diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1412 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:753920 +- loss:MultipleNegativesRankingLoss +base_model: aubmindlab/bert-base-arabertv02 +widget: +- source_sentence: ': استجابة الحادث بعد حادث كشف عن أوجه القصور في الشركة' + sentences: + - ': لقد وقعت حادثة مؤسفة في الشركة' + - ': الحادثة التي حدثت في الشركة لم تكن غلطتهم' + - ': من غير الواضح بالنسبة لي ان كانوا قد اعط كل المعلومات قبل النطق بالحكم + .' +- source_sentence: ': ما معنى اختصار rq؟' + sentences: + - ': تم نشر غلوبال هوك عسكريًا لدعم العمليات الاستثنائية منذ نوفمبر 2001. + في اسم RQ-4، يشير R إلى التعيين الذي تستخدمه وزارة الدفاع للتعقب، وQ يعني نظام + طيران بدون طيار. يشير الرقم 4 إلى سلسلة من أنظمة الطيران المأهولة عن بعد.' + - ': برنامج تسجيل الشاشة Camtasia. Camtasia هو برنامج يستخدم لتسجيل الأنشطة + على الشاشة، والصوت، والفيديو من الكاميرا، وتقديم العروض التقديمية من PowerPoint. + من خلال Camtasia، يمكنك تسجيل وتحرير وإنتاج ومشاركة محتوى الدروس. تشمل ميزات التحرير + إشارات التوضيح، والتحولات، والتقريب والتحريك، وتعزيزات الصوت، وغيرها. أنتج ملف + الفيديو النهائي الذي يشاهده الطلاب حسب ملاءمةهم، ويمكنك تضمين جدول المحتويات للمساعدة + في التنقل.' + - ': تعريف أعلى. RQ. اختصار لـ ''Rage Quit''، وهو ما يحدث عندما يغادر اللاعب/المستخدم + اللعبة بسبب الغضب، عادة عندما يقتل. يستخدم في الألعاب متعددة اللاعبين عبر الإنترنت + أو LAN، أو الدردشة. على سبيل المثال، WC3(DOTA).' +- source_sentence: ': فتاة تلعب في ساحة لعب' + sentences: + - ': فتاة تلعب في الفناء الأمامي لمنزلهم.' + - ': واذا لم يكمل المتعاقد عمله في الوقت المناسب , فانه قد يتعين عليه تعويض + الحكومة .' + - ': فتاة في ساحة لعب' +- source_sentence: ': ما هو كاسكوس' + sentences: + - ': تستخدم تفاعل بوليميراز سلسلة متعدد الأطراف الكمي (qPCR) لتحديد عدد + نسخ الحمض النووي المحدد في عينة، مقارنةً بمقياس. في PCR في الوقت الحقيقي، يمكن + تحديد عدد نسخ الحمض النووي بعد كل دورة من عمليات التضاعيف.' + - ': كاسكوس هو تحالف متوسط قائم على كرات التداول البيضاء والبنية.' + - ': تُظهر الرسوم البيانية أعلاه نشاط حالة الخدمة لكاسكوس.كو.ايد خلال آخر + 10 فحوصات أوتوماتيكية. يُظهر الشريط الأزرق وقت الاستجابة، وهو أفضل عندما يكون + أصغر. إذا لم يُعرض أي شريط لوقت معين، فهذا يعني أن الخدمة كانت غير متاحة وكان + الموقع غير متصل بالإنترنت.' +- source_sentence: ': يبدو أن الفتاة ذات الوشاح الأخضر والكلب الأبيض يلعبان.' + sentences: + - ': يبدو أن الفتاة والكلب يلعبان.' + - ': الفتاة والكلب لا يتفاعلان' + - ': فيلكيتونوريا هي اضطراب وراثي يزيد من مستويات الفينيلalanine في الدم.' +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 +- pearson_cosine +- spearman_cosine +- pearson_manhattan +- spearman_manhattan +- pearson_euclidean +- spearman_euclidean +- pearson_dot +- spearman_dot +- pearson_max +- spearman_max +model-index: +- name: SentenceTransformer based on aubmindlab/bert-base-arabertv02 + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: Unknown + type: unknown + metrics: + - type: cosine_accuracy@1 + value: 0.7065 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.84 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.877 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9125 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7065 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.28 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.17540000000000003 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09125 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.7065 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.84 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.877 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9125 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.8126831693564126 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.7803275793650786 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.7837897332371666 + name: Cosine Map@100 + - task: + type: semantic-similarity + name: Semantic Similarity + dataset: + name: sts dev + type: sts-dev + metrics: + - type: pearson_cosine + value: 0.8041663291384161 + name: Pearson Cosine + - type: spearman_cosine + value: 0.8057219672338036 + name: Spearman Cosine + - type: pearson_manhattan + value: 0.7938961056566315 + name: Pearson Manhattan + - type: spearman_manhattan + value: 0.804480775014223 + name: Spearman Manhattan + - type: pearson_euclidean + value: 0.7922157416357797 + name: Pearson Euclidean + - type: spearman_euclidean + value: 0.8028596887695664 + name: Spearman Euclidean + - type: pearson_dot + value: 0.7985215152878228 + name: Pearson Dot + - type: spearman_dot + value: 0.7985137905715485 + name: Spearman Dot + - type: pearson_max + value: 0.8041663291384161 + name: Pearson Max + - type: spearman_max + value: 0.8057219672338036 + name: Spearman Max +--- + +# SentenceTransformer based on aubmindlab/bert-base-arabertv02 + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02). 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:** [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 tokens +- **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': 512, 'do_lower_case': False}) with Transformer model: 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 = [ + ': يبدو أن الفتاة ذات الوشاح الأخضر والكلب الأبيض يلعبان.', + ': يبدو أن الفتاة والكلب يلعبان.', + ': الفتاة والكلب لا يتفاعلان', +] +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] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Information Retrieval + +* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| cosine_accuracy@1 | 0.7065 | +| cosine_accuracy@3 | 0.84 | +| cosine_accuracy@5 | 0.877 | +| cosine_accuracy@10 | 0.9125 | +| cosine_precision@1 | 0.7065 | +| cosine_precision@3 | 0.28 | +| cosine_precision@5 | 0.1754 | +| cosine_precision@10 | 0.0912 | +| cosine_recall@1 | 0.7065 | +| cosine_recall@3 | 0.84 | +| cosine_recall@5 | 0.877 | +| cosine_recall@10 | 0.9125 | +| cosine_ndcg@10 | 0.8127 | +| cosine_mrr@10 | 0.7803 | +| **cosine_map@100** | **0.7838** | + +#### Semantic Similarity +* Dataset: `sts-dev` +* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| pearson_cosine | 0.8042 | +| **spearman_cosine** | **0.8057** | +| pearson_manhattan | 0.7939 | +| spearman_manhattan | 0.8045 | +| pearson_euclidean | 0.7922 | +| spearman_euclidean | 0.8029 | +| pearson_dot | 0.7985 | +| spearman_dot | 0.7985 | +| pearson_max | 0.8042 | +| spearman_max | 0.8057 | + + + + + +## Training Details + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 64 +- `per_device_eval_batch_size`: 64 +- `learning_rate`: 1e-05 +- `num_train_epochs`: 1 +- `lr_scheduler_type`: cosine +- `warmup_ratio`: 0.1 +- `fp16`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 64 +- `per_device_eval_batch_size`: 64 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 1e-05 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 1 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine +- `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`: 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`: True +- `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`: False +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `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 +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | Validation Loss | cosine_map@100 | sts-dev_spearman_cosine | +|:------:|:----:|:-------------:|:---------------:|:--------------:|:-----------------------:| +| 0 | 0 | - | - | 0.4308 | 0.4999 | +| 0.0008 | 10 | 4.229 | - | - | - | +| 0.0017 | 20 | 4.2873 | - | - | - | +| 0.0025 | 30 | 3.6209 | - | - | - | +| 0.0034 | 40 | 4.2523 | - | - | - | +| 0.0042 | 50 | 3.6983 | - | - | - | +| 0.0051 | 60 | 3.9072 | - | - | - | +| 0.0059 | 70 | 3.403 | - | - | - | +| 0.0068 | 80 | 3.0885 | - | - | - | +| 0.0076 | 90 | 3.3575 | - | - | - | +| 0.0085 | 100 | 3.9237 | - | - | - | +| 0.0093 | 110 | 3.6796 | - | - | - | +| 0.0102 | 120 | 3.182 | - | - | - | +| 0.0110 | 130 | 3.5442 | - | - | - | +| 0.0119 | 140 | 2.9727 | - | - | - | +| 0.0127 | 150 | 2.9999 | - | - | - | +| 0.0136 | 160 | 2.8556 | - | - | - | +| 0.0144 | 170 | 2.7022 | - | - | - | +| 0.0153 | 180 | 3.2263 | - | - | - | +| 0.0161 | 190 | 2.796 | - | - | - | +| 0.0170 | 200 | 2.6446 | - | - | - | +| 0.0178 | 210 | 2.3583 | - | - | - | +| 0.0187 | 220 | 2.8925 | - | - | - | +| 0.0195 | 230 | 2.8626 | - | - | - | +| 0.0204 | 240 | 2.9225 | - | - | - | +| 0.0212 | 250 | 2.2267 | - | - | - | +| 0.0221 | 260 | 1.9864 | - | - | - | +| 0.0229 | 270 | 2.6644 | - | - | - | +| 0.0238 | 280 | 2.6638 | - | - | - | +| 0.0246 | 290 | 2.3745 | - | - | - | +| 0.0255 | 300 | 1.8501 | - | - | - | +| 0.0263 | 310 | 2.8233 | - | - | - | +| 0.0272 | 320 | 2.1929 | - | - | - | +| 0.0280 | 330 | 2.4857 | - | - | - | +| 0.0289 | 340 | 2.1028 | - | - | - | +| 0.0297 | 350 | 1.9448 | - | - | - | +| 0.0306 | 360 | 1.5421 | - | - | - | +| 0.0314 | 370 | 2.7751 | - | - | - | +| 0.0323 | 380 | 2.5278 | - | - | - | +| 0.0331 | 390 | 2.2187 | - | - | - | +| 0.0340 | 400 | 1.848 | - | - | - | +| 0.0348 | 410 | 2.5653 | - | - | - | +| 0.0357 | 420 | 2.1551 | - | - | - | +| 0.0365 | 430 | 2.7703 | - | - | - | +| 0.0374 | 440 | 2.2344 | - | - | - | +| 0.0382 | 450 | 1.8822 | - | - | - | +| 0.0391 | 460 | 2.2032 | - | - | - | +| 0.0399 | 470 | 2.5615 | - | - | - | +| 0.0408 | 480 | 1.9196 | - | - | - | +| 0.0416 | 490 | 1.7664 | - | - | - | +| 0.0425 | 500 | 1.6504 | - | - | - | +| 0.0433 | 510 | 2.1702 | - | - | - | +| 0.0442 | 520 | 2.5528 | - | - | - | +| 0.0450 | 530 | 2.3397 | - | - | - | +| 0.0459 | 540 | 2.4859 | - | - | - | +| 0.0467 | 550 | 2.3076 | - | - | - | +| 0.0476 | 560 | 2.7487 | - | - | - | +| 0.0484 | 570 | 1.9055 | - | - | - | +| 0.0493 | 580 | 2.2271 | - | - | - | +| 0.0501 | 590 | 1.5778 | - | - | - | +| 0.0510 | 600 | 2.7517 | - | - | - | +| 0.0518 | 610 | 1.941 | - | - | - | +| 0.0527 | 620 | 2.1769 | - | - | - | +| 0.0535 | 630 | 1.7947 | - | - | - | +| 0.0544 | 640 | 1.8666 | - | - | - | +| 0.0552 | 650 | 2.4285 | - | - | - | +| 0.0561 | 660 | 1.998 | - | - | - | +| 0.0569 | 670 | 2.0776 | - | - | - | +| 0.0578 | 680 | 1.7107 | - | - | - | +| 0.0586 | 690 | 1.4931 | - | - | - | +| 0.0595 | 700 | 1.4785 | - | - | - | +| 0.0603 | 710 | 2.3844 | - | - | - | +| 0.0612 | 720 | 1.7501 | - | - | - | +| 0.0620 | 730 | 1.7656 | - | - | - | +| 0.0629 | 740 | 2.1083 | - | - | - | +| 0.0637 | 750 | 2.3617 | - | - | - | +| 0.0646 | 760 | 1.9304 | - | - | - | +| 0.0654 | 770 | 2.1605 | - | - | - | +| 0.0663 | 780 | 2.0953 | - | - | - | +| 0.0671 | 790 | 1.6414 | - | - | - | +| 0.0680 | 800 | 2.9633 | - | - | - | +| 0.0688 | 810 | 2.4998 | - | - | - | +| 0.0697 | 820 | 1.8811 | - | - | - | +| 0.0705 | 830 | 1.4012 | - | - | - | +| 0.0714 | 840 | 1.6192 | - | - | - | +| 0.0722 | 850 | 2.1715 | - | - | - | +| 0.0731 | 860 | 1.965 | - | - | - | +| 0.0739 | 870 | 1.61 | - | - | - | +| 0.0748 | 880 | 2.1058 | - | - | - | +| 0.0756 | 890 | 1.346 | - | - | - | +| 0.0765 | 900 | 1.8801 | - | - | - | +| 0.0773 | 910 | 1.9211 | - | - | - | +| 0.0782 | 920 | 2.234 | - | - | - | +| 0.0790 | 930 | 1.9114 | - | - | - | +| 0.0799 | 940 | 1.9662 | - | - | - | +| 0.0807 | 950 | 1.5801 | - | - | - | +| 0.0816 | 960 | 1.3301 | - | - | - | +| 0.0824 | 970 | 1.6247 | - | - | - | +| 0.0833 | 980 | 1.5851 | - | - | - | +| 0.0841 | 990 | 1.9348 | - | - | - | +| 0.0850 | 1000 | 1.7108 | 1.7932 | 0.7490 | 0.8062 | +| 0.0858 | 1010 | 1.84 | - | - | - | +| 0.0867 | 1020 | 2.2945 | - | - | - | +| 0.0875 | 1030 | 1.9166 | - | - | - | +| 0.0884 | 1040 | 1.4608 | - | - | - | +| 0.0892 | 1050 | 2.6738 | - | - | - | +| 0.0901 | 1060 | 1.9956 | - | - | - | +| 0.0909 | 1070 | 1.8495 | - | - | - | +| 0.0918 | 1080 | 1.8593 | - | - | - | +| 0.0926 | 1090 | 2.3967 | - | - | - | +| 0.0935 | 1100 | 1.9406 | - | - | - | +| 0.0943 | 1110 | 1.7431 | - | - | - | +| 0.0952 | 1120 | 1.6852 | - | - | - | +| 0.0960 | 1130 | 2.5254 | - | - | - | +| 0.0969 | 1140 | 1.5981 | - | - | - | +| 0.0977 | 1150 | 1.4272 | - | - | - | +| 0.0986 | 1160 | 1.5939 | - | - | - | +| 0.0994 | 1170 | 1.6437 | - | - | - | +| 0.1003 | 1180 | 1.9103 | - | - | - | +| 0.1011 | 1190 | 2.0834 | - | - | - | +| 0.1020 | 1200 | 1.9915 | - | - | - | +| 0.1028 | 1210 | 2.486 | - | - | - | +| 0.1037 | 1220 | 2.0627 | - | - | - | +| 0.1045 | 1230 | 1.6629 | - | - | - | +| 0.1054 | 1240 | 2.5541 | - | - | - | +| 0.1062 | 1250 | 1.9547 | - | - | - | +| 0.1071 | 1260 | 1.4342 | - | - | - | +| 0.1079 | 1270 | 2.5096 | - | - | - | +| 0.1088 | 1280 | 2.0713 | - | - | - | +| 0.1096 | 1290 | 2.0861 | - | - | - | +| 0.1105 | 1300 | 1.6125 | - | - | - | +| 0.1113 | 1310 | 1.6563 | - | - | - | +| 0.1122 | 1320 | 1.759 | - | - | - | +| 0.1130 | 1330 | 1.3864 | - | - | - | +| 0.1139 | 1340 | 1.5737 | - | - | - | +| 0.1147 | 1350 | 2.2294 | - | - | - | +| 0.1156 | 1360 | 1.9181 | - | - | - | +| 0.1164 | 1370 | 2.2238 | - | - | - | +| 0.1173 | 1380 | 1.4119 | - | - | - | +| 0.1181 | 1390 | 2.1013 | - | - | - | +| 0.1190 | 1400 | 2.1451 | - | - | - | +| 0.1198 | 1410 | 1.7866 | - | - | - | +| 0.1207 | 1420 | 2.2415 | - | - | - | +| 0.1215 | 1430 | 2.118 | - | - | - | +| 0.1224 | 1440 | 1.2572 | - | - | - | +| 0.1232 | 1450 | 2.2137 | - | - | - | +| 0.1241 | 1460 | 1.8935 | - | - | - | +| 0.1249 | 1470 | 1.9213 | - | - | - | +| 0.1258 | 1480 | 2.2463 | - | - | - | +| 0.1266 | 1490 | 1.634 | - | - | - | +| 0.1275 | 1500 | 1.3852 | - | - | - | +| 0.1283 | 1510 | 2.0875 | - | - | - | +| 0.1292 | 1520 | 1.5067 | - | - | - | +| 0.1300 | 1530 | 2.1362 | - | - | - | +| 0.1309 | 1540 | 1.8342 | - | - | - | +| 0.1317 | 1550 | 1.4859 | - | - | - | +| 0.1326 | 1560 | 2.4904 | - | - | - | +| 0.1334 | 1570 | 1.5241 | - | - | - | +| 0.1343 | 1580 | 1.6228 | - | - | - | +| 0.1351 | 1590 | 1.5443 | - | - | - | +| 0.1360 | 1600 | 2.3666 | - | - | - | +| 0.1368 | 1610 | 1.3122 | - | - | - | +| 0.1377 | 1620 | 1.1251 | - | - | - | +| 0.1385 | 1630 | 2.1177 | - | - | - | +| 0.1394 | 1640 | 2.2723 | - | - | - | +| 0.1402 | 1650 | 2.0156 | - | - | - | +| 0.1411 | 1660 | 2.6568 | - | - | - | +| 0.1419 | 1670 | 2.6251 | - | - | - | +| 0.1428 | 1680 | 2.535 | - | - | - | +| 0.1436 | 1690 | 1.5212 | - | - | - | +| 0.1445 | 1700 | 1.8659 | - | - | - | +| 0.1453 | 1710 | 1.3508 | - | - | - | +| 0.1462 | 1720 | 1.0096 | - | - | - | +| 0.1470 | 1730 | 1.8963 | - | - | - | +| 0.1479 | 1740 | 1.3431 | - | - | - | +| 0.1487 | 1750 | 1.6355 | - | - | - | +| 0.1496 | 1760 | 1.5312 | - | - | - | +| 0.1504 | 1770 | 1.7622 | - | - | - | +| 0.1513 | 1780 | 2.4495 | - | - | - | +| 0.1521 | 1790 | 1.6367 | - | - | - | +| 0.1530 | 1800 | 1.588 | - | - | - | +| 0.1538 | 1810 | 2.0486 | - | - | - | +| 0.1547 | 1820 | 1.5316 | - | - | - | +| 0.1555 | 1830 | 1.6174 | - | - | - | +| 0.1564 | 1840 | 1.8715 | - | - | - | +| 0.1572 | 1850 | 1.209 | - | - | - | +| 0.1581 | 1860 | 1.3456 | - | - | - | +| 0.1589 | 1870 | 1.8368 | - | - | - | +| 0.1598 | 1880 | 1.627 | - | - | - | +| 0.1606 | 1890 | 1.9879 | - | - | - | +| 0.1615 | 1900 | 1.9625 | - | - | - | +| 0.1623 | 1910 | 2.0395 | - | - | - | +| 0.1632 | 1920 | 1.7252 | - | - | - | +| 0.1640 | 1930 | 1.8004 | - | - | - | +| 0.1649 | 1940 | 2.2082 | - | - | - | +| 0.1657 | 1950 | 2.2102 | - | - | - | +| 0.1666 | 1960 | 1.1757 | - | - | - | +| 0.1674 | 1970 | 1.9025 | - | - | - | +| 0.1683 | 1980 | 1.5126 | - | - | - | +| 0.1691 | 1990 | 1.4776 | - | - | - | +| 0.1700 | 2000 | 1.5689 | 1.6420 | 0.7600 | 0.8100 | +| 0.1708 | 2010 | 1.6085 | - | - | - | +| 0.1717 | 2020 | 1.5845 | - | - | - | +| 0.1725 | 2030 | 2.0365 | - | - | - | +| 0.1734 | 2040 | 1.7228 | - | - | - | +| 0.1742 | 2050 | 2.2482 | - | - | - | +| 0.1751 | 2060 | 1.961 | - | - | - | +| 0.1759 | 2070 | 1.3036 | - | - | - | +| 0.1768 | 2080 | 1.6365 | - | - | - | +| 0.1776 | 2090 | 2.0563 | - | - | - | +| 0.1785 | 2100 | 1.6711 | - | - | - | +| 0.1793 | 2110 | 1.438 | - | - | - | +| 0.1801 | 2120 | 1.551 | - | - | - | +| 0.1810 | 2130 | 1.4024 | - | - | - | +| 0.1818 | 2140 | 1.6733 | - | - | - | +| 0.1827 | 2150 | 1.7135 | - | - | - | +| 0.1835 | 2160 | 1.6766 | - | - | - | +| 0.1844 | 2170 | 1.4573 | - | - | - | +| 0.1852 | 2180 | 1.0338 | - | - | - | +| 0.1861 | 2190 | 1.6221 | - | - | - | +| 0.1869 | 2200 | 1.5327 | - | - | - | +| 0.1878 | 2210 | 2.1644 | - | - | - | +| 0.1886 | 2220 | 1.3049 | - | - | - | +| 0.1895 | 2230 | 2.1003 | - | - | - | +| 0.1903 | 2240 | 1.9195 | - | - | - | +| 0.1912 | 2250 | 2.1153 | - | - | - | +| 0.1920 | 2260 | 1.5994 | - | - | - | +| 0.1929 | 2270 | 1.417 | - | - | - | +| 0.1937 | 2280 | 1.7211 | - | - | - | +| 0.1946 | 2290 | 1.8263 | - | - | - | +| 0.1954 | 2300 | 1.6932 | - | - | - | +| 0.1963 | 2310 | 2.5187 | - | - | - | +| 0.1971 | 2320 | 1.2162 | - | - | - | +| 0.1980 | 2330 | 2.1805 | - | - | - | +| 0.1988 | 2340 | 2.3068 | - | - | - | +| 0.1997 | 2350 | 1.7788 | - | - | - | +| 0.2005 | 2360 | 1.2979 | - | - | - | +| 0.2014 | 2370 | 2.1878 | - | - | - | +| 0.2022 | 2380 | 1.5155 | - | - | - | +| 0.2031 | 2390 | 1.8877 | - | - | - | +| 0.2039 | 2400 | 2.1062 | - | - | - | +| 0.2048 | 2410 | 2.0619 | - | - | - | +| 0.2056 | 2420 | 1.8003 | - | - | - | +| 0.2065 | 2430 | 1.9592 | - | - | - | +| 0.2073 | 2440 | 1.7833 | - | - | - | +| 0.2082 | 2450 | 2.0723 | - | - | - | +| 0.2090 | 2460 | 2.4516 | - | - | - | +| 0.2099 | 2470 | 1.7211 | - | - | - | +| 0.2107 | 2480 | 1.2233 | - | - | - | +| 0.2116 | 2490 | 2.0983 | - | - | - | +| 0.2124 | 2500 | 1.2546 | - | - | - | +| 0.2133 | 2510 | 1.8098 | - | - | - | +| 0.2141 | 2520 | 1.8222 | - | - | - | +| 0.2150 | 2530 | 1.4316 | - | - | - | +| 0.2158 | 2540 | 1.5401 | - | - | - | +| 0.2167 | 2550 | 2.347 | - | - | - | +| 0.2175 | 2560 | 1.6326 | - | - | - | +| 0.2184 | 2570 | 2.0589 | - | - | - | +| 0.2192 | 2580 | 1.7676 | - | - | - | +| 0.2201 | 2590 | 1.3967 | - | - | - | +| 0.2209 | 2600 | 2.0463 | - | - | - | +| 0.2218 | 2610 | 1.5589 | - | - | - | +| 0.2226 | 2620 | 2.1871 | - | - | - | +| 0.2235 | 2630 | 1.2149 | - | - | - | +| 0.2243 | 2640 | 1.4454 | - | - | - | +| 0.2252 | 2650 | 1.5395 | - | - | - | +| 0.2260 | 2660 | 1.5236 | - | - | - | +| 0.2269 | 2670 | 2.0349 | - | - | - | +| 0.2277 | 2680 | 2.001 | - | - | - | +| 0.2286 | 2690 | 1.3894 | - | - | - | +| 0.2294 | 2700 | 1.5097 | - | - | - | +| 0.2303 | 2710 | 1.754 | - | - | - | +| 0.2311 | 2720 | 1.3568 | - | - | - | +| 0.2320 | 2730 | 2.0414 | - | - | - | +| 0.2328 | 2740 | 1.4016 | - | - | - | +| 0.2337 | 2750 | 1.6315 | - | - | - | +| 0.2345 | 2760 | 1.8147 | - | - | - | +| 0.2354 | 2770 | 1.3025 | - | - | - | +| 0.2362 | 2780 | 1.3445 | - | - | - | +| 0.2371 | 2790 | 1.741 | - | - | - | +| 0.2379 | 2800 | 1.8655 | - | - | - | +| 0.2388 | 2810 | 1.4883 | - | - | - | +| 0.2396 | 2820 | 1.6449 | - | - | - | +| 0.2405 | 2830 | 1.662 | - | - | - | +| 0.2413 | 2840 | 1.2729 | - | - | - | +| 0.2422 | 2850 | 1.518 | - | - | - | +| 0.2430 | 2860 | 1.3131 | - | - | - | +| 0.2439 | 2870 | 1.6467 | - | - | - | +| 0.2447 | 2880 | 1.8138 | - | - | - | +| 0.2456 | 2890 | 1.664 | - | - | - | +| 0.2464 | 2900 | 1.6083 | - | - | - | +| 0.2473 | 2910 | 1.8901 | - | - | - | +| 0.2481 | 2920 | 1.5651 | - | - | - | +| 0.2490 | 2930 | 1.477 | - | - | - | +| 0.2498 | 2940 | 2.043 | - | - | - | +| 0.2507 | 2950 | 1.7563 | - | - | - | +| 0.2515 | 2960 | 1.1634 | - | - | - | +| 0.2524 | 2970 | 2.095 | - | - | - | +| 0.2532 | 2980 | 1.9133 | - | - | - | +| 0.2541 | 2990 | 1.9891 | - | - | - | +| 0.2549 | 3000 | 2.1321 | 1.5351 | 0.7675 | 0.8069 | +| 0.2558 | 3010 | 1.8755 | - | - | - | +| 0.2566 | 3020 | 2.3428 | - | - | - | +| 0.2575 | 3030 | 1.3314 | - | - | - | +| 0.2583 | 3040 | 1.5536 | - | - | - | +| 0.2592 | 3050 | 1.7259 | - | - | - | +| 0.2600 | 3060 | 1.7929 | - | - | - | +| 0.2609 | 3070 | 1.4687 | - | - | - | +| 0.2617 | 3080 | 1.9342 | - | - | - | +| 0.2626 | 3090 | 1.5374 | - | - | - | +| 0.2634 | 3100 | 1.9888 | - | - | - | +| 0.2643 | 3110 | 1.7331 | - | - | - | +| 0.2651 | 3120 | 1.1483 | - | - | - | +| 0.2660 | 3130 | 1.5639 | - | - | - | +| 0.2668 | 3140 | 2.3273 | - | - | - | +| 0.2677 | 3150 | 1.3604 | - | - | - | +| 0.2685 | 3160 | 2.005 | - | - | - | +| 0.2694 | 3170 | 1.0727 | - | - | - | +| 0.2702 | 3180 | 1.9414 | - | - | - | +| 0.2711 | 3190 | 1.5934 | - | - | - | +| 0.2719 | 3200 | 1.027 | - | - | - | +| 0.2728 | 3210 | 1.5364 | - | - | - | +| 0.2736 | 3220 | 1.2373 | - | - | - | +| 0.2745 | 3230 | 1.6682 | - | - | - | +| 0.2753 | 3240 | 1.9316 | - | - | - | +| 0.2762 | 3250 | 1.8804 | - | - | - | +| 0.2770 | 3260 | 2.5283 | - | - | - | +| 0.2779 | 3270 | 1.5819 | - | - | - | +| 0.2787 | 3280 | 1.3799 | - | - | - | +| 0.2796 | 3290 | 1.3378 | - | - | - | +| 0.2804 | 3300 | 1.5859 | - | - | - | +| 0.2813 | 3310 | 1.2679 | - | - | - | +| 0.2821 | 3320 | 1.9714 | - | - | - | +| 0.2830 | 3330 | 1.1809 | - | - | - | +| 0.2838 | 3340 | 0.9777 | - | - | - | +| 0.2847 | 3350 | 1.3289 | - | - | - | +| 0.2855 | 3360 | 1.3455 | - | - | - | +| 0.2864 | 3370 | 2.1545 | - | - | - | +| 0.2872 | 3380 | 1.5625 | - | - | - | +| 0.2881 | 3390 | 1.5034 | - | - | - | +| 0.2889 | 3400 | 1.8048 | - | - | - | +| 0.2898 | 3410 | 2.0181 | - | - | - | +| 0.2906 | 3420 | 1.5169 | - | - | - | +| 0.2915 | 3430 | 1.5428 | - | - | - | +| 0.2923 | 3440 | 1.8036 | - | - | - | +| 0.2932 | 3450 | 1.5026 | - | - | - | +| 0.2940 | 3460 | 2.1377 | - | - | - | +| 0.2949 | 3470 | 1.7275 | - | - | - | +| 0.2957 | 3480 | 1.4765 | - | - | - | +| 0.2966 | 3490 | 2.1761 | - | - | - | +| 0.2974 | 3500 | 1.3884 | - | - | - | +| 0.2983 | 3510 | 1.1503 | - | - | - | +| 0.2991 | 3520 | 1.9235 | - | - | - | +| 0.3000 | 3530 | 1.6309 | - | - | - | +| 0.3008 | 3540 | 1.3259 | - | - | - | +| 0.3017 | 3550 | 1.3987 | - | - | - | +| 0.3025 | 3560 | 1.5873 | - | - | - | +| 0.3034 | 3570 | 1.7668 | - | - | - | +| 0.3042 | 3580 | 1.6178 | - | - | - | +| 0.3051 | 3590 | 1.7817 | - | - | - | +| 0.3059 | 3600 | 1.8823 | - | - | - | +| 0.3068 | 3610 | 1.6265 | - | - | - | +| 0.3076 | 3620 | 1.3581 | - | - | - | +| 0.3085 | 3630 | 1.8566 | - | - | - | +| 0.3093 | 3640 | 1.35 | - | - | - | +| 0.3102 | 3650 | 1.7068 | - | - | - | +| 0.3110 | 3660 | 1.6618 | - | - | - | +| 0.3119 | 3670 | 1.6905 | - | - | - | +| 0.3127 | 3680 | 1.2407 | - | - | - | +| 0.3136 | 3690 | 1.4478 | - | - | - | +| 0.3144 | 3700 | 1.9195 | - | - | - | +| 0.3153 | 3710 | 1.6154 | - | - | - | +| 0.3161 | 3720 | 1.5699 | - | - | - | +| 0.3170 | 3730 | 1.8805 | - | - | - | +| 0.3178 | 3740 | 2.069 | - | - | - | +| 0.3187 | 3750 | 1.4729 | - | - | - | +| 0.3195 | 3760 | 1.6945 | - | - | - | +| 0.3204 | 3770 | 1.8679 | - | - | - | +| 0.3212 | 3780 | 1.6665 | - | - | - | +| 0.3221 | 3790 | 1.5134 | - | - | - | +| 0.3229 | 3800 | 1.9532 | - | - | - | +| 0.3238 | 3810 | 1.3903 | - | - | - | +| 0.3246 | 3820 | 1.9471 | - | - | - | +| 0.3255 | 3830 | 1.9619 | - | - | - | +| 0.3263 | 3840 | 2.0609 | - | - | - | +| 0.3272 | 3850 | 2.2819 | - | - | - | +| 0.3280 | 3860 | 1.775 | - | - | - | +| 0.3289 | 3870 | 1.1209 | - | - | - | +| 0.3297 | 3880 | 1.6595 | - | - | - | +| 0.3306 | 3890 | 1.44 | - | - | - | +| 0.3314 | 3900 | 1.2321 | - | - | - | +| 0.3323 | 3910 | 1.4774 | - | - | - | +| 0.3331 | 3920 | 1.3621 | - | - | - | +| 0.3340 | 3930 | 1.6229 | - | - | - | +| 0.3348 | 3940 | 1.5508 | - | - | - | +| 0.3357 | 3950 | 2.0397 | - | - | - | +| 0.3365 | 3960 | 1.5626 | - | - | - | +| 0.3374 | 3970 | 1.2912 | - | - | - | +| 0.3382 | 3980 | 1.9062 | - | - | - | +| 0.3391 | 3990 | 1.8215 | - | - | - | +| 0.3399 | 4000 | 1.4379 | 1.5198 | 0.7777 | 0.8034 | +| 0.3408 | 4010 | 1.4425 | - | - | - | +| 0.3416 | 4020 | 1.5301 | - | - | - | +| 0.3425 | 4030 | 1.4369 | - | - | - | +| 0.3433 | 4040 | 2.0321 | - | - | - | +| 0.3442 | 4050 | 1.5148 | - | - | - | +| 0.3450 | 4060 | 1.1118 | - | - | - | +| 0.3459 | 4070 | 0.9621 | - | - | - | +| 0.3467 | 4080 | 1.0928 | - | - | - | +| 0.3476 | 4090 | 1.5899 | - | - | - | +| 0.3484 | 4100 | 1.8656 | - | - | - | +| 0.3493 | 4110 | 1.8926 | - | - | - | +| 0.3501 | 4120 | 1.8682 | - | - | - | +| 0.3510 | 4130 | 1.1497 | - | - | - | +| 0.3518 | 4140 | 1.8596 | - | - | - | +| 0.3527 | 4150 | 1.3003 | - | - | - | +| 0.3535 | 4160 | 0.8871 | - | - | - | +| 0.3544 | 4170 | 1.4811 | - | - | - | +| 0.3552 | 4180 | 1.8324 | - | - | - | +| 0.3561 | 4190 | 1.4659 | - | - | - | +| 0.3569 | 4200 | 1.3213 | - | - | - | +| 0.3577 | 4210 | 1.7336 | - | - | - | +| 0.3586 | 4220 | 1.2831 | - | - | - | +| 0.3594 | 4230 | 1.465 | - | - | - | +| 0.3603 | 4240 | 1.6939 | - | - | - | +| 0.3611 | 4250 | 2.2305 | - | - | - | +| 0.3620 | 4260 | 2.0054 | - | - | - | +| 0.3628 | 4270 | 1.4337 | - | - | - | +| 0.3637 | 4280 | 1.5175 | - | - | - | +| 0.3645 | 4290 | 1.1785 | - | - | - | +| 0.3654 | 4300 | 2.1175 | - | - | - | +| 0.3662 | 4310 | 1.3009 | - | - | - | +| 0.3671 | 4320 | 1.72 | - | - | - | +| 0.3679 | 4330 | 1.5373 | - | - | - | +| 0.3688 | 4340 | 1.8053 | - | - | - | +| 0.3696 | 4350 | 1.9372 | - | - | - | +| 0.3705 | 4360 | 1.3671 | - | - | - | +| 0.3713 | 4370 | 1.8016 | - | - | - | +| 0.3722 | 4380 | 1.5083 | - | - | - | +| 0.3730 | 4390 | 0.9237 | - | - | - | +| 0.3739 | 4400 | 2.0224 | - | - | - | +| 0.3747 | 4410 | 1.69 | - | - | - | +| 0.3756 | 4420 | 1.9105 | - | - | - | +| 0.3764 | 4430 | 1.4098 | - | - | - | +| 0.3773 | 4440 | 2.2114 | - | - | - | +| 0.3781 | 4450 | 1.7582 | - | - | - | +| 0.3790 | 4460 | 1.4494 | - | - | - | +| 0.3798 | 4470 | 1.2828 | - | - | - | +| 0.3807 | 4480 | 1.7309 | - | - | - | +| 0.3815 | 4490 | 1.5327 | - | - | - | +| 0.3824 | 4500 | 1.7761 | - | - | - | +| 0.3832 | 4510 | 1.3889 | - | - | - 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| - | +| 0.6195 | 7290 | 1.0137 | - | - | - | +| 0.6203 | 7300 | 1.6892 | - | - | - | +| 0.6212 | 7310 | 1.3141 | - | - | - | +| 0.6220 | 7320 | 1.6547 | - | - | - | +| 0.6229 | 7330 | 1.77 | - | - | - | +| 0.6237 | 7340 | 0.8455 | - | - | - | +| 0.6246 | 7350 | 2.2177 | - | - | - | +| 0.6254 | 7360 | 1.1667 | - | - | - | +| 0.6263 | 7370 | 1.7424 | - | - | - | +| 0.6271 | 7380 | 1.4834 | - | - | - | +| 0.6280 | 7390 | 1.6292 | - | - | - | +| 0.6288 | 7400 | 1.5977 | - | - | - | +| 0.6297 | 7410 | 1.4547 | - | - | - | +| 0.6305 | 7420 | 0.9533 | - | - | - | +| 0.6314 | 7430 | 1.1249 | - | - | - | +| 0.6322 | 7440 | 1.3636 | - | - | - | +| 0.6331 | 7450 | 1.8638 | - | - | - | +| 0.6339 | 7460 | 1.0395 | - | - | - | +| 0.6348 | 7470 | 1.5179 | - | - | - | +| 0.6356 | 7480 | 1.8981 | - | - | - | +| 0.6365 | 7490 | 1.6223 | - | - | - | +| 0.6373 | 7500 | 1.2023 | - | - | - | +| 0.6382 | 7510 | 1.1419 | - | - | - | +| 0.6390 | 7520 | 1.3842 | - | - | - | +| 0.6399 | 7530 | 1.509 | - | - | - | +| 0.6407 | 7540 | 1.5919 | - | - | - | +| 0.6416 | 7550 | 1.4598 | - | - | - | +| 0.6424 | 7560 | 1.2801 | - | - | - | +| 0.6433 | 7570 | 1.2692 | - | - | - | +| 0.6441 | 7580 | 1.5529 | - | - | - | +| 0.6450 | 7590 | 1.1517 | - | - | - | +| 0.6458 | 7600 | 1.6056 | - | - | - | +| 0.6467 | 7610 | 1.2688 | - | - | - | +| 0.6475 | 7620 | 1.479 | - | - | - | +| 0.6484 | 7630 | 1.4652 | - | - | - | +| 0.6492 | 7640 | 1.4347 | - | - | - | +| 0.6501 | 7650 | 1.2185 | - | - | - | +| 0.6509 | 7660 | 1.1517 | - | - | - | +| 0.6518 | 7670 | 1.156 | - | - | - | +| 0.6526 | 7680 | 1.8372 | - | - | - | +| 0.6535 | 7690 | 1.6171 | - | - | - | +| 0.6543 | 7700 | 1.3407 | - | - | - | +| 0.6552 | 7710 | 0.9872 | - | - | - | +| 0.6560 | 7720 | 1.3951 | - | - | - | +| 0.6569 | 7730 | 1.4822 | - | - | - | +| 0.6577 | 7740 | 1.3158 | - | - | - | +| 0.6586 | 7750 | 1.1347 | - | - | - | +| 0.6594 | 7760 | 1.0472 | - | - | - | +| 0.6603 | 7770 | 1.457 | - | - | - | +| 0.6611 | 7780 | 1.3921 | - | - | - | +| 0.6620 | 7790 | 1.3998 | - | - | - | +| 0.6628 | 7800 | 1.1502 | - | - | - | +| 0.6637 | 7810 | 2.2267 | - | - | - | +| 0.6645 | 7820 | 1.9351 | - | - | - | +| 0.6654 | 7830 | 1.4257 | - | - | - | +| 0.6662 | 7840 | 1.6525 | - | - | - | +| 0.6671 | 7850 | 1.381 | - | - | - | +| 0.6679 | 7860 | 1.5691 | - | - | - | +| 0.6688 | 7870 | 1.5265 | - | - | - | +| 0.6696 | 7880 | 1.977 | - | - | - | +| 0.6705 | 7890 | 1.2655 | - | - | - | +| 0.6713 | 7900 | 1.8937 | - | - | - | +| 0.6722 | 7910 | 1.6809 | - | - | - | +| 0.6730 | 7920 | 1.025 | - | - | - | +| 0.6739 | 7930 | 1.4864 | - | - | - | +| 0.6747 | 7940 | 1.5898 | - | - | - | +| 0.6756 | 7950 | 1.6339 | - | - | - | +| 0.6764 | 7960 | 1.8916 | - | - | - | +| 0.6773 | 7970 | 1.0224 | - | - | - | +| 0.6781 | 7980 | 1.7948 | - | - | - | +| 0.6790 | 7990 | 1.1079 | - | - | - | +| 0.6798 | 8000 | 1.7875 | 1.3588 | 0.7839 | 0.8058 | +| 0.6807 | 8010 | 1.4137 | - | - | - | +| 0.6815 | 8020 | 1.9477 | - | - | - | +| 0.6824 | 8030 | 1.0443 | - | - | - | +| 0.6832 | 8040 | 1.497 | - | - | - | +| 0.6841 | 8050 | 1.5791 | - | - | - | +| 0.6849 | 8060 | 1.9525 | - | - | - | +| 0.6858 | 8070 | 1.1862 | - | - | - | +| 0.6866 | 8080 | 1.7105 | - | - | - | +| 0.6875 | 8090 | 1.2424 | - | - | - | +| 0.6883 | 8100 | 1.317 | - | - | - | +| 0.6892 | 8110 | 1.424 | - | - | - | +| 0.6900 | 8120 | 1.3014 | - | - | - | +| 0.6909 | 8130 | 1.4309 | - | - | - | +| 0.6917 | 8140 | 1.7155 | - | - | - | +| 0.6926 | 8150 | 1.2908 | - | - | - | +| 0.6934 | 8160 | 1.4366 | - | - | - | +| 0.6943 | 8170 | 1.5324 | - | - | - | +| 0.6951 | 8180 | 1.859 | - | - | - | +| 0.6960 | 8190 | 1.2369 | - | - | - | +| 0.6968 | 8200 | 1.1292 | - | - | - | +| 0.6977 | 8210 | 1.7091 | - | - | - | +| 0.6985 | 8220 | 1.2218 | - | - | - | +| 0.6994 | 8230 | 1.823 | - | - | - | +| 0.7002 | 8240 | 1.5848 | - | - | - | +| 0.7011 | 8250 | 1.973 | - | - | - | +| 0.7019 | 8260 | 1.2432 | - | - | - | +| 0.7028 | 8270 | 1.0431 | - | - | - | +| 0.7036 | 8280 | 1.4068 | - | - | - | +| 0.7045 | 8290 | 1.1851 | - | - | - | +| 0.7053 | 8300 | 1.2187 | - | - | - | +| 0.7062 | 8310 | 1.0254 | - | - | - | +| 0.7070 | 8320 | 1.293 | - | - | - | +| 0.7079 | 8330 | 1.393 | - | - | - | +| 0.7087 | 8340 | 1.8043 | - | - | - | +| 0.7096 | 8350 | 1.4796 | - | - | - | +| 0.7104 | 8360 | 1.3933 | - | - | - | +| 0.7113 | 8370 | 1.2227 | - | - | - | +| 0.7121 | 8380 | 0.7937 | - | - | - | +| 0.7130 | 8390 | 1.8031 | - | - | - | +| 0.7138 | 8400 | 1.1379 | - | - | - | +| 0.7146 | 8410 | 1.3593 | - | - | - | +| 0.7155 | 8420 | 1.1971 | - | - | - | +| 0.7163 | 8430 | 1.5309 | - | - | - | +| 0.7172 | 8440 | 1.4029 | - | - | - | +| 0.7180 | 8450 | 1.0953 | - | - | - | +| 0.7189 | 8460 | 0.9642 | - | - | - | +| 0.7197 | 8470 | 1.559 | - | - | - | +| 0.7206 | 8480 | 1.2116 | - | - | - | +| 0.7214 | 8490 | 1.5634 | - | - | - | +| 0.7223 | 8500 | 1.4752 | - | - | - | +| 0.7231 | 8510 | 1.579 | - | - | - | +| 0.7240 | 8520 | 1.3966 | - | - | - | +| 0.7248 | 8530 | 1.249 | - | - | - | +| 0.7257 | 8540 | 1.6503 | - | - | - | +| 0.7265 | 8550 | 1.5157 | - | - | - | +| 0.7274 | 8560 | 1.3254 | - | - | - | +| 0.7282 | 8570 | 1.379 | - | - | - | +| 0.7291 | 8580 | 1.4666 | - | - | - | +| 0.7299 | 8590 | 2.0379 | - | - | - | +| 0.7308 | 8600 | 1.0678 | - | - | - | +| 0.7316 | 8610 | 0.8082 | - | - | - | +| 0.7325 | 8620 | 1.892 | - | - | - | +| 0.7333 | 8630 | 1.5657 | - | - | - | +| 0.7342 | 8640 | 2.0957 | - | - | - | +| 0.7350 | 8650 | 1.6971 | - | - | - | +| 0.7359 | 8660 | 1.6588 | - | - | - | +| 0.7367 | 8670 | 2.0749 | - | - | - | +| 0.7376 | 8680 | 1.4549 | - | - | - | +| 0.7384 | 8690 | 1.5151 | - | - | - | +| 0.7393 | 8700 | 1.4208 | - | - | - | +| 0.7401 | 8710 | 1.1372 | - | - | - | +| 0.7410 | 8720 | 1.3161 | - | - | - | +| 0.7418 | 8730 | 1.7169 | - | - | - | +| 0.7427 | 8740 | 1.1011 | - | - | - | +| 0.7435 | 8750 | 1.5178 | - | - | - | +| 0.7444 | 8760 | 1.3397 | - | - | - | +| 0.7452 | 8770 | 1.0119 | - | - | - | +| 0.7461 | 8780 | 1.4627 | - | - | - | +| 0.7469 | 8790 | 0.9999 | - | - | - | +| 0.7478 | 8800 | 0.9797 | - | - | - | +| 0.7486 | 8810 | 1.6933 | - | - | - | +| 0.7495 | 8820 | 1.2377 | - | - | - | +| 0.7503 | 8830 | 1.2728 | - | - | - | +| 0.7512 | 8840 | 1.1199 | - | - | - | +| 0.7520 | 8850 | 1.5914 | - | - | - | +| 0.7529 | 8860 | 1.7762 | - | - | - | +| 0.7537 | 8870 | 1.4173 | - | - | - | +| 0.7546 | 8880 | 1.7525 | - | - | - | +| 0.7554 | 8890 | 2.119 | - | - | - | +| 0.7563 | 8900 | 1.6517 | - | - | - | +| 0.7571 | 8910 | 1.6911 | - | - | - | +| 0.7580 | 8920 | 1.5806 | - | - | - | +| 0.7588 | 8930 | 1.5838 | - | - | - | +| 0.7597 | 8940 | 1.6819 | - | - | - | +| 0.7605 | 8950 | 1.5756 | - | - | - | +| 0.7614 | 8960 | 1.3978 | - | - | - | +| 0.7622 | 8970 | 1.7492 | - | - | - | +| 0.7631 | 8980 | 1.0175 | - | - | - | +| 0.7639 | 8990 | 2.0354 | - | - | - | +| 0.7648 | 9000 | 1.5181 | 1.3434 | 0.7838 | 0.8057 | + +
+ +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 3.2.1 +- Transformers: 4.45.2 +- PyTorch: 2.1.0+cu118 +- Accelerate: 1.0.1 +- Datasets: 3.0.2 +- Tokenizers: 0.20.3 + +## 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} +} +``` + + + + + + \ No newline at end of file