| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:3954179 |
| - loss:MatryoshkaLoss |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: إذا لم تكن قد فعلت ذلك بالفعل ، تحقق من تصميمنا الجديد للمراسلات |
| والحوارات واليوميات . |
| sentences: |
| - تم إعادة تصميم الرسائل والحوارات . |
| - يقدم مقهى بارج كل من وجبات الغداء والإفطار . |
| - قبل ان نعرف اسماء بعضنا او اي شيء قد تعانقنا وبكىنا . |
| - source_sentence: أين تقع جامعة واينزبرج |
| sentences: |
| - جامعة دالاس بابتيست ( DBU ) ، المعروفة سابقا باسم كلية دالاس بابتيست ، هي جامعة |
| فنون ليبرالية مسيحية تقع في دالاس ، تكساس . يقع الحرم الجامعي الرئيسي على بعد |
| حوالي 12 ميلا ( 19 كم ) جنوب غرب وسط مدينة دالاس ويطل على بحيرة ماونتين كريك . |
| تأسست جامعة دالاس بابتيست عام 1898 باسم كلية ديكاتور بابتيست ، وتدير حاليا حرما |
| جامعيا في دالاس وبلانو وهيرست . |
| - الزوجان معا |
| - تقع جامعة واينسبرغ في حرم جامعي معاصر في تلال جنوب غرب ولاية بنسلفانيا ، مع ثلاثة |
| مراكز للبالغين تقع في مناطق بيتسبرغ في ساوثبوينت وكرانبيري ومونروفيل . تم إدراج |
| Hanna Hall و Miller Hall في السجل الوطني للأماكن التاريخية . |
| - source_sentence: The isolated Russian forces resisted in several areas for two more |
| days . |
| sentences: |
| - 'ياهو : كيف يمكنني معرفة ما إذا كان البريد الإلكتروني الذي أرسلته قد تم استلامه |
| أو قراءته ؟' |
| - واستمرت الاشتباكات الحدودية خلال اليومين المقبلين ، حيث استهدفت المخافر الحدودية |
| من الجانبين والتي أسفرت عن وقوع عشرات الإصابات . |
| - قاومت القوات الروسية المعزولة في عة مناطق لمدة يومين آخرين . |
| - source_sentence: فتاة هيبي بشعر أشقر وأرجواني على الجانب يرتدي قميص أبيض وملابس |
| سوداء |
| sentences: |
| - فتاة " هيبي " ترتدي قميصا أبيضا وملابس سوداء شعرها أشقر وأحمر |
| - المرأة تضع يدها في جيب الرجل |
| - فتاة لديها سترة حمراء وسوداء |
| - source_sentence: رجل وامرأة يجلسان في سيارة ووجههما في الاتجاه المعاكس من الكاميرا |
| sentences: |
| - هناك شخصان وسيارة |
| - سيارة صدئة هي الشيء الوحيد المرئي |
| - كان أفضل حالا |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: dev 768 |
| type: dev-768 |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9809960126876831 |
| name: Cosine Accuracy |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: dev 512 |
| type: dev-512 |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9811199903488159 |
| name: Cosine Accuracy |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: dev 256 |
| type: dev-256 |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9813200235366821 |
| name: Cosine Accuracy |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: dev 128 |
| type: dev-128 |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9811360239982605 |
| name: Cosine Accuracy |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: dev 64 |
| type: dev-64 |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9796760082244873 |
| name: Cosine Accuracy |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained on the train dataset. 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - train |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 512, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'رجل وامرأة يجلسان في سيارة ووجههما في الاتجاه المعاكس من الكاميرا', |
| 'هناك شخصان وسيارة', |
| 'سيارة صدئة هي الشيء الوحيد المرئي', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.6451, 0.3299], |
| # [0.6451, 1.0000, 0.4022], |
| # [0.3299, 0.4022, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Triplet |
|
|
| * Dataset: `dev-768` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 768 |
| } |
| ``` |
|
|
| | Metric | Value | |
| |:--------------------|:----------| |
| | **cosine_accuracy** | **0.981** | |
| |
| #### Triplet |
| |
| * Dataset: `dev-512` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 512 |
| } |
| ``` |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.9811** | |
| |
| #### Triplet |
| |
| * Dataset: `dev-256` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 256 |
| } |
| ``` |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.9813** | |
| |
| #### Triplet |
| |
| * Dataset: `dev-128` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 128 |
| } |
| ``` |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.9811** | |
| |
| #### Triplet |
| |
| * Dataset: `dev-64` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 64 |
| } |
| ``` |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.9797** | |
| |
| <!-- |
| ## 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 |
| |
| #### train |
| |
| * Dataset: train |
| * Size: 3,954,179 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 16.1 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 41.85 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 41.99 tokens</li><li>max: 512 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:----------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>في أي مقاطعة تقع لويسفيل أركنساس</code> | <code>لويسفيل هي بلدة في مقاطعة لافاييت ، أركنساس ، الولايات المتحدة . كان عدد السكان 1285 في تعداد عام 2000 . . المدينة هي مقر مقاطعة لافاييت .</code> | <code>ماونتن هوم ، أركنساس . ماونتن هوم هي مدينة صغيرة في مقاطعة باكستر ، أركنساس ، الولايات المتحدة ، في جبال أوزارك الجنوبية بالقرب من حدود الولاية الشمالية مع ميسوري . اعتبارا من تعداد عام 2010 ، بلغ عدد سكان المدينة 12448 نسمة .</code> | |
| | <code>متوسط سمك باب الخزانة</code> | <code>تتميز أبواب العالم القديم بميزات رائعة مثل السماكة المتزايدة ، والملامح الأعمق ، والأعمدة والقضبان الأوسع لإضفاء مظهر وإحساس أكثر دراماتيكية عند مقارنتها بأبواب الخزانة التقليدية . يبلغ عرض Stiles Rails القياسية 3 بوصات ويمكن تصنيعها في 1 و 1 1 - 8 و 1 سمك .</code> | <code>اعتمادا على الخطأ في اللوحة ، يبلغ متوسط أسعار الإصلاح 130 دولارا لإصلاح الأبواب الفولاذية و 190 دولارا للخشب و 170 دولارا للألمنيوم و 150 دولارا للألياف الزجاجية . مزيد من المعلومات حول كيفية استبدال لوحة باب المرآب . إذا تعطلت أداة فتح باب الجراج ، فقد تكون سلامتك في خطر . تريد التأكد من أن بابك يعمل بشكل صحيح حتى لا يغلق بطريق الخطأ على حيوان أليف أو شخص . تريد أيضا إغلاقها لإبعاد اللصوص عن منزلك .</code> | |
| | <code>ما هو تعريف الملء</code> | <code>اعادة تعبئه . اسم تخصيص ثان لوكيل الوصفات الطبية تم الحصول عليه من الصيدلية ، والذي يسمح به فعل الوصفة الأصلية علم الأدوية للحصول على المزيد من دواء معين ، بعد استخدام الكمية الموصوفة في البداية من الوكيل أو إعطائها . انظر الوصفة الطبية .</code> | <code>تعليمات إعادة الملء قم بإعادة الملء فقط باستخدام Spectracide ' Bug Stop Home Barrier Refill . قم بإزالة الغطاء . قم بقياس وصب 12 . 8 أونصة سائلة من المركز في حاوية فارغة سعة 1 جالون من Spectracide - Bug Stop - حاجز منزلي ، واملأه حتى 1 جالون بالماء ، استبدل الغطاء وأغلقه بإحكام . المنتج المنسكب قم بقياس 12 . 8 أونصة سائلة من المركز وصبها بحذر في حاوية فارغة سعة 1 جالون من Spectracide - حاجز منزلي من Spectracide - حاجز منزلي ، واملأه حتى 1 جالون بالماء . استبدل الغطاء وأغلقه بإحكام . امسح أي منتج مسكوب .</code> | |
| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| ```json |
| { |
| "loss": "MultipleNegativesRankingLoss", |
| "matryoshka_dims": [ |
| 768, |
| 512, |
| 256, |
| 128, |
| 64 |
| ], |
| "matryoshka_weights": [ |
| 1, |
| 1, |
| 1, |
| 1, |
| 1 |
| ], |
| "n_dims_per_step": -1 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
|
|
| #### train |
|
|
| * Dataset: train |
| * Size: 1,129,759 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 16.7 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.54 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 35.42 tokens</li><li>max: 512 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------------------|:---------------------------------|:----------------------------------------------------------------------| |
| | <code>رجل يرتدي سروال تنس أزرق وقميص بولو أبيض يضرب كرة التنس</code> | <code>رجل يلعب رياضة</code> | <code>هناك رجل يرتدي زي البيسبول يضرب كرة البيسبول بمضرب التنس</code> | |
| | <code>امرأة في ثوب أسود تبدو متفاجئة</code> | <code>امرأة تغيرت مشاعرها</code> | <code>امرأة تسبح في المحيط</code> | |
| | <code>رجل يرتدي قميص أبيض يقفز على شيء ما على دراجته الصفراء</code> | <code>رجل يركب دراجته</code> | <code>رجل يركب لوح التزلج فوق المنحدر</code> | |
| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| ```json |
| { |
| "loss": "MultipleNegativesRankingLoss", |
| "matryoshka_dims": [ |
| 768, |
| 512, |
| 256, |
| 128, |
| 64 |
| ], |
| "matryoshka_weights": [ |
| 1, |
| 1, |
| 1, |
| 1, |
| 1 |
| ], |
| "n_dims_per_step": -1 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 64 |
| - `num_train_epochs`: 2 |
| - `learning_rate`: 2e-05 |
| - `warmup_steps`: 0.1 |
| - `gradient_accumulation_steps`: 2 |
| - `bf16`: True |
| - `eval_strategy`: steps |
| - `warmup_ratio`: 0.1 |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 64 |
| - `num_train_epochs`: 2 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 2 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `eval_strategy`: steps |
| - `per_device_eval_batch_size`: 8 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: 0.1 |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | train loss | dev-768_cosine_accuracy | dev-512_cosine_accuracy | dev-256_cosine_accuracy | dev-128_cosine_accuracy | dev-64_cosine_accuracy | |
| |:------:|:-----:|:-------------:|:----------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:| |
| | 0.5891 | 18200 | 0.8727 | - | - | - | - | - | - | |
| | 0.5956 | 18400 | 0.8524 | - | - | - | - | - | - | |
| | 0.6021 | 18600 | 0.8995 | - | - | - | - | - | - | |
| | 0.6086 | 18800 | 0.8360 | - | - | - | - | - | - | |
| | 0.6150 | 19000 | 0.8628 | - | - | - | - | - | - | |
| | 0.6215 | 19200 | 0.8244 | - | - | - | - | - | - | |
| | 0.6280 | 19400 | 0.8647 | - | - | - | - | - | - | |
| | 0.6345 | 19600 | 0.8479 | - | - | - | - | - | - | |
| | 0.6409 | 19800 | 0.8204 | - | - | - | - | - | - | |
| | 0.6474 | 20000 | 0.8359 | - | - | - | - | - | - | |
| | 0.6539 | 20200 | 0.7952 | - | - | - | - | - | - | |
| | 0.6604 | 20400 | 0.8375 | - | - | - | - | - | - | |
| | 0.6668 | 20600 | 0.8364 | - | - | - | - | - | - | |
| | 0.6733 | 20800 | 0.8131 | - | - | - | - | - | - | |
| | 0.6798 | 21000 | 0.8310 | - | - | - | - | - | - | |
| | 0.6863 | 21200 | 0.8295 | - | - | - | - | - | - | |
| | 0.6927 | 21400 | 0.7865 | - | - | - | - | - | - | |
| | 0.6992 | 21600 | 0.7960 | - | - | - | - | - | - | |
| | 0.7057 | 21800 | 0.8287 | - | - | - | - | - | - | |
| | 0.7121 | 22000 | 0.8214 | - | - | - | - | - | - | |
| | 0.7186 | 22200 | 0.7879 | - | - | - | - | - | - | |
| | 0.7251 | 22400 | 0.8139 | - | - | - | - | - | - | |
| | 0.7316 | 22600 | 0.7849 | - | - | - | - | - | - | |
| | 0.7380 | 22800 | 0.7880 | - | - | - | - | - | - | |
| | 0.7445 | 23000 | 0.7725 | - | - | - | - | - | - | |
| | 0.7510 | 23200 | 0.8086 | - | - | - | - | - | - | |
| | 0.7575 | 23400 | 0.7687 | - | - | - | - | - | - | |
| | 0.7639 | 23600 | 0.7828 | - | - | - | - | - | - | |
| | 0.7704 | 23800 | 0.7518 | - | - | - | - | - | - | |
| | 0.7769 | 24000 | 0.7599 | 0.4041 | 0.9737 | 0.9738 | 0.9738 | 0.9734 | 0.9718 | |
| | 0.7834 | 24200 | 0.7332 | - | - | - | - | - | - | |
| | 0.7898 | 24400 | 0.7476 | - | - | - | - | - | - | |
| | 0.7963 | 24600 | 0.7806 | - | - | - | - | - | - | |
| | 0.8028 | 24800 | 0.7511 | - | - | - | - | - | - | |
| | 0.8093 | 25000 | 0.7652 | - | - | - | - | - | - | |
| | 0.8157 | 25200 | 0.7883 | - | - | - | - | - | - | |
| | 0.8222 | 25400 | 0.7305 | - | - | - | - | - | - | |
| | 0.8287 | 25600 | 0.7308 | - | - | - | - | - | - | |
| | 0.8352 | 25800 | 0.7368 | - | - | - | - | - | - | |
| | 0.8416 | 26000 | 0.7432 | - | - | - | - | - | - | |
| | 0.8481 | 26200 | 0.7046 | - | - | - | - | - | - | |
| | 0.8546 | 26400 | 0.7476 | - | - | - | - | - | - | |
| | 0.8611 | 26600 | 0.7212 | - | - | - | - | - | - | |
| | 0.8675 | 26800 | 0.7335 | - | - | - | - | - | - | |
| | 0.8740 | 27000 | 0.7415 | - | - | - | - | - | - | |
| | 0.8805 | 27200 | 0.6937 | - | - | - | - | - | - | |
| | 0.8869 | 27400 | 0.7294 | - | - | - | - | - | - | |
| | 0.8934 | 27600 | 0.7436 | - | - | - | - | - | - | |
| | 0.8999 | 27800 | 0.7093 | - | - | - | - | - | - | |
| | 0.9064 | 28000 | 0.7480 | - | - | - | - | - | - | |
| | 0.9128 | 28200 | 0.7039 | - | - | - | - | - | - | |
| | 0.9193 | 28400 | 0.7091 | - | - | - | - | - | - | |
| | 0.9258 | 28600 | 0.7019 | - | - | - | - | - | - | |
| | 0.9323 | 28800 | 0.7081 | - | - | - | - | - | - | |
| | 0.9387 | 29000 | 0.6833 | - | - | - | - | - | - | |
| | 0.9452 | 29200 | 0.6982 | - | - | - | - | - | - | |
| | 0.9517 | 29400 | 0.7249 | - | - | - | - | - | - | |
| | 0.9582 | 29600 | 0.7282 | - | - | - | - | - | - | |
| | 0.9646 | 29800 | 0.7147 | - | - | - | - | - | - | |
| | 0.9711 | 30000 | 0.6742 | 0.3640 | 0.9758 | 0.9759 | 0.9761 | 0.9757 | 0.9742 | |
| | 0.9776 | 30200 | 0.6901 | - | - | - | - | - | - | |
| | 0.9841 | 30400 | 0.7067 | - | - | - | - | - | - | |
| | 0.9905 | 30600 | 0.7166 | - | - | - | - | - | - | |
| | 0.9970 | 30800 | 0.6800 | - | - | - | - | - | - | |
| | 1.0035 | 31000 | 0.6846 | - | - | - | - | - | - | |
| | 1.0099 | 31200 | 0.6723 | - | - | - | - | - | - | |
| | 1.0164 | 31400 | 0.6573 | - | - | - | - | - | - | |
| | 1.0229 | 31600 | 0.6895 | - | - | - | - | - | - | |
| | 1.0294 | 31800 | 0.6588 | - | - | - | - | - | - | |
| | 1.0358 | 32000 | 0.6517 | - | - | - | - | - | - | |
| | 1.0423 | 32200 | 0.6498 | - | - | - | - | - | - | |
| | 1.0488 | 32400 | 0.6836 | - | - | - | - | - | - | |
| | 1.0553 | 32600 | 0.6819 | - | - | - | - | - | - | |
| | 1.0617 | 32800 | 0.6463 | - | - | - | - | - | - | |
| | 1.0682 | 33000 | 0.6645 | - | - | - | - | - | - | |
| | 1.0747 | 33200 | 0.6518 | - | - | - | - | - | - | |
| | 1.0812 | 33400 | 0.6235 | - | - | - | - | - | - | |
| | 1.0876 | 33600 | 0.6302 | - | - | - | - | - | - | |
| | 1.0941 | 33800 | 0.6452 | - | - | - | - | - | - | |
| | 1.1006 | 34000 | 0.6477 | - | - | - | - | - | - | |
| | 1.1070 | 34200 | 0.6084 | - | - | - | - | - | - | |
| | 1.1135 | 34400 | 0.6259 | - | - | - | - | - | - | |
| | 1.1200 | 34600 | 0.6070 | - | - | - | - | - | - | |
| | 1.1265 | 34800 | 0.5977 | - | - | - | - | - | - | |
| | 1.1329 | 35000 | 0.6044 | - | - | - | - | - | - | |
| | 1.1394 | 35200 | 0.6007 | - | - | - | - | - | - | |
| | 1.1459 | 35400 | 0.5628 | - | - | - | - | - | - | |
| | 1.1524 | 35600 | 0.5732 | - | - | - | - | - | - | |
| | 1.1588 | 35800 | 0.5773 | - | - | - | - | - | - | |
| | 1.1653 | 36000 | 0.5719 | 0.3356 | 0.9775 | 0.9777 | 0.9777 | 0.9774 | 0.9760 | |
| | 1.1718 | 36200 | 0.5471 | - | - | - | - | - | - | |
| | 1.1783 | 36400 | 0.5635 | - | - | - | - | - | - | |
| | 1.1847 | 36600 | 0.5390 | - | - | - | - | - | - | |
| | 1.1912 | 36800 | 0.5428 | - | - | - | - | - | - | |
| | 1.1977 | 37000 | 0.5205 | - | - | - | - | - | - | |
| | 1.2042 | 37200 | 0.5362 | - | - | - | - | - | - | |
| | 1.2106 | 37400 | 0.5386 | - | - | - | - | - | - | |
| | 1.2171 | 37600 | 0.5203 | - | - | - | - | - | - | |
| | 1.2236 | 37800 | 0.5301 | - | - | - | - | - | - | |
| | 1.2301 | 38000 | 0.5232 | - | - | - | - | - | - | |
| | 1.2365 | 38200 | 0.4922 | - | - | - | - | - | - | |
| | 1.2430 | 38400 | 0.5029 | - | - | - | - | - | - | |
| | 1.2495 | 38600 | 0.4989 | - | - | - | - | - | - | |
| | 1.2560 | 38800 | 0.5053 | - | - | - | - | - | - | |
| | 1.2624 | 39000 | 0.5081 | - | - | - | - | - | - | |
| | 1.2689 | 39200 | 0.4960 | - | - | - | - | - | - | |
| | 1.2754 | 39400 | 0.5052 | - | - | - | - | - | - | |
| | 1.2818 | 39600 | 0.4984 | - | - | - | - | - | - | |
| | 1.2883 | 39800 | 0.4909 | - | - | - | - | - | - | |
| | 1.2948 | 40000 | 0.5120 | - | - | - | - | - | - | |
| | 1.3013 | 40200 | 0.4873 | - | - | - | - | - | - | |
| | 1.3077 | 40400 | 0.4896 | - | - | - | - | - | - | |
| | 1.3142 | 40600 | 0.4900 | - | - | - | - | - | - | |
| | 1.3207 | 40800 | 0.5036 | - | - | - | - | - | - | |
| | 1.3272 | 41000 | 0.4876 | - | - | - | - | - | - | |
| | 1.3336 | 41200 | 0.4705 | - | - | - | - | - | - | |
| | 1.3401 | 41400 | 0.4786 | - | - | - | - | - | - | |
| | 1.3466 | 41600 | 0.4998 | - | - | - | - | - | - | |
| | 1.3531 | 41800 | 0.4692 | - | - | - | - | - | - | |
| | 1.3595 | 42000 | 0.5064 | 0.3160 | 0.9788 | 0.9790 | 0.9790 | 0.9785 | 0.9774 | |
| | 1.3660 | 42200 | 0.4925 | - | - | - | - | - | - | |
| | 1.3725 | 42400 | 0.4601 | - | - | - | - | - | - | |
| | 1.3790 | 42600 | 0.4762 | - | - | - | - | - | - | |
| | 1.3854 | 42800 | 0.4986 | - | - | - | - | - | - | |
| | 1.3919 | 43000 | 0.4656 | - | - | - | - | - | - | |
| | 1.3984 | 43200 | 0.4507 | - | - | - | - | - | - | |
| | 1.4049 | 43400 | 0.4862 | - | - | - | - | - | - | |
| | 1.4113 | 43600 | 0.4596 | - | - | - | - | - | - | |
| | 1.4178 | 43800 | 0.4696 | - | - | - | - | - | - | |
| | 1.4243 | 44000 | 0.4925 | - | - | - | - | - | - | |
| | 1.4308 | 44200 | 0.4796 | - | - | - | - | - | - | |
| | 1.4372 | 44400 | 0.4525 | - | - | - | - | - | - | |
| | 1.4437 | 44600 | 0.4717 | - | - | - | - | - | - | |
| | 1.4502 | 44800 | 0.4803 | - | - | - | - | - | - | |
| | 1.4566 | 45000 | 0.4675 | - | - | - | - | - | - | |
| | 1.4631 | 45200 | 0.4631 | - | - | - | - | - | - | |
| | 1.4696 | 45400 | 0.4622 | - | - | - | - | - | - | |
| | 1.4761 | 45600 | 0.4496 | - | - | - | - | - | - | |
| | 1.4825 | 45800 | 0.4678 | - | - | - | - | - | - | |
| | 1.4890 | 46000 | 0.4495 | - | - | - | - | - | - | |
| | 1.4955 | 46200 | 0.4474 | - | - | - | - | - | - | |
| | 1.5020 | 46400 | 0.4587 | - | - | - | - | - | - | |
| | 1.5084 | 46600 | 0.4591 | - | - | - | - | - | - | |
| | 1.5149 | 46800 | 0.4573 | - | - | - | - | - | - | |
| | 1.5214 | 47000 | 0.4442 | - | - | - | - | - | - | |
| | 1.5279 | 47200 | 0.4550 | - | - | - | - | - | - | |
| | 1.5343 | 47400 | 0.4493 | - | - | - | - | - | - | |
| | 1.5408 | 47600 | 0.4485 | - | - | - | - | - | - | |
| | 1.5473 | 47800 | 0.4569 | - | - | - | - | - | - | |
| | 1.5538 | 48000 | 0.4346 | 0.3001 | 0.9799 | 0.9802 | 0.9802 | 0.9798 | 0.9788 | |
| | 1.5602 | 48200 | 0.4469 | - | - | - | - | - | - | |
| | 1.5667 | 48400 | 0.4602 | - | - | - | - | - | - | |
| | 1.5732 | 48600 | 0.4430 | - | - | - | - | - | - | |
| | 1.5797 | 48800 | 0.4524 | - | - | - | - | - | - | |
| | 1.5861 | 49000 | 0.4528 | - | - | - | - | - | - | |
| | 1.5926 | 49200 | 0.4348 | - | - | - | - | - | - | |
| | 1.5991 | 49400 | 0.4533 | - | - | - | - | - | - | |
| | 1.6056 | 49600 | 0.4523 | - | - | - | - | - | - | |
| | 1.6120 | 49800 | 0.4509 | - | - | - | - | - | - | |
| | 1.6185 | 50000 | 0.4365 | - | - | - | - | - | - | |
| | 1.6250 | 50200 | 0.4504 | - | - | - | - | - | - | |
| | 1.6314 | 50400 | 0.4292 | - | - | - | - | - | - | |
| | 1.6379 | 50600 | 0.4406 | - | - | - | - | - | - | |
| | 1.6444 | 50800 | 0.4333 | - | - | - | - | - | - | |
| | 1.6509 | 51000 | 0.4361 | - | - | - | - | - | - | |
| | 1.6573 | 51200 | 0.4065 | - | - | - | - | - | - | |
| | 1.6638 | 51400 | 0.4671 | - | - | - | - | - | - | |
| | 1.6703 | 51600 | 0.4328 | - | - | - | - | - | - | |
| | 1.6768 | 51800 | 0.4310 | - | - | - | - | - | - | |
| | 1.6832 | 52000 | 0.4523 | - | - | - | - | - | - | |
| | 1.6897 | 52200 | 0.4232 | - | - | - | - | - | - | |
| | 1.6962 | 52400 | 0.4257 | - | - | - | - | - | - | |
| | 1.7027 | 52600 | 0.4448 | - | - | - | - | - | - | |
| | 1.7091 | 52800 | 0.4491 | - | - | - | - | - | - | |
| | 1.7156 | 53000 | 0.4224 | - | - | - | - | - | - | |
| | 1.7221 | 53200 | 0.4297 | - | - | - | - | - | - | |
| | 1.7286 | 53400 | 0.4522 | - | - | - | - | - | - | |
| | 1.7350 | 53600 | 0.4195 | - | - | - | - | - | - | |
| | 1.7415 | 53800 | 0.4227 | - | - | - | - | - | - | |
| | 1.7480 | 54000 | 0.4381 | 0.2875 | 0.9807 | 0.9808 | 0.9808 | 0.9805 | 0.9794 | |
| | 1.7545 | 54200 | 0.4460 | - | - | - | - | - | - | |
| | 1.7609 | 54400 | 0.4260 | - | - | - | - | - | - | |
| | 1.7674 | 54600 | 0.4299 | - | - | - | - | - | - | |
| | 1.7739 | 54800 | 0.4247 | - | - | - | - | - | - | |
| | 1.7804 | 55000 | 0.4244 | - | - | - | - | - | - | |
| | 1.7868 | 55200 | 0.4185 | - | - | - | - | - | - | |
| | 1.7933 | 55400 | 0.4292 | - | - | - | - | - | - | |
| | 1.7998 | 55600 | 0.4468 | - | - | - | - | - | - | |
| | 1.8062 | 55800 | 0.4118 | - | - | - | - | - | - | |
| | 1.8127 | 56000 | 0.4306 | - | - | - | - | - | - | |
| | 1.8192 | 56200 | 0.4447 | - | - | - | - | - | - | |
| | 1.8257 | 56400 | 0.4147 | - | - | - | - | - | - | |
| | 1.8321 | 56600 | 0.4189 | - | - | - | - | - | - | |
| | 1.8386 | 56800 | 0.4167 | - | - | - | - | - | - | |
| | 1.8451 | 57000 | 0.4022 | - | - | - | - | - | - | |
| | 1.8516 | 57200 | 0.4158 | - | - | - | - | - | - | |
| | 1.8580 | 57400 | 0.4228 | - | - | - | - | - | - | |
| | 1.8645 | 57600 | 0.4256 | - | - | - | - | - | - | |
| | 1.8710 | 57800 | 0.4251 | - | - | - | - | - | - | |
| | 1.8775 | 58000 | 0.4232 | - | - | - | - | - | - | |
| | 1.8839 | 58200 | 0.4143 | - | - | - | - | - | - | |
| | 1.8904 | 58400 | 0.4331 | - | - | - | - | - | - | |
| | 1.8969 | 58600 | 0.4253 | - | - | - | - | - | - | |
| | 1.9034 | 58800 | 0.4410 | - | - | - | - | - | - | |
| | 1.9098 | 59000 | 0.4337 | - | - | - | - | - | - | |
| | 1.9163 | 59200 | 0.4016 | - | - | - | - | - | - | |
| | 1.9228 | 59400 | 0.4249 | - | - | - | - | - | - | |
| | 1.9293 | 59600 | 0.4108 | - | - | - | - | - | - | |
| | 1.9357 | 59800 | 0.4272 | - | - | - | - | - | - | |
| | 1.9422 | 60000 | 0.3916 | 0.2812 | 0.9810 | 0.9811 | 0.9813 | 0.9811 | 0.9797 | |
| | 1.9487 | 60200 | 0.4334 | - | - | - | - | - | - | |
| | 1.9552 | 60400 | 0.4462 | - | - | - | - | - | - | |
| | 1.9616 | 60600 | 0.4436 | - | - | - | - | - | - | |
| | 1.9681 | 60800 | 0.4278 | - | - | - | - | - | - | |
| | 1.9746 | 61000 | 0.4170 | - | - | - | - | - | - | |
| | 1.9810 | 61200 | 0.4376 | - | - | - | - | - | - | |
| | 1.9875 | 61400 | 0.4433 | - | - | - | - | - | - | |
| | 1.9940 | 61600 | 0.4292 | - | - | - | - | - | - | |
|
|
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.10.19 |
| - Sentence Transformers: 5.2.3 |
| - Transformers: 5.2.0 |
| - PyTorch: 2.6.0+cu124 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.5.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MatryoshkaLoss |
| ```bibtex |
| @misc{kusupati2024matryoshka, |
| title={Matryoshka Representation Learning}, |
| author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
| year={2024}, |
| eprint={2205.13147}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
|
|
| #### 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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