 | 0.7729 | #### Binary Classification * Dataset: `quora_duplicates_dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9915 | | cosine_accuracy_threshold | 0.3195 | | cosine_f1 | 0.9851 | | cosine_f1_threshold | 0.3036 | | cosine_precision | 0.9889 | | cosine_recall | 0.9814 | | **cosine_ap** | **0.9957** | | cosine_mcc | 0.9791 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,432 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------| | Average monthly net wage/salary of employees by age group and type of work (Rupiah), 2018 | Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur dan Jenis Pekerjaan (Rupiah), 2018 | 1 | | Cek average real wage buruh industri pengolahan (level bawah) sekitar tahun 2009 | Rata-rata Upah Riil Per Bulan Buruh Industri Pengolahan di Bawah Mandor, 2005-2014 (1996=100) | 1 | | Dimana saya bisa lihat rekapitulasi dokumen RPB kabupaten/kota? | Rekap Dokumen RPB Kabupaten/Kota | 1 | * 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`: 30 - `fp16`: True - `multi_dataset_batch_sampler`: round_robin #### 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 - `num_train_epochs`: 30 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs
Click to expand | Epoch | Step | Training Loss | eval_cosine_ndcg@10 | quora_duplicates_dev_cosine_ap | |:-------:|:----:|:-------------:|:-------------------:|:------------------------------:| | 0.2222 | 20 | - | 0.7769 | - | | 0.4444 | 40 | - | 0.8167 | - | | 0.6667 | 60 | - | 0.8221 | - | | 0.8889 | 80 | - | 0.8282 | - | | 1.0 | 90 | - | 0.8256 | - | | 1.1111 | 100 | - | 0.8278 | - | | 1.3333 | 120 | - | 0.8388 | - | | 1.5556 | 140 | - | 0.8347 | - | | 1.7778 | 160 | - | 0.8351 | - | | 2.0 | 180 | - | 0.8407 | - | | 2.2222 | 200 | - | 0.8302 | - | | 2.4444 | 220 | - | 0.8261 | - | | 2.6667 | 240 | - | 0.8217 | - | | 2.8889 | 260 | - | 0.8161 | - | | 3.0 | 270 | - | 0.8143 | - | | 3.1111 | 280 | - | 0.8133 | - | | 3.3333 | 300 | - | 0.8259 | - | | 3.5556 | 320 | - | 0.8342 | - | | 3.7778 | 340 | - | 0.8267 | - | | 4.0 | 360 | - | 0.8190 | - | | 4.2222 | 380 | - | 0.8193 | - | | 4.4444 | 400 | - | 0.8281 | - | | 4.6667 | 420 | - | 0.8283 | - | | 4.8889 | 440 | - | 0.8197 | - | | 5.0 | 450 | - | 0.8211 | - | | 5.1111 | 460 | - | 0.8118 | - | | 5.3333 | 480 | - | 0.8298 | - | | 5.5556 | 500 | 0.0412 | 0.8283 | - | | 5.7778 | 520 | - | 0.8264 | - | | 6.0 | 540 | - | 0.8271 | - | | 6.2222 | 560 | - | 0.8243 | - | | 6.4444 | 580 | - | 0.8256 | - | | 6.6667 | 600 | - | 0.8356 | - | | 6.8889 | 620 | - | 0.8332 | - | | 7.0 | 630 | - | 0.8250 | - | | 7.1111 | 640 | - | 0.8179 | - | | 7.3333 | 660 | - | 0.8356 | - | | 7.5556 | 680 | - | 0.8400 | - | | 7.7778 | 700 | - | 0.8349 | - | | 8.0 | 720 | - | 0.8281 | - | | 8.2222 | 740 | - | 0.8330 | - | | 8.4444 | 760 | - | 0.8338 | - | | 8.6667 | 780 | - | 0.8338 | - | | 8.8889 | 800 | - | 0.8344 | - | | 9.0 | 810 | - | 0.8319 | - | | 9.1111 | 820 | - | 0.8328 | - | | 9.3333 | 840 | - | 0.8325 | - | | 9.5556 | 860 | - | 0.8375 | - | | 9.7778 | 880 | - | 0.8306 | - | | 10.0 | 900 | - | 0.8263 | - | | 10.2222 | 920 | - | 0.8280 | - | | 10.4444 | 940 | - | 0.8272 | - | | 10.6667 | 960 | - | 0.8280 | - | | 10.8889 | 980 | - | 0.8313 | - | | 11.0 | 990 | - | 0.8307 | - | | 11.1111 | 1000 | 0.0198 | 0.8324 | - | | 11.3333 | 1020 | - | 0.8303 | - | | 11.5556 | 1040 | - | 0.8262 | - | | 11.7778 | 1060 | - | 0.8294 | - | | 12.0 | 1080 | - | 0.8309 | - | | 12.2222 | 1100 | - | 0.8274 | - | | 12.4444 | 1120 | - | 0.8312 | - | | 12.6667 | 1140 | - | 0.8371 | - | | 12.8889 | 1160 | - | 0.8408 | - | | 13.0 | 1170 | - | 0.8374 | - | | 13.1111 | 1180 | - | 0.8344 | - | | 13.3333 | 1200 | - | 0.8341 | - | | 13.5556 | 1220 | - | 0.8333 | - | | 13.7778 | 1240 | - | 0.8388 | - | | 14.0 | 1260 | - | 0.8414 | - | | 14.2222 | 1280 | - | 0.8344 | - | | 14.4444 | 1300 | - | 0.8328 | - | | 14.6667 | 1320 | - | 0.8340 | - | | 14.8889 | 1340 | - | 0.8317 | - | | 15.0 | 1350 | - | 0.8260 | - | | 15.1111 | 1360 | - | 0.8252 | - | | 15.3333 | 1380 | - | 0.8244 | - | | 15.5556 | 1400 | - | 0.8269 | - | | 15.7778 | 1420 | - | 0.8275 | - | | 16.0 | 1440 | - | 0.8281 | - | | 16.2222 | 1460 | - | 0.8294 | - | | 16.4444 | 1480 | - | 0.8299 | - | | 16.6667 | 1500 | 0.0136 | 0.8318 | - | | 16.8889 | 1520 | - | 0.8320 | - | | 17.0 | 1530 | - | 0.8332 | - | | 17.1111 | 1540 | - | 0.8337 | - | | 17.3333 | 1560 | - | 0.8299 | - | | 17.5556 | 1580 | - | 0.8283 | - | | 17.7778 | 1600 | - | 0.8309 | - | | 18.0 | 1620 | - | 0.8329 | - | | 18.2222 | 1640 | - | 0.8317 | - | | 18.4444 | 1660 | - | 0.8313 | - | | 18.6667 | 1680 | - | 0.8317 | - | | 18.8889 | 1700 | - | 0.8356 | - | | 19.0 | 1710 | - | 0.8345 | - | | 19.1111 | 1720 | - | 0.8358 | - | | 19.3333 | 1740 | - | 0.8334 | - | | 19.5556 | 1760 | - | 0.8335 | - | | 19.7778 | 1780 | - | 0.8318 | - | | 20.0 | 1800 | - | 0.8326 | - | | 20.2222 | 1820 | - | 0.8318 | - | | 20.4444 | 1840 | - | 0.8335 | - | | 20.6667 | 1860 | - | 0.8333 | - | | 20.8889 | 1880 | - | 0.8335 | - | | 21.0 | 1890 | - | 0.8341 | - | | 21.1111 | 1900 | - | 0.8341 | - | | 21.3333 | 1920 | - | 0.8355 | - | | 21.5556 | 1940 | - | 0.8360 | - | | 21.7778 | 1960 | - | 0.8343 | - | | 22.0 | 1980 | - | 0.8351 | - | | 22.2222 | 2000 | 0.015 | 0.8342 | - | | 22.4444 | 2020 | - | 0.8342 | - | | 22.6667 | 2040 | - | 0.8339 | - | | 22.8889 | 2060 | - | 0.8342 | - | | 23.0 | 2070 | - | 0.8345 | - | | 23.1111 | 2080 | - | 0.8354 | - | | 23.3333 | 2100 | - | 0.8366 | - | | 23.5556 | 2120 | - | 0.8379 | - | | 23.7778 | 2140 | - | 0.8386 | - | | 24.0 | 2160 | - | 0.8367 | - | | 24.2222 | 2180 | - | 0.8357 | - | | 24.4444 | 2200 | - | 0.8372 | - | | 24.6667 | 2220 | - | 0.8377 | - | | 24.8889 | 2240 | - | 0.8373 | - | | 25.0 | 2250 | - | 0.8367 | - | | 25.1111 | 2260 | - | 0.8366 | - | | 25.3333 | 2280 | - | 0.8369 | - | | 25.5556 | 2300 | - | 0.8373 | - | | 25.7778 | 2320 | - | 0.8366 | - | | 26.0 | 2340 | - | 0.8354 | - | | 26.2222 | 2360 | - | 0.8347 | - | | 26.4444 | 2380 | - | 0.8344 | - | | 26.6667 | 2400 | - | 0.8341 | - | | 26.8889 | 2420 | - | 0.8343 | - | | 27.0 | 2430 | - | 0.8344 | - | | 27.1111 | 2440 | - | 0.8345 | - | | 27.3333 | 2460 | - | 0.8344 | - | | 27.5556 | 2480 | - | 0.8347 | - | | 27.7778 | 2500 | 0.0136 | 0.8342 | - | | 28.0 | 2520 | - | 0.8347 | - | | 28.2222 | 2540 | - | 0.8346 | - | | 28.4444 | 2560 | - | 0.8346 | - | | 28.6667 | 2580 | - | 0.8347 | - | | 28.8889 | 2600 | - | 0.8348 | - | | 29.0 | 2610 | - | 0.8348 | - | | 29.1111 | 2620 | - | 0.8348 | - | | 29.3333 | 2640 | - | 0.8348 | - | | 29.5556 | 2660 | - | 0.8348 | - | | 29.7778 | 2680 | - | 0.8348 | - | | 30.0 | 2700 | - | 0.8348 | - | | -1 | -1 | - | - | 0.9957 |
### 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} } ```