| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:21484 |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: sentence-transformers/LaBSE |
| | widget: |
| | - source_sentence: زنی ماهی را سرخ می کند. |
| | sentences: |
| | - ماهی توسط زنی پخته می شود |
| | - در سال ۱۱۵۷ ق.م کوتیر-ناهوته حکمران ایلام برای گرفتن انتقام بابل را فتح میکند. |
| | - دو نفر سوار موتورسیکلت می شوند |
| | - source_sentence: نرخهای بهره چگونه بر قرضگیری و سرمایهگذاری تأثیر میگذارند؟ |
| | sentences: |
| | - چالشها و تجربیات شخصی جی.K. رولینگ، از جمله مرگ مادرش، بر عمق احساسی و مضامین |
| | مجموعه 'هری پاتر' تأثیرگذار بود. |
| | - نرخ بهره میتواند تحت تأثیر تورم، رشد اقتصادی و سیاستهای پولی قرار گیرد. |
| | - گروهی از مردم به لباس محافظتی مجهز نیستند |
| | - source_sentence: 'شهرستان مدیسون، تگزاس (به انگلیسی: Madison County, Texas) یک سکونتگاه |
| | مسکونی در ایالات متحده آمریکا است که در تگزاس واقع شدهاست.' |
| | sentences: |
| | - شهرستان مدیسون در در ایالت تگزاس قرار دارد. |
| | - زنان در حال پوشاندن گوش های بونی و شماره مسابقه هستند و به چیزی از دور اشاره |
| | می کنند |
| | - سوار در برف در حال دوچرخه سواری است و یک ژاکت قرمز پوشیده است |
| | - source_sentence: خانواده ای خوشحال در کنار شومینه برای عکس ژست گرفته اند |
| | sentences: |
| | - مردی آنجا نیست که روی صندلی نشسته و چشم هایش را مالش دهد |
| | - آیا باید برای CAT به مربیگری بپیوندم؟ |
| | - خانواده ای غمگین کنار شومینه ژست گرفته اند |
| | - source_sentence: کودک جوان دارد اسکوتر سه چرخ را روبه پایین در پیاده رو می راند. |
| | sentences: |
| | - کتاب قابوس نامه اثر عنصرالمعالی کیکاووس بن اسکندر می باشد. |
| | - دو سگ بزرگ در چمن زار ورجه ورجه میکنند |
| | - کودک جوانی دارد اسکوتر سه چرخ را روبه پایین در پیاده رو می راند. |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on sentence-transformers/LaBSE |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-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/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b --> |
| | - **Maximum Sequence Length:** 256 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
| | (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
| | (3): Normalize() |
| | ) |
| | ``` |
| |
|
| | ## 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("codersan/validadted_FaLabse_onV8d") |
| | # Run inference |
| | sentences = [ |
| | 'کودک جوان دارد اسکوتر سه چرخ را روبه پایین در پیاده رو می راند.', |
| | 'کودک جوانی دارد اسکوتر سه چرخ را روبه پایین در پیاده رو می راند.', |
| | 'کتاب قابوس نامه اثر عنصرالمعالی کیکاووس بن اسکندر می باشد.', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 768] |
| | |
| | # 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.* |
| | --> |
| |
|
| | <!-- |
| | ## 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 |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 21,484 training samples |
| | * Columns: <code>anchor</code> and <code>positive</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | |
| | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 4 tokens</li><li>mean: 19.86 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.49 tokens</li><li>max: 76 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | |
| | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>کارگردان چگونه بر یک نمایش تئاتری تأثیر میگذارد؟</code> | <code>کارگردان نورپردازی و جلوههای صوتی را که در نمایش استفاده خواهد شد انتخاب میکند، که بر حال و هوا و جو اجرای نمایش تأثیر میگذارد.</code> | |
| | | <code>پیش از پیدایش شهر اراک گویشهای متفاوتی در منطقه وجود داشت، اما با مهاجرت گروههای مختلف و ساکنان آنها در شهر ترکیب خاصی از لهجههای مختلف به وجود آمد که امروزه به نام لهجه اراکی شناخته میشود.</code> | <code>لهجه اراکی ترکیبی از لهجه های مختلف است</code> | |
| | | <code>اهمیت تاریخی واتیکان چیست؟</code> | <code>واتیکان مرکز روحانی و اداری کلیسای کاتولیک رومی است و برای قرنها یک نهاد مذهبی و سیاسی مهم بوده است.</code> | |
| | * Loss: [<code>MultipleNegativesRankingLoss</code>](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 |
| | - `learning_rate`: 5e-06 |
| | - `weight_decay`: 0.01 |
| | - `num_train_epochs`: 10 |
| | - `warmup_ratio`: 0.1 |
| | - `push_to_hub`: True |
| | - `hub_model_id`: codersan/validadted_FaLabse_onV8d |
| | - `eval_on_start`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### 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`: 8 |
| | - `per_device_eval_batch_size`: 8 |
| | - `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-06 |
| | - `weight_decay`: 0.01 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 10 |
| | - `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`: False |
| | - `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`: True |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: codersan/validadted_FaLabse_onV8d |
| | - `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 |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | <details><summary>Click to expand</summary> |
| |
|
| | | Epoch | Step | Training Loss | |
| | |:------:|:-----:|:-------------:| |
| | | 0 | 0 | - | |
| | | 0.0372 | 100 | 0.1886 | |
| | | 0.0745 | 200 | 0.1598 | |
| | | 0.1117 | 300 | 0.0973 | |
| | | 0.1489 | 400 | 0.1205 | |
| | | 0.1862 | 500 | 0.0752 | |
| | | 0.2234 | 600 | 0.0669 | |
| | | 0.2606 | 700 | 0.0603 | |
| | | 0.2978 | 800 | 0.0492 | |
| | | 0.3351 | 900 | 0.0479 | |
| | | 0.3723 | 1000 | 0.0396 | |
| | | 0.4095 | 1100 | 0.0394 | |
| | | 0.4468 | 1200 | 0.0344 | |
| | | 0.4840 | 1300 | 0.0477 | |
| | | 0.5212 | 1400 | 0.028 | |
| | | 0.5585 | 1500 | 0.0317 | |
| | | 0.5957 | 1600 | 0.054 | |
| | | 0.6329 | 1700 | 0.0526 | |
| | | 0.6701 | 1800 | 0.0288 | |
| | | 0.7074 | 1900 | 0.0319 | |
| | | 0.7446 | 2000 | 0.0374 | |
| | | 0.7818 | 2100 | 0.0155 | |
| | | 0.8191 | 2200 | 0.0447 | |
| | | 0.8563 | 2300 | 0.0241 | |
| | | 0.8935 | 2400 | 0.03 | |
| | | 0.9308 | 2500 | 0.0563 | |
| | | 0.9680 | 2600 | 0.0405 | |
| | | 1.0052 | 2700 | 0.0313 | |
| | | 1.0424 | 2800 | 0.0402 | |
| | | 1.0797 | 2900 | 0.0424 | |
| | | 1.1169 | 3000 | 0.0239 | |
| | | 1.1541 | 3100 | 0.0464 | |
| | | 1.1914 | 3200 | 0.0233 | |
| | | 1.2286 | 3300 | 0.0198 | |
| | | 1.2658 | 3400 | 0.0253 | |
| | | 1.3031 | 3500 | 0.016 | |
| | | 1.3403 | 3600 | 0.0199 | |
| | | 1.3775 | 3700 | 0.0145 | |
| | | 1.4147 | 3800 | 0.0154 | |
| | | 1.4520 | 3900 | 0.0072 | |
| | | 1.4892 | 4000 | 0.0132 | |
| | | 1.5264 | 4100 | 0.0074 | |
| | | 1.5637 | 4200 | 0.016 | |
| | | 1.6009 | 4300 | 0.0222 | |
| | | 1.6381 | 4400 | 0.0243 | |
| | | 1.6754 | 4500 | 0.0071 | |
| | | 1.7126 | 4600 | 0.0061 | |
| | | 1.7498 | 4700 | 0.018 | |
| | | 1.7870 | 4800 | 0.0059 | |
| | | 1.8243 | 4900 | 0.0106 | |
| | | 1.8615 | 5000 | 0.0087 | |
| | | 1.8987 | 5100 | 0.0123 | |
| | | 1.9360 | 5200 | 0.0105 | |
| | | 1.9732 | 5300 | 0.0167 | |
| | | 2.0104 | 5400 | 0.0099 | |
| | | 2.0477 | 5500 | 0.0332 | |
| | | 2.0849 | 5600 | 0.0071 | |
| | | 2.1221 | 5700 | 0.0107 | |
| | | 2.1593 | 5800 | 0.0048 | |
| | | 2.1966 | 5900 | 0.0046 | |
| | | 2.2338 | 6000 | 0.0052 | |
| | | 2.2710 | 6100 | 0.0066 | |
| | | 2.3083 | 6200 | 0.0022 | |
| | | 2.3455 | 6300 | 0.0115 | |
| | | 2.3827 | 6400 | 0.0039 | |
| | | 2.4200 | 6500 | 0.0052 | |
| | | 2.4572 | 6600 | 0.0014 | |
| | | 2.4944 | 6700 | 0.0039 | |
| | | 2.5316 | 6800 | 0.002 | |
| | | 2.5689 | 6900 | 0.0061 | |
| | | 2.6061 | 7000 | 0.016 | |
| | | 2.6433 | 7100 | 0.0064 | |
| | | 2.6806 | 7200 | 0.0031 | |
| | | 2.7178 | 7300 | 0.0016 | |
| | | 2.7550 | 7400 | 0.0037 | |
| | | 2.7923 | 7500 | 0.0015 | |
| | | 2.8295 | 7600 | 0.0035 | |
| | | 2.8667 | 7700 | 0.0021 | |
| | | 2.9039 | 7800 | 0.0083 | |
| | | 2.9412 | 7900 | 0.0037 | |
| | | 2.9784 | 8000 | 0.0092 | |
| | | 3.0156 | 8100 | 0.0014 | |
| | | 3.0529 | 8200 | 0.006 | |
| | | 3.0901 | 8300 | 0.0027 | |
| | | 3.1273 | 8400 | 0.0017 | |
| | | 3.1646 | 8500 | 0.0019 | |
| | | 3.2018 | 8600 | 0.0012 | |
| | | 3.2390 | 8700 | 0.0016 | |
| | | 3.2762 | 8800 | 0.001 | |
| | | 3.3135 | 8900 | 0.0042 | |
| | | 3.3507 | 9000 | 0.001 | |
| | | 3.3879 | 9100 | 0.0008 | |
| | | 3.4252 | 9200 | 0.0009 | |
| | | 3.4624 | 9300 | 0.0007 | |
| | | 3.4996 | 9400 | 0.0015 | |
| | | 3.5369 | 9500 | 0.0013 | |
| | | 3.5741 | 9600 | 0.0014 | |
| | | 3.6113 | 9700 | 0.0125 | |
| | | 3.6485 | 9800 | 0.0011 | |
| | | 3.6858 | 9900 | 0.0008 | |
| | | 3.7230 | 10000 | 0.0013 | |
| | | 3.7602 | 10100 | 0.0007 | |
| | | 3.7975 | 10200 | 0.0005 | |
| | | 3.8347 | 10300 | 0.0008 | |
| | | 3.8719 | 10400 | 0.0006 | |
| | | 3.9092 | 10500 | 0.0037 | |
| | | 3.9464 | 10600 | 0.0045 | |
| | | 3.9836 | 10700 | 0.0008 | |
| | | 4.0208 | 10800 | 0.0007 | |
| | | 4.0581 | 10900 | 0.0014 | |
| | | 4.0953 | 11000 | 0.001 | |
| | | 4.1325 | 11100 | 0.0008 | |
| | | 4.1698 | 11200 | 0.0007 | |
| | | 4.2070 | 11300 | 0.0006 | |
| | | 4.2442 | 11400 | 0.0005 | |
| | | 4.2815 | 11500 | 0.0006 | |
| | | 4.3187 | 11600 | 0.0012 | |
| | | 4.3559 | 11700 | 0.0005 | |
| | | 4.3931 | 11800 | 0.001 | |
| | | 4.4304 | 11900 | 0.0004 | |
| | | 4.4676 | 12000 | 0.0005 | |
| | | 4.5048 | 12100 | 0.0006 | |
| | | 4.5421 | 12200 | 0.0005 | |
| | | 4.5793 | 12300 | 0.0093 | |
| | | 4.6165 | 12400 | 0.0007 | |
| | | 4.6538 | 12500 | 0.0004 | |
| | | 4.6910 | 12600 | 0.0003 | |
| | | 4.7282 | 12700 | 0.0005 | |
| | | 4.7655 | 12800 | 0.0008 | |
| | | 4.8027 | 12900 | 0.0004 | |
| | | 4.8399 | 13000 | 0.0004 | |
| | | 4.8771 | 13100 | 0.0004 | |
| | | 4.9144 | 13200 | 0.0031 | |
| | | 4.9516 | 13300 | 0.0014 | |
| | | 4.9888 | 13400 | 0.0004 | |
| | | 5.0261 | 13500 | 0.0005 | |
| | | 5.0633 | 13600 | 0.0005 | |
| | | 5.1005 | 13700 | 0.0004 | |
| | | 5.1378 | 13800 | 0.0004 | |
| | | 5.1750 | 13900 | 0.0004 | |
| | | 5.2122 | 14000 | 0.0004 | |
| | | 5.2494 | 14100 | 0.0004 | |
| | | 5.2867 | 14200 | 0.0004 | |
| | | 5.3239 | 14300 | 0.0004 | |
| | | 5.3611 | 14400 | 0.0002 | |
| | | 5.3984 | 14500 | 0.0005 | |
| | | 5.4356 | 14600 | 0.0004 | |
| | | 5.4728 | 14700 | 0.0003 | |
| | | 5.5101 | 14800 | 0.0004 | |
| | | 5.5473 | 14900 | 0.0004 | |
| | | 5.5845 | 15000 | 0.0064 | |
| | | 5.6217 | 15100 | 0.0003 | |
| | | 5.6590 | 15200 | 0.0003 | |
| | | 5.6962 | 15300 | 0.0002 | |
| | | 5.7334 | 15400 | 0.0003 | |
| | | 5.7707 | 15500 | 0.0002 | |
| | | 5.8079 | 15600 | 0.0003 | |
| | | 5.8451 | 15700 | 0.0002 | |
| | | 5.8824 | 15800 | 0.0002 | |
| | | 5.9196 | 15900 | 0.0023 | |
| | | 5.9568 | 16000 | 0.0009 | |
| | | 5.9940 | 16100 | 0.0003 | |
| | | 6.0313 | 16200 | 0.0003 | |
| | | 6.0685 | 16300 | 0.0003 | |
| | | 6.1057 | 16400 | 0.0002 | |
| | | 6.1430 | 16500 | 0.0003 | |
| | | 6.1802 | 16600 | 0.0002 | |
| | | 6.2174 | 16700 | 0.0003 | |
| | | 6.2547 | 16800 | 0.0002 | |
| | | 6.2919 | 16900 | 0.0002 | |
| | | 6.3291 | 17000 | 0.0003 | |
| | | 6.3663 | 17100 | 0.0002 | |
| | | 6.4036 | 17200 | 0.0002 | |
| | | 6.4408 | 17300 | 0.0002 | |
| | | 6.4780 | 17400 | 0.0003 | |
| | | 6.5153 | 17500 | 0.0003 | |
| | | 6.5525 | 17600 | 0.0003 | |
| | | 6.5897 | 17700 | 0.0025 | |
| | | 6.6270 | 17800 | 0.0002 | |
| | | 6.6642 | 17900 | 0.0002 | |
| | | 6.7014 | 18000 | 0.0002 | |
| | | 6.7386 | 18100 | 0.0003 | |
| | | 6.7759 | 18200 | 0.0002 | |
| | | 6.8131 | 18300 | 0.0002 | |
| | | 6.8503 | 18400 | 0.0002 | |
| | | 6.8876 | 18500 | 0.0002 | |
| | | 6.9248 | 18600 | 0.0014 | |
| | | 6.9620 | 18700 | 0.0003 | |
| | | 6.9993 | 18800 | 0.0003 | |
| | | 7.0365 | 18900 | 0.0003 | |
| | | 7.0737 | 19000 | 0.0002 | |
| | | 7.1109 | 19100 | 0.0002 | |
| | | 7.1482 | 19200 | 0.0002 | |
| | | 7.1854 | 19300 | 0.0002 | |
| | | 7.2226 | 19400 | 0.0002 | |
| | | 7.2599 | 19500 | 0.0003 | |
| | | 7.2971 | 19600 | 0.0002 | |
| | | 7.3343 | 19700 | 0.0002 | |
| | | 7.3716 | 19800 | 0.0002 | |
| | | 7.4088 | 19900 | 0.0002 | |
| | | 7.4460 | 20000 | 0.0002 | |
| | | 7.4832 | 20100 | 0.0003 | |
| | | 7.5205 | 20200 | 0.0002 | |
| | | 7.5577 | 20300 | 0.0002 | |
| | | 7.5949 | 20400 | 0.0018 | |
| | | 7.6322 | 20500 | 0.0002 | |
| | | 7.6694 | 20600 | 0.0002 | |
| | | 7.7066 | 20700 | 0.0002 | |
| | | 7.7439 | 20800 | 0.0002 | |
| | | 7.7811 | 20900 | 0.0001 | |
| | | 7.8183 | 21000 | 0.0002 | |
| | | 7.8555 | 21100 | 0.0001 | |
| | | 7.8928 | 21200 | 0.0002 | |
| | | 7.9300 | 21300 | 0.0011 | |
| | | 7.9672 | 21400 | 0.0002 | |
| | | 8.0045 | 21500 | 0.0002 | |
| | | 8.0417 | 21600 | 0.0002 | |
| | | 8.0789 | 21700 | 0.0002 | |
| | | 8.1162 | 21800 | 0.0002 | |
| | | 8.1534 | 21900 | 0.0002 | |
| | | 8.1906 | 22000 | 0.0002 | |
| | | 8.2278 | 22100 | 0.0002 | |
| | | 8.2651 | 22200 | 0.0002 | |
| | | 8.3023 | 22300 | 0.0001 | |
| | | 8.3395 | 22400 | 0.0001 | |
| | | 8.3768 | 22500 | 0.0002 | |
| | | 8.4140 | 22600 | 0.0001 | |
| | | 8.4512 | 22700 | 0.0001 | |
| | | 8.4885 | 22800 | 0.0002 | |
| | | 8.5257 | 22900 | 0.0001 | |
| | | 8.5629 | 23000 | 0.0001 | |
| | | 8.6001 | 23100 | 0.0011 | |
| | | 8.6374 | 23200 | 0.0002 | |
| | | 8.6746 | 23300 | 0.0001 | |
| | | 8.7118 | 23400 | 0.0002 | |
| | | 8.7491 | 23500 | 0.0001 | |
| | | 8.7863 | 23600 | 0.0001 | |
| | | 8.8235 | 23700 | 0.0001 | |
| | | 8.8608 | 23800 | 0.0001 | |
| | | 8.8980 | 23900 | 0.0004 | |
| | | 8.9352 | 24000 | 0.0002 | |
| | | 8.9724 | 24100 | 0.0002 | |
| | | 9.0097 | 24200 | 0.0001 | |
| | | 9.0469 | 24300 | 0.0001 | |
| | | 9.0841 | 24400 | 0.0001 | |
| | | 9.1214 | 24500 | 0.0001 | |
| | | 9.1586 | 24600 | 0.0002 | |
| | | 9.1958 | 24700 | 0.0002 | |
| | | 9.2331 | 24800 | 0.0001 | |
| | | 9.2703 | 24900 | 0.0002 | |
| | | 9.3075 | 25000 | 0.0001 | |
| | | 9.3448 | 25100 | 0.0001 | |
| | | 9.3820 | 25200 | 0.0001 | |
| | | 9.4192 | 25300 | 0.0001 | |
| | | 9.4564 | 25400 | 0.0001 | |
| | | 9.4937 | 25500 | 0.0002 | |
| | | 9.5309 | 25600 | 0.0001 | |
| | | 9.5681 | 25700 | 0.0001 | |
| | | 9.6054 | 25800 | 0.0004 | |
| | | 9.6426 | 25900 | 0.0001 | |
| | | 9.6798 | 26000 | 0.0001 | |
| | | 9.7171 | 26100 | 0.0001 | |
| | | 9.7543 | 26200 | 0.0002 | |
| | | 9.7915 | 26300 | 0.0001 | |
| | | 9.8287 | 26400 | 0.0001 | |
| | | 9.8660 | 26500 | 0.0001 | |
| | | 9.9032 | 26600 | 0.0006 | |
| | | 9.9404 | 26700 | 0.0001 | |
| | | 9.9777 | 26800 | 0.0002 | |
| |
|
| | </details> |
| |
|
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.3.1 |
| | - Transformers: 4.47.0 |
| | - PyTorch: 2.5.1+cu121 |
| | - Accelerate: 1.2.1 |
| | - 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} |
| | } |
| | ``` |
| |
|
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