| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:1021596 |
| - loss:MultipleNegativesRankingLoss |
| base_model: codersan/FaLabse |
| widget: |
| - source_sentence: Most women can't understand why this happens. |
| sentences: |
| - 'بیشتر زنان دلیل این کار را درک نمیکنند ' |
| - ' سخت از خود در غضب بود که آن چه را به آسانی و صراحت میتوانست نزد خود تصمیم بگیرد، |
| قادر به بیان آن در حضور شاهزاده خانم تورسکی نیست. زیرا این زن در نظر او تجسم همان |
| نیروی بیدادگری بود که بر زندگی ظاهری او حکومت میکرد و مانع ابراز عشق و عفو و |
| نمایاندن احساساتش بود.' |
| - 'آقای تالبویز: چه روزهای خوشی، عجب روزهای خوشی!' |
| - source_sentence: to government offices, to the post office, and to the Governor's. |
| sentences: |
| - ناخوشی را تقویت میکند. |
| - به ادارات دولتی و اداره پست و سپس نزد استاندار رفت. |
| - اما به حال طبیعی نبود و در حالی که بازوی شوهرش را گرفته بود، گفتی که در عالم رؤیا |
| قدم بر میدارد. |
| - source_sentence: Even as she did so a sound checked her for an instant ' the unmistakable |
| bang of a window shutting, somewhere in Mrs Semprill's house. |
| sentences: |
| - در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن پنجره خانه خانم سمپریل |
| بود! |
| - این کارم گذشتن از مرز بود. |
| - به همین دلیل هیچ کس بهتر از او برای تربیت مردی که حافظ تمامی خصوصیات نیک خانوادگی |
| باشد، وجود نداشت. |
| - source_sentence: 'It signifies God: done this day by my hand.' |
| sentences: |
| - معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد. |
| - همه یکدیگر را بوسیدند |
| - این نشو نهی جادوگرهای تبه کاره |
| - source_sentence: If this were continued, the barricade was no longer tenable. |
| sentences: |
| - اگر این کار مداومت مییافت، سنگر قادر به مقاومت نمیبود. |
| - هر دو با هم به زمین میغلتیدند. |
| - خوب، در این لحظه او یک محافظ داشت. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on codersan/FaLabse |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaLabse](https://huggingface.co/codersan/FaLabse). 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:** [codersan/FaLabse](https://huggingface.co/codersan/FaLabse) <!-- at revision 0fe1341c6962d7fe2ea375d90f9f55f34e395bcd --> |
| - **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/FaLabse_Mizan4") |
| # Run inference |
| sentences = [ |
| 'If this were continued, the barricade was no longer tenable.', |
| 'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.', |
| 'خوب، در این لحظه او یک محافظ داشت.', |
| ] |
| 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: 1,021,596 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: 3 tokens</li><li>mean: 16.37 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.63 tokens</li><li>max: 81 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------| |
| | <code>They arose to obey.</code> | <code>دختران برای اطاعت امر پدر از جا برخاستند.</code> | |
| | <code>You'll know it all in time</code> | <code>همه چیز را بم وقع خواهی دانست.</code> | |
| | <code>She is in hysterics up there, and moans and says that we have been 'shamed and disgraced.</code> | <code>او هر لحظه گرفتار یک وضع است، زارزار گریه میکند. میگوید به ما توهین کردهاند، حیثیتمان را لکهدار نمودند.</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 |
| - `per_device_train_batch_size`: 32 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 1 |
| - `warmup_ratio`: 0.1 |
| - `load_best_model_at_end`: True |
| - `push_to_hub`: True |
| - `hub_model_id`: codersan/FaLabse_Mizan4 |
| - `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`: 32 |
| - `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`: 2e-05 |
| - `weight_decay`: 0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1 |
| - `num_train_epochs`: 1 |
| - `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`: 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`: True |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: codersan/FaLabse_Mizan4 |
| - `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 | - | |
| | 0.0031 | 100 | 0.1023 | |
| | 0.0063 | 200 | 0.1162 | |
| | 0.0094 | 300 | 0.0976 | |
| | **0.0125** | **400** | **0.088** | |
| | 0.0157 | 500 | 0.0691 | |
| | 0.0188 | 600 | 0.0678 | |
| | 0.0219 | 700 | 0.082 | |
| | 0.0251 | 800 | 0.08 | |
| | 0.0282 | 900 | 0.0758 | |
| | 0.0313 | 1000 | 0.0763 | |
| | 0.0345 | 1100 | 0.0786 | |
| | 0.0376 | 1200 | 0.0666 | |
| | 0.0407 | 1300 | 0.0722 | |
| | 0.0439 | 1400 | 0.0638 | |
| | 0.0470 | 1500 | 0.0615 | |
| | 0.0501 | 1600 | 0.0623 | |
| | 0.0532 | 1700 | 0.0639 | |
| | 0.0564 | 1800 | 0.0692 | |
| | 0.0595 | 1900 | 0.0625 | |
| | 0.0626 | 2000 | 0.0774 | |
| | 0.0658 | 2100 | 0.06 | |
| | 0.0689 | 2200 | 0.0543 | |
| | 0.0720 | 2300 | 0.0611 | |
| | 0.0752 | 2400 | 0.0697 | |
| | 0.0783 | 2500 | 0.0703 | |
| | 0.0814 | 2600 | 0.058 | |
| | 0.0846 | 2700 | 0.075 | |
| | 0.0877 | 2800 | 0.062 | |
| | 0.0908 | 2900 | 0.0756 | |
| | 0.0940 | 3000 | 0.0668 | |
| | 0.0971 | 3100 | 0.054 | |
| | 0.1002 | 3200 | 0.0626 | |
| | 0.1034 | 3300 | 0.0645 | |
| | 0.1065 | 3400 | 0.0714 | |
| | 0.1096 | 3500 | 0.0644 | |
| | 0.1128 | 3600 | 0.0693 | |
| | 0.1159 | 3700 | 0.0734 | |
| | 0.1190 | 3800 | 0.0622 | |
| | 0.1222 | 3900 | 0.0741 | |
| | 0.1253 | 4000 | 0.0761 | |
| | 0.1284 | 4100 | 0.0582 | |
| | 0.1316 | 4200 | 0.0804 | |
| | 0.1347 | 4300 | 0.0708 | |
| | 0.1378 | 4400 | 0.0734 | |
| | 0.1410 | 4500 | 0.0709 | |
| | 0.1441 | 4600 | 0.0759 | |
| | 0.1472 | 4700 | 0.085 | |
| | 0.1504 | 4800 | 0.0573 | |
| | 0.1535 | 4900 | 0.056 | |
| | 0.1566 | 5000 | 0.0601 | |
| | 0.1597 | 5100 | 0.0596 | |
| | 0.1629 | 5200 | 0.079 | |
| | 0.1660 | 5300 | 0.0679 | |
| | 0.1691 | 5400 | 0.0553 | |
| | 0.1723 | 5500 | 0.0677 | |
| | 0.1754 | 5600 | 0.0795 | |
| | 0.1785 | 5700 | 0.0779 | |
| | 0.1817 | 5800 | 0.0599 | |
| | 0.1848 | 5900 | 0.0667 | |
| | 0.1879 | 6000 | 0.064 | |
| | 0.1911 | 6100 | 0.0637 | |
| | 0.1942 | 6200 | 0.0747 | |
| | 0.1973 | 6300 | 0.0829 | |
| | 0.2005 | 6400 | 0.0589 | |
| | 0.2036 | 6500 | 0.0623 | |
| | 0.2067 | 6600 | 0.0589 | |
| | 0.2099 | 6700 | 0.0648 | |
| | 0.2130 | 6800 | 0.0527 | |
| | 0.2161 | 6900 | 0.0519 | |
| | 0.2193 | 7000 | 0.0668 | |
| | 0.2224 | 7100 | 0.0729 | |
| | 0.2255 | 7200 | 0.0627 | |
| | 0.2287 | 7300 | 0.0539 | |
| | 0.2318 | 7400 | 0.055 | |
| | 0.2349 | 7500 | 0.0663 | |
| | 0.2381 | 7600 | 0.0589 | |
| | 0.2412 | 7700 | 0.0555 | |
| | 0.2443 | 7800 | 0.0875 | |
| | 0.2475 | 7900 | 0.055 | |
| | 0.2506 | 8000 | 0.0584 | |
| | 0.2537 | 8100 | 0.0607 | |
| | 0.2569 | 8200 | 0.0551 | |
| | 0.2600 | 8300 | 0.0527 | |
| | 0.2631 | 8400 | 0.0773 | |
| | 0.2662 | 8500 | 0.0696 | |
| | 0.2694 | 8600 | 0.062 | |
| | 0.2725 | 8700 | 0.0716 | |
| | 0.2756 | 8800 | 0.06 | |
| | 0.2788 | 8900 | 0.0536 | |
| | 0.2819 | 9000 | 0.0604 | |
| | 0.2850 | 9100 | 0.0563 | |
| | 0.2882 | 9200 | 0.0734 | |
| | 0.2913 | 9300 | 0.0714 | |
| | 0.2944 | 9400 | 0.0658 | |
| | 0.2976 | 9500 | 0.0623 | |
| | 0.3007 | 9600 | 0.0713 | |
| | 0.3038 | 9700 | 0.0674 | |
| | 0.3070 | 9800 | 0.0708 | |
| | 0.3101 | 9900 | 0.0579 | |
| | 0.3132 | 10000 | 0.0616 | |
| | 0.3164 | 10100 | 0.0653 | |
| | 0.3195 | 10200 | 0.0614 | |
| | 0.3226 | 10300 | 0.0626 | |
| | 0.3258 | 10400 | 0.0611 | |
| | 0.3289 | 10500 | 0.0521 | |
| | 0.3320 | 10600 | 0.056 | |
| | 0.3352 | 10700 | 0.0761 | |
| | 0.3383 | 10800 | 0.0629 | |
| | 0.3414 | 10900 | 0.0658 | |
| | 0.3446 | 11000 | 0.0576 | |
| | 0.3477 | 11100 | 0.0483 | |
| | 0.3508 | 11200 | 0.0654 | |
| | 0.3540 | 11300 | 0.0602 | |
| | 0.3571 | 11400 | 0.065 | |
| | 0.3602 | 11500 | 0.0787 | |
| | 0.3634 | 11600 | 0.0634 | |
| | 0.3665 | 11700 | 0.0678 | |
| | 0.3696 | 11800 | 0.0758 | |
| | 0.3727 | 11900 | 0.0637 | |
| | 0.3759 | 12000 | 0.0577 | |
| | 0.3790 | 12100 | 0.0572 | |
| | 0.3821 | 12200 | 0.0614 | |
| | 0.3853 | 12300 | 0.0685 | |
| | 0.3884 | 12400 | 0.0641 | |
| | 0.3915 | 12500 | 0.0583 | |
| | 0.3947 | 12600 | 0.0502 | |
| | 0.3978 | 12700 | 0.0481 | |
| | 0.4009 | 12800 | 0.0546 | |
| | 0.4041 | 12900 | 0.0664 | |
| | 0.4072 | 13000 | 0.0699 | |
| | 0.4103 | 13100 | 0.0513 | |
| | 0.4135 | 13200 | 0.0423 | |
| | 0.4166 | 13300 | 0.0554 | |
| | 0.4197 | 13400 | 0.0592 | |
| | 0.4229 | 13500 | 0.0457 | |
| | 0.4260 | 13600 | 0.0612 | |
| | 0.4291 | 13700 | 0.0507 | |
| | 0.4323 | 13800 | 0.0592 | |
| | 0.4354 | 13900 | 0.0566 | |
| | 0.4385 | 14000 | 0.0806 | |
| | 0.4417 | 14100 | 0.0648 | |
| | 0.4448 | 14200 | 0.0535 | |
| | 0.4479 | 14300 | 0.0748 | |
| | 0.4511 | 14400 | 0.0488 | |
| | 0.4542 | 14500 | 0.0539 | |
| | 0.4573 | 14600 | 0.0597 | |
| | 0.4605 | 14700 | 0.065 | |
| | 0.4636 | 14800 | 0.0594 | |
| | 0.4667 | 14900 | 0.05 | |
| | 0.4699 | 15000 | 0.0488 | |
| | 0.4730 | 15100 | 0.0537 | |
| | 0.4761 | 15200 | 0.0396 | |
| | 0.4792 | 15300 | 0.0616 | |
| | 0.4824 | 15400 | 0.0605 | |
| | 0.4855 | 15500 | 0.0599 | |
| | 0.4886 | 15600 | 0.0616 | |
| | 0.4918 | 15700 | 0.0731 | |
| | 0.4949 | 15800 | 0.0654 | |
| | 0.4980 | 15900 | 0.0463 | |
| | 0.5012 | 16000 | 0.0463 | |
| | 0.5043 | 16100 | 0.0594 | |
| | 0.5074 | 16200 | 0.0575 | |
| | 0.5106 | 16300 | 0.056 | |
| | 0.5137 | 16400 | 0.0542 | |
| | 0.5168 | 16500 | 0.052 | |
| | 0.5200 | 16600 | 0.0438 | |
| | 0.5231 | 16700 | 0.0675 | |
| | 0.5262 | 16800 | 0.0619 | |
| | 0.5294 | 16900 | 0.0515 | |
| | 0.5325 | 17000 | 0.0575 | |
| | 0.5356 | 17100 | 0.0568 | |
| | 0.5388 | 17200 | 0.0508 | |
| | 0.5419 | 17300 | 0.059 | |
| | 0.5450 | 17400 | 0.0505 | |
| | 0.5482 | 17500 | 0.0582 | |
| | 0.5513 | 17600 | 0.0574 | |
| | 0.5544 | 17700 | 0.0613 | |
| | 0.5576 | 17800 | 0.048 | |
| | 0.5607 | 17900 | 0.0553 | |
| | 0.5638 | 18000 | 0.0571 | |
| | 0.5670 | 18100 | 0.0543 | |
| | 0.5701 | 18200 | 0.0484 | |
| | 0.5732 | 18300 | 0.0763 | |
| | 0.5764 | 18400 | 0.056 | |
| | 0.5795 | 18500 | 0.0533 | |
| | 0.5826 | 18600 | 0.044 | |
| | 0.5857 | 18700 | 0.0515 | |
| | 0.5889 | 18800 | 0.0516 | |
| | 0.5920 | 18900 | 0.0586 | |
| | 0.5951 | 19000 | 0.0523 | |
| | 0.5983 | 19100 | 0.0733 | |
| | 0.6014 | 19200 | 0.0453 | |
| | 0.6045 | 19300 | 0.0663 | |
| | 0.6077 | 19400 | 0.0381 | |
| | 0.6108 | 19500 | 0.0568 | |
| | 0.6139 | 19600 | 0.0492 | |
| | 0.6171 | 19700 | 0.0489 | |
| | 0.6202 | 19800 | 0.0575 | |
| | 0.6233 | 19900 | 0.0642 | |
| | 0.6265 | 20000 | 0.0535 | |
| | 0.6296 | 20100 | 0.0598 | |
| | 0.6327 | 20200 | 0.0569 | |
| | 0.6359 | 20300 | 0.0513 | |
| | 0.6390 | 20400 | 0.0515 | |
| | 0.6421 | 20500 | 0.053 | |
| | 0.6453 | 20600 | 0.0569 | |
| | 0.6484 | 20700 | 0.0372 | |
| | 0.6515 | 20800 | 0.0464 | |
| | 0.6547 | 20900 | 0.0522 | |
| | 0.6578 | 21000 | 0.0427 | |
| | 0.6609 | 21100 | 0.0584 | |
| | 0.6641 | 21200 | 0.0616 | |
| | 0.6672 | 21300 | 0.0552 | |
| | 0.6703 | 21400 | 0.0509 | |
| | 0.6735 | 21500 | 0.0439 | |
| | 0.6766 | 21600 | 0.0762 | |
| | 0.6797 | 21700 | 0.0539 | |
| | 0.6829 | 21800 | 0.0475 | |
| | 0.6860 | 21900 | 0.0557 | |
| | 0.6891 | 22000 | 0.0421 | |
| | 0.6922 | 22100 | 0.0471 | |
| | 0.6954 | 22200 | 0.0398 | |
| | 0.6985 | 22300 | 0.0521 | |
| | 0.7016 | 22400 | 0.0472 | |
| | 0.7048 | 22500 | 0.0579 | |
| | 0.7079 | 22600 | 0.0539 | |
| | 0.7110 | 22700 | 0.0527 | |
| | 0.7142 | 22800 | 0.0677 | |
| | 0.7173 | 22900 | 0.0509 | |
| | 0.7204 | 23000 | 0.0478 | |
| | 0.7236 | 23100 | 0.0593 | |
| | 0.7267 | 23200 | 0.0419 | |
| | 0.7298 | 23300 | 0.0576 | |
| | 0.7330 | 23400 | 0.0485 | |
| | 0.7361 | 23500 | 0.0544 | |
| | 0.7392 | 23600 | 0.0537 | |
| | 0.7424 | 23700 | 0.0481 | |
| | 0.7455 | 23800 | 0.0597 | |
| | 0.7486 | 23900 | 0.0464 | |
| | 0.7518 | 24000 | 0.0537 | |
| | 0.7549 | 24100 | 0.0508 | |
| | 0.7580 | 24200 | 0.045 | |
| | 0.7612 | 24300 | 0.0337 | |
| | 0.7643 | 24400 | 0.0478 | |
| | 0.7674 | 24500 | 0.0495 | |
| | 0.7706 | 24600 | 0.0427 | |
| | 0.7737 | 24700 | 0.0596 | |
| | 0.7768 | 24800 | 0.0468 | |
| | 0.7800 | 24900 | 0.0404 | |
| | 0.7831 | 25000 | 0.0467 | |
| | 0.7862 | 25100 | 0.0514 | |
| | 0.7894 | 25200 | 0.0462 | |
| | 0.7925 | 25300 | 0.0401 | |
| | 0.7956 | 25400 | 0.0539 | |
| | 0.7987 | 25500 | 0.0541 | |
| | 0.8019 | 25600 | 0.0639 | |
| | 0.8050 | 25700 | 0.0392 | |
| | 0.8081 | 25800 | 0.0466 | |
| | 0.8113 | 25900 | 0.0543 | |
| | 0.8144 | 26000 | 0.0507 | |
| | 0.8175 | 26100 | 0.0465 | |
| | 0.8207 | 26200 | 0.0386 | |
| | 0.8238 | 26300 | 0.0606 | |
| | 0.8269 | 26400 | 0.0558 | |
| | 0.8301 | 26500 | 0.0488 | |
| | 0.8332 | 26600 | 0.0556 | |
| | 0.8363 | 26700 | 0.047 | |
| | 0.8395 | 26800 | 0.0548 | |
| | 0.8426 | 26900 | 0.0423 | |
| | 0.8457 | 27000 | 0.0529 | |
| | 0.8489 | 27100 | 0.0513 | |
| | 0.8520 | 27200 | 0.0432 | |
| | 0.8551 | 27300 | 0.0605 | |
| | 0.8583 | 27400 | 0.0448 | |
| | 0.8614 | 27500 | 0.0508 | |
| | 0.8645 | 27600 | 0.0578 | |
| | 0.8677 | 27700 | 0.0409 | |
| | 0.8708 | 27800 | 0.0487 | |
| | 0.8739 | 27900 | 0.058 | |
| | 0.8771 | 28000 | 0.0461 | |
| | 0.8802 | 28100 | 0.0389 | |
| | 0.8833 | 28200 | 0.0427 | |
| | 0.8865 | 28300 | 0.0473 | |
| | 0.8896 | 28400 | 0.061 | |
| | 0.8927 | 28500 | 0.0423 | |
| | 0.8958 | 28600 | 0.0435 | |
| | 0.8990 | 28700 | 0.0389 | |
| | 0.9021 | 28800 | 0.0466 | |
| | 0.9052 | 28900 | 0.042 | |
| | 0.9084 | 29000 | 0.0466 | |
| | 0.9115 | 29100 | 0.0412 | |
| | 0.9146 | 29200 | 0.0444 | |
| | 0.9178 | 29300 | 0.059 | |
| | 0.9209 | 29400 | 0.0466 | |
| | 0.9240 | 29500 | 0.0381 | |
| | 0.9272 | 29600 | 0.0408 | |
| | 0.9303 | 29700 | 0.0557 | |
| | 0.9334 | 29800 | 0.0567 | |
| | 0.9366 | 29900 | 0.0537 | |
| | 0.9397 | 30000 | 0.041 | |
| | 0.9428 | 30100 | 0.0383 | |
| | 0.9460 | 30200 | 0.0412 | |
| | 0.9491 | 30300 | 0.0489 | |
| | 0.9522 | 30400 | 0.046 | |
| | 0.9554 | 30500 | 0.0525 | |
| | 0.9585 | 30600 | 0.0493 | |
| | 0.9616 | 30700 | 0.0485 | |
| | 0.9648 | 30800 | 0.0532 | |
| | 0.9679 | 30900 | 0.0446 | |
| | 0.9710 | 31000 | 0.0372 | |
| | 0.9742 | 31100 | 0.0472 | |
| | 0.9773 | 31200 | 0.0399 | |
| | 0.9804 | 31300 | 0.0402 | |
| | 0.9836 | 31400 | 0.0372 | |
| | 0.9867 | 31500 | 0.0497 | |
| | 0.9898 | 31600 | 0.0432 | |
| | 0.9930 | 31700 | 0.0382 | |
| | 0.9961 | 31800 | 0.0475 | |
| | 0.9992 | 31900 | 0.0367 | |
|
|
| * The bold row denotes the saved checkpoint. |
| </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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