| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - loss:CachedMultipleNegativesRankingLoss |
| 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_ndcg@100 |
| - cosine_mrr@10 |
| - cosine_mrr@100 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: validation retrieval |
| type: validation_retrieval |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.62 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.79 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.88 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.93 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.62 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.4733333333333334 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.406 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.29200000000000004 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.12063996930780413 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.23012174376303066 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.3082990416454995 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.3879067546365363 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.4788390199568546 |
| name: Cosine Ndcg@10 |
| - type: cosine_ndcg@100 |
| value: 0.5509783510399775 |
| name: Cosine Ndcg@100 |
| - type: cosine_mrr@10 |
| value: 0.721873015873016 |
| name: Cosine Mrr@10 |
| - type: cosine_mrr@100 |
| value: 0.7250404366243076 |
| name: Cosine Mrr@100 |
| - type: cosine_map@100 |
| value: 0.3511116398212947 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained on the generator dataset. It maps sentences & paragraphs to a 4096-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:** 32768 tokens |
| - **Output Dimensionality:** 4096 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - generator |
| <!-- - **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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'}) |
| (1): Pooling({'word_embedding_dimension': 2048, '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': True, '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("reasonwang/embedding-qwen3-1.7b-embedding_ac1_unicode_shuf") |
| # Run inference |
| sentences = [ |
| 'The weather is lovely today.', |
| "It's so sunny outside!", |
| 'He drove to the stadium.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 4096] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.8993, 0.7656], |
| # [0.8993, 1.0000, 0.6804], |
| # [0.7656, 0.6804, 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 |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `validation_retrieval` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:----------| |
| | cosine_accuracy@1 | 0.62 | |
| | cosine_accuracy@3 | 0.79 | |
| | cosine_accuracy@5 | 0.88 | |
| | cosine_accuracy@10 | 0.93 | |
| | cosine_precision@1 | 0.62 | |
| | cosine_precision@3 | 0.4733 | |
| | cosine_precision@5 | 0.406 | |
| | cosine_precision@10 | 0.292 | |
| | cosine_recall@1 | 0.1206 | |
| | cosine_recall@3 | 0.2301 | |
| | cosine_recall@5 | 0.3083 | |
| | cosine_recall@10 | 0.3879 | |
| | cosine_ndcg@10 | 0.4788 | |
| | **cosine_ndcg@100** | **0.551** | |
| | cosine_mrr@10 | 0.7219 | |
| | cosine_mrr@100 | 0.725 | |
| | cosine_map@100 | 0.3511 | |
|
|
| <!-- |
| ## 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 |
|
|
| #### generator |
|
|
| * Dataset: generator |
| * Columns: <code>sentence1</code> and <code>sentence2</code> |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 4, |
| "gather_across_devices": false |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 256 |
| - `learning_rate`: 2e-05 |
| - `max_steps`: 100000 |
| - `log_level`: info |
| - `bf16`: True |
| - `dataloader_num_workers`: 1 |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
| #### 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`: 256 |
| - `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`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 3.0 |
| - `max_steps`: 100000 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `warmup_steps`: 0 |
| - `log_level`: info |
| - `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 |
| - `bf16`: True |
| - `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`: True |
| - `dataloader_num_workers`: 1 |
| - `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': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `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 |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | validation_retrieval_cosine_ndcg@100 | |
| |:------:|:-----:|:-------------:|:------------------------------------:| |
| | 1e-05 | 1 | 5.2053 | - | |
| | 0.0001 | 10 | 3.8468 | - | |
| | 0.0002 | 20 | 2.8407 | - | |
| | 0.0003 | 30 | 2.5497 | - | |
| | 0.0004 | 40 | 2.4161 | - | |
| | 0.0005 | 50 | 2.3331 | - | |
| | 0.0006 | 60 | 2.3184 | - | |
| | 0.0007 | 70 | 2.2386 | - | |
| | 0.0008 | 80 | 2.2135 | - | |
| | 0.0009 | 90 | 2.1836 | - | |
| | 0.001 | 100 | 2.1797 | 0.4941 | |
| | 0.0011 | 110 | 2.1426 | - | |
| | 0.0012 | 120 | 2.1384 | - | |
| | 0.0013 | 130 | 2.1306 | - | |
| | 0.0014 | 140 | 2.1141 | - | |
| | 0.0015 | 150 | 2.1241 | - | |
| | 0.0016 | 160 | 2.0885 | - | |
| | 0.0017 | 170 | 2.0815 | - | |
| | 0.0018 | 180 | 2.099 | - | |
| | 0.0019 | 190 | 2.0519 | - | |
| | 0.002 | 200 | 2.0482 | 0.5110 | |
| | 0.0021 | 210 | 2.0405 | - | |
| | 0.0022 | 220 | 2.0122 | - | |
| | 0.0023 | 230 | 2.025 | - | |
| | 0.0024 | 240 | 2.011 | - | |
| | 0.0025 | 250 | 2.0368 | - | |
| | 0.0026 | 260 | 1.9879 | - | |
| | 0.0027 | 270 | 2.0191 | - | |
| | 0.0028 | 280 | 1.9754 | - | |
| | 0.0029 | 290 | 2.0114 | - | |
| | 0.003 | 300 | 1.9769 | 0.5158 | |
| | 0.0031 | 310 | 1.9594 | - | |
| | 0.0032 | 320 | 1.9792 | - | |
| | 0.0033 | 330 | 1.9615 | - | |
| | 0.0034 | 340 | 1.955 | - | |
| | 0.0035 | 350 | 1.9725 | - | |
| | 0.0036 | 360 | 1.9524 | - | |
| | 0.0037 | 370 | 1.9435 | - | |
| | 0.0038 | 380 | 1.9398 | - | |
| | 0.0039 | 390 | 1.9251 | - | |
| | 0.004 | 400 | 1.8997 | 0.5228 | |
| | 0.0041 | 410 | 1.9296 | - | |
| | 0.0042 | 420 | 1.9267 | - | |
| | 0.0043 | 430 | 1.9301 | - | |
| | 0.0044 | 440 | 1.9417 | - | |
| | 0.0045 | 450 | 1.9179 | - | |
| | 0.0046 | 460 | 1.9091 | - | |
| | 0.0047 | 470 | 1.9147 | - | |
| | 0.0048 | 480 | 1.9187 | - | |
| | 0.0049 | 490 | 1.9345 | - | |
| | 0.005 | 500 | 1.883 | 0.5258 | |
| | 0.0051 | 510 | 1.9023 | - | |
| | 0.0052 | 520 | 1.8664 | - | |
| | 0.0053 | 530 | 1.9142 | - | |
| | 0.0054 | 540 | 1.8902 | - | |
| | 0.0055 | 550 | 1.877 | - | |
| | 0.0056 | 560 | 1.8753 | - | |
| | 0.0057 | 570 | 1.8804 | - | |
| | 0.0058 | 580 | 1.8923 | - | |
| | 0.0059 | 590 | 1.8501 | - | |
| | 0.006 | 600 | 1.8499 | 0.5265 | |
| | 0.0061 | 610 | 1.8719 | - | |
| | 0.0062 | 620 | 1.8692 | - | |
| | 0.0063 | 630 | 1.8813 | - | |
| | 0.0064 | 640 | 1.8758 | - | |
| | 0.0065 | 650 | 1.8721 | - | |
| | 0.0066 | 660 | 1.8466 | - | |
| | 0.0067 | 670 | 1.8873 | - | |
| | 0.0068 | 680 | 1.8429 | - | |
| | 0.0069 | 690 | 1.8719 | - | |
| | 0.007 | 700 | 1.8478 | 0.5271 | |
| | 0.0071 | 710 | 1.8616 | - | |
| | 0.0072 | 720 | 1.8486 | - | |
| | 0.0073 | 730 | 1.8766 | - | |
| | 0.0074 | 740 | 1.8614 | - | |
| | 0.0075 | 750 | 1.8722 | - | |
| | 0.0076 | 760 | 1.829 | - | |
| | 0.0077 | 770 | 1.8341 | - | |
| | 0.0078 | 780 | 1.8304 | - | |
| | 0.0079 | 790 | 1.8494 | - | |
| | 0.008 | 800 | 1.838 | 0.5328 | |
| | 0.0081 | 810 | 1.851 | - | |
| | 0.0082 | 820 | 1.8359 | - | |
| | 0.0083 | 830 | 1.8528 | - | |
| | 0.0084 | 840 | 1.8295 | - | |
| | 0.0085 | 850 | 1.8337 | - | |
| | 0.0086 | 860 | 1.8072 | - | |
| | 0.0087 | 870 | 1.8068 | - | |
| | 0.0088 | 880 | 1.8102 | - | |
| | 0.0089 | 890 | 1.8199 | - | |
| | 0.009 | 900 | 1.8308 | 0.5324 | |
| | 0.0091 | 910 | 1.8155 | - | |
| | 0.0092 | 920 | 1.7918 | - | |
| | 0.0093 | 930 | 1.7969 | - | |
| | 0.0094 | 940 | 1.8054 | - | |
| | 0.0095 | 950 | 1.8032 | - | |
| | 0.0096 | 960 | 1.7972 | - | |
| | 0.0097 | 970 | 1.8063 | - | |
| | 0.0098 | 980 | 1.8227 | - | |
| | 0.0099 | 990 | 1.8111 | - | |
| | 0.01 | 1000 | 1.7909 | 0.5312 | |
| | 0.0101 | 1010 | 1.8105 | - | |
| | 0.0102 | 1020 | 1.7976 | - | |
| | 0.0103 | 1030 | 1.8117 | - | |
| | 0.0104 | 1040 | 1.7823 | - | |
| | 0.0105 | 1050 | 1.7967 | - | |
| | 0.0106 | 1060 | 1.7941 | - | |
| | 0.0107 | 1070 | 1.8054 | - | |
| | 0.0108 | 1080 | 1.7938 | - | |
| | 0.0109 | 1090 | 1.784 | - | |
| | 0.011 | 1100 | 1.8 | 0.5342 | |
| | 0.0111 | 1110 | 1.7895 | - | |
| | 0.0112 | 1120 | 1.805 | - | |
| | 0.0113 | 1130 | 1.8063 | - | |
| | 0.0114 | 1140 | 1.8011 | - | |
| | 0.0115 | 1150 | 1.7609 | - | |
| | 0.0116 | 1160 | 1.7658 | - | |
| | 0.0117 | 1170 | 1.7393 | - | |
| | 0.0118 | 1180 | 1.7716 | - | |
| | 0.0119 | 1190 | 1.7546 | - | |
| | 0.012 | 1200 | 1.776 | 0.5351 | |
| | 0.0121 | 1210 | 1.7735 | - | |
| | 0.0122 | 1220 | 1.7815 | - | |
| | 0.0123 | 1230 | 1.7805 | - | |
| | 0.0124 | 1240 | 1.7677 | - | |
| | 0.0125 | 1250 | 1.7697 | - | |
| | 0.0126 | 1260 | 1.7599 | - | |
| | 0.0127 | 1270 | 1.752 | - | |
| | 0.0128 | 1280 | 1.7591 | - | |
| | 0.0129 | 1290 | 1.7519 | - | |
| | 0.013 | 1300 | 1.7782 | 0.5344 | |
| | 0.0131 | 1310 | 1.741 | - | |
| | 0.0132 | 1320 | 1.7475 | - | |
| | 0.0133 | 1330 | 1.7903 | - | |
| | 0.0134 | 1340 | 1.7488 | - | |
| | 0.0135 | 1350 | 1.7426 | - | |
| | 0.0136 | 1360 | 1.7589 | - | |
| | 0.0137 | 1370 | 1.7327 | - | |
| | 0.0138 | 1380 | 1.7453 | - | |
| | 0.0139 | 1390 | 1.7453 | - | |
| | 0.014 | 1400 | 1.7347 | 0.5345 | |
| | 0.0141 | 1410 | 1.7467 | - | |
| | 0.0142 | 1420 | 1.743 | - | |
| | 0.0143 | 1430 | 1.7587 | - | |
| | 0.0144 | 1440 | 1.73 | - | |
| | 0.0145 | 1450 | 1.7453 | - | |
| | 0.0146 | 1460 | 1.7387 | - | |
| | 0.0147 | 1470 | 1.7431 | - | |
| | 0.0148 | 1480 | 1.7542 | - | |
| | 0.0149 | 1490 | 1.764 | - | |
| | 0.015 | 1500 | 1.7297 | 0.5376 | |
| | 0.0151 | 1510 | 1.7404 | - | |
| | 0.0152 | 1520 | 1.7497 | - | |
| | 0.0153 | 1530 | 1.756 | - | |
| | 0.0154 | 1540 | 1.7426 | - | |
| | 0.0155 | 1550 | 1.7246 | - | |
| | 0.0156 | 1560 | 1.7217 | - | |
| | 0.0157 | 1570 | 1.7319 | - | |
| | 0.0158 | 1580 | 1.7642 | - | |
| | 0.0159 | 1590 | 1.7345 | - | |
| | 0.016 | 1600 | 1.7312 | 0.5360 | |
| | 0.0161 | 1610 | 1.7394 | - | |
| | 0.0162 | 1620 | 1.7025 | - | |
| | 0.0163 | 1630 | 1.7431 | - | |
| | 0.0164 | 1640 | 1.7216 | - | |
| | 0.0165 | 1650 | 1.7378 | - | |
| | 0.0166 | 1660 | 1.7217 | - | |
| | 0.0167 | 1670 | 1.7424 | - | |
| | 0.0168 | 1680 | 1.7349 | - | |
| | 0.0169 | 1690 | 1.7191 | - | |
| | 0.017 | 1700 | 1.7288 | 0.5380 | |
| | 0.0171 | 1710 | 1.7173 | - | |
| | 0.0172 | 1720 | 1.7215 | - | |
| | 0.0173 | 1730 | 1.7108 | - | |
| | 0.0174 | 1740 | 1.6972 | - | |
| | 0.0175 | 1750 | 1.7177 | - | |
| | 0.0176 | 1760 | 1.7259 | - | |
| | 0.0177 | 1770 | 1.7103 | - | |
| | 0.0178 | 1780 | 1.7286 | - | |
| | 0.0179 | 1790 | 1.7127 | - | |
| | 0.018 | 1800 | 1.7102 | 0.5373 | |
| | 0.0181 | 1810 | 1.6951 | - | |
| | 0.0182 | 1820 | 1.7221 | - | |
| | 0.0183 | 1830 | 1.7135 | - | |
| | 0.0184 | 1840 | 1.6976 | - | |
| | 0.0185 | 1850 | 1.719 | - | |
| | 0.0186 | 1860 | 1.7182 | - | |
| | 0.0187 | 1870 | 1.7 | - | |
| | 0.0188 | 1880 | 1.7307 | - | |
| | 0.0189 | 1890 | 1.7024 | - | |
| | 0.019 | 1900 | 1.7092 | 0.5395 | |
| | 0.0191 | 1910 | 1.6905 | - | |
| | 0.0192 | 1920 | 1.7068 | - | |
| | 0.0193 | 1930 | 1.7053 | - | |
| | 0.0194 | 1940 | 1.7104 | - | |
| | 0.0195 | 1950 | 1.7068 | - | |
| | 0.0196 | 1960 | 1.7136 | - | |
| | 0.0197 | 1970 | 1.672 | - | |
| | 0.0198 | 1980 | 1.7229 | - | |
| | 0.0199 | 1990 | 1.6988 | - | |
| | 0.02 | 2000 | 1.7009 | 0.5376 | |
| | 0.0201 | 2010 | 1.6763 | - | |
| | 0.0202 | 2020 | 1.7004 | - | |
| | 0.0203 | 2030 | 1.6916 | - | |
| | 0.0204 | 2040 | 1.7155 | - | |
| | 0.0205 | 2050 | 1.7055 | - | |
| | 0.0206 | 2060 | 1.6892 | - | |
| | 0.0207 | 2070 | 1.7236 | - | |
| | 0.0208 | 2080 | 1.6971 | - | |
| | 0.0209 | 2090 | 1.7105 | - | |
| | 0.021 | 2100 | 1.6989 | 0.5398 | |
| | 0.0211 | 2110 | 1.7052 | - | |
| | 0.0212 | 2120 | 1.6936 | - | |
| | 0.0213 | 2130 | 1.7055 | - | |
| | 0.0214 | 2140 | 1.7038 | - | |
| | 0.0215 | 2150 | 1.6902 | - | |
| | 0.0216 | 2160 | 1.6808 | - | |
| | 0.0217 | 2170 | 1.6859 | - | |
| | 0.0218 | 2180 | 1.7133 | - | |
| | 0.0219 | 2190 | 1.6992 | - | |
| | 0.022 | 2200 | 1.6929 | 0.5389 | |
| | 0.0221 | 2210 | 1.6918 | - | |
| | 0.0222 | 2220 | 1.6732 | - | |
| | 0.0223 | 2230 | 1.7048 | - | |
| | 0.0224 | 2240 | 1.6682 | - | |
| | 0.0225 | 2250 | 1.6733 | - | |
| | 0.0226 | 2260 | 1.6655 | - | |
| | 0.0227 | 2270 | 1.6712 | - | |
| | 0.0228 | 2280 | 1.6707 | - | |
| | 0.0229 | 2290 | 1.668 | - | |
| | 0.023 | 2300 | 1.7152 | 0.5413 | |
| | 0.0231 | 2310 | 1.6816 | - | |
| | 0.0232 | 2320 | 1.6642 | - | |
| | 0.0233 | 2330 | 1.6809 | - | |
| | 0.0234 | 2340 | 1.6925 | - | |
| | 0.0235 | 2350 | 1.679 | - | |
| | 0.0236 | 2360 | 1.6857 | - | |
| | 0.0237 | 2370 | 1.6958 | - | |
| | 0.0238 | 2380 | 1.6943 | - | |
| | 0.0239 | 2390 | 1.6487 | - | |
| | 0.024 | 2400 | 1.6818 | 0.5352 | |
| | 0.0241 | 2410 | 1.6752 | - | |
| | 0.0242 | 2420 | 1.6707 | - | |
| | 0.0243 | 2430 | 1.6891 | - | |
| | 0.0244 | 2440 | 1.6691 | - | |
| | 0.0245 | 2450 | 1.6786 | - | |
| | 0.0246 | 2460 | 1.6831 | - | |
| | 0.0247 | 2470 | 1.6788 | - | |
| | 0.0248 | 2480 | 1.69 | - | |
| | 0.0249 | 2490 | 1.6811 | - | |
| | 0.025 | 2500 | 1.6584 | 0.5425 | |
| | 0.0251 | 2510 | 1.6523 | - | |
| | 0.0252 | 2520 | 1.6947 | - | |
| | 0.0253 | 2530 | 1.6542 | - | |
| | 0.0254 | 2540 | 1.6411 | - | |
| | 0.0255 | 2550 | 1.6729 | - | |
| | 0.0256 | 2560 | 1.6851 | - | |
| | 0.0257 | 2570 | 1.6642 | - | |
| | 0.0258 | 2580 | 1.6614 | - | |
| | 0.0259 | 2590 | 1.6684 | - | |
| | 0.026 | 2600 | 1.6676 | 0.5402 | |
| | 0.0261 | 2610 | 1.6678 | - | |
| | 0.0262 | 2620 | 1.6741 | - | |
| | 0.0263 | 2630 | 1.6769 | - | |
| | 0.0264 | 2640 | 1.6468 | - | |
| | 0.0265 | 2650 | 1.6688 | - | |
| | 0.0266 | 2660 | 1.6543 | - | |
| | 0.0267 | 2670 | 1.7078 | - | |
| | 0.0268 | 2680 | 1.6353 | - | |
| | 0.0269 | 2690 | 1.665 | - | |
| | 0.027 | 2700 | 1.6629 | 0.5441 | |
| | 0.0271 | 2710 | 1.6567 | - | |
| | 0.0272 | 2720 | 1.6395 | - | |
| | 0.0273 | 2730 | 1.6629 | - | |
| | 0.0274 | 2740 | 1.6852 | - | |
| | 0.0275 | 2750 | 1.6635 | - | |
| | 0.0276 | 2760 | 1.6765 | - | |
| | 0.0277 | 2770 | 1.6598 | - | |
| | 0.0278 | 2780 | 1.6547 | - | |
| | 0.0279 | 2790 | 1.6741 | - | |
| | 0.028 | 2800 | 1.6664 | 0.5423 | |
| | 0.0281 | 2810 | 1.6446 | - | |
| | 0.0282 | 2820 | 1.6662 | - | |
| | 0.0283 | 2830 | 1.6566 | - | |
| | 0.0284 | 2840 | 1.6397 | - | |
| | 0.0285 | 2850 | 1.6728 | - | |
| | 0.0286 | 2860 | 1.6592 | - | |
| | 0.0287 | 2870 | 1.657 | - | |
| | 0.0288 | 2880 | 1.6498 | - | |
| | 0.0289 | 2890 | 1.6375 | - | |
| | 0.029 | 2900 | 1.6437 | 0.5376 | |
| | 0.0291 | 2910 | 1.6309 | - | |
| | 0.0292 | 2920 | 1.6665 | - | |
| | 0.0293 | 2930 | 1.6666 | - | |
| | 0.0294 | 2940 | 1.6476 | - | |
| | 0.0295 | 2950 | 1.6604 | - | |
| | 0.0296 | 2960 | 1.6595 | - | |
| | 0.0297 | 2970 | 1.6476 | - | |
| | 0.0298 | 2980 | 1.6716 | - | |
| | 0.0299 | 2990 | 1.629 | - | |
| | 0.03 | 3000 | 1.6383 | 0.5425 | |
| | 0.0301 | 3010 | 1.6545 | - | |
| | 0.0302 | 3020 | 1.6477 | - | |
| | 0.0303 | 3030 | 1.6137 | - | |
| | 0.0304 | 3040 | 1.6581 | - | |
| | 0.0305 | 3050 | 1.6696 | - | |
| | 0.0306 | 3060 | 1.6316 | - | |
| | 0.0307 | 3070 | 1.6439 | - | |
| | 0.0308 | 3080 | 1.6537 | - | |
| | 0.0309 | 3090 | 1.6466 | - | |
| | 0.031 | 3100 | 1.6388 | 0.5416 | |
| | 0.0311 | 3110 | 1.6467 | - | |
| | 0.0312 | 3120 | 1.6235 | - | |
| | 0.0313 | 3130 | 1.6201 | - | |
| | 0.0314 | 3140 | 1.6342 | - | |
| | 0.0315 | 3150 | 1.6487 | - | |
| | 0.0316 | 3160 | 1.6531 | - | |
| | 0.0317 | 3170 | 1.6552 | - | |
| | 0.0318 | 3180 | 1.65 | - | |
| | 0.0319 | 3190 | 1.6457 | - | |
| | 0.032 | 3200 | 1.6448 | 0.5427 | |
| | 0.0321 | 3210 | 1.641 | - | |
| | 0.0322 | 3220 | 1.6612 | - | |
| | 0.0323 | 3230 | 1.6482 | - | |
| | 0.0324 | 3240 | 1.6362 | - | |
| | 0.0325 | 3250 | 1.6299 | - | |
| | 0.0326 | 3260 | 1.642 | - | |
| | 0.0327 | 3270 | 1.6443 | - | |
| | 0.0328 | 3280 | 1.6362 | - | |
| | 0.0329 | 3290 | 1.6234 | - | |
| | 0.033 | 3300 | 1.6294 | 0.5409 | |
| | 0.0331 | 3310 | 1.6278 | - | |
| | 0.0332 | 3320 | 1.6201 | - | |
| | 0.0333 | 3330 | 1.6059 | - | |
| | 0.0334 | 3340 | 1.6609 | - | |
| | 0.0335 | 3350 | 1.6263 | - | |
| | 0.0336 | 3360 | 1.6113 | - | |
| | 0.0337 | 3370 | 1.6403 | - | |
| | 0.0338 | 3380 | 1.6425 | - | |
| | 0.0339 | 3390 | 1.6274 | - | |
| | 0.034 | 3400 | 1.6364 | 0.5377 | |
| | 0.0341 | 3410 | 1.6252 | - | |
| | 0.0342 | 3420 | 1.6144 | - | |
| | 0.0343 | 3430 | 1.6334 | - | |
| | 0.0344 | 3440 | 1.6172 | - | |
| | 0.0345 | 3450 | 1.6293 | - | |
| | 0.0346 | 3460 | 1.6308 | - | |
| | 0.0347 | 3470 | 1.6309 | - | |
| | 0.0348 | 3480 | 1.6178 | - | |
| | 0.0349 | 3490 | 1.6426 | - | |
| | 0.035 | 3500 | 1.6178 | 0.5466 | |
| | 0.0351 | 3510 | 1.6516 | - | |
| | 0.0352 | 3520 | 1.6249 | - | |
| | 0.0353 | 3530 | 1.6355 | - | |
| | 0.0354 | 3540 | 1.6451 | - | |
| | 0.0355 | 3550 | 1.611 | - | |
| | 0.0356 | 3560 | 1.6265 | - | |
| | 0.0357 | 3570 | 1.633 | - | |
| | 0.0358 | 3580 | 1.6222 | - | |
| | 0.0359 | 3590 | 1.616 | - | |
| | 0.036 | 3600 | 1.6226 | 0.5412 | |
| | 0.0361 | 3610 | 1.6251 | - | |
| | 0.0362 | 3620 | 1.6148 | - | |
| | 0.0363 | 3630 | 1.6348 | - | |
| | 0.0364 | 3640 | 1.6421 | - | |
| | 0.0365 | 3650 | 1.5937 | - | |
| | 0.0366 | 3660 | 1.6469 | - | |
| | 0.0367 | 3670 | 1.6259 | - | |
| | 0.0368 | 3680 | 1.6216 | - | |
| | 0.0369 | 3690 | 1.6206 | - | |
| | 0.037 | 3700 | 1.6159 | 0.5422 | |
| | 0.0371 | 3710 | 1.6185 | - | |
| | 0.0372 | 3720 | 1.6414 | - | |
| | 0.0373 | 3730 | 1.6179 | - | |
| | 0.0374 | 3740 | 1.6002 | - | |
| | 0.0375 | 3750 | 1.6226 | - | |
| | 0.0376 | 3760 | 1.6305 | - | |
| | 0.0377 | 3770 | 1.6198 | - | |
| | 0.0378 | 3780 | 1.6184 | - | |
| | 0.0379 | 3790 | 1.6445 | - | |
| | 0.038 | 3800 | 1.6291 | 0.5396 | |
| | 0.0381 | 3810 | 1.6029 | - | |
| | 0.0382 | 3820 | 1.6039 | - | |
| | 0.0383 | 3830 | 1.6351 | - | |
| | 0.0384 | 3840 | 1.6238 | - | |
| | 0.0385 | 3850 | 1.6086 | - | |
| | 0.0386 | 3860 | 1.6435 | - | |
| | 0.0387 | 3870 | 1.5971 | - | |
| | 0.0388 | 3880 | 1.6114 | - | |
| | 0.0389 | 3890 | 1.6077 | - | |
| | 0.039 | 3900 | 1.584 | 0.5387 | |
| | 0.0391 | 3910 | 1.6086 | - | |
| | 0.0392 | 3920 | 1.6165 | - | |
| | 0.0393 | 3930 | 1.611 | - | |
| | 0.0394 | 3940 | 1.6102 | - | |
| | 0.0395 | 3950 | 1.5823 | - | |
| | 0.0396 | 3960 | 1.6146 | - | |
| | 0.0397 | 3970 | 1.5876 | - | |
| | 0.0398 | 3980 | 1.605 | - | |
| | 0.0399 | 3990 | 1.629 | - | |
| | 0.04 | 4000 | 1.6349 | 0.5434 | |
| | 0.0401 | 4010 | 1.6273 | - | |
| | 0.0402 | 4020 | 1.6252 | - | |
| | 0.0403 | 4030 | 1.6426 | - | |
| | 0.0404 | 4040 | 1.6003 | - | |
| | 0.0405 | 4050 | 1.6135 | - | |
| | 0.0406 | 4060 | 1.5962 | - | |
| | 0.0407 | 4070 | 1.6133 | - | |
| | 0.0408 | 4080 | 1.5951 | - | |
| | 0.0409 | 4090 | 1.6001 | - | |
| | 0.041 | 4100 | 1.5997 | 0.5432 | |
| | 0.0411 | 4110 | 1.6117 | - | |
| | 0.0412 | 4120 | 1.6178 | - | |
| | 0.0413 | 4130 | 1.6151 | - | |
| | 0.0414 | 4140 | 1.6253 | - | |
| | 0.0415 | 4150 | 1.613 | - | |
| | 0.0416 | 4160 | 1.6197 | - | |
| | 0.0417 | 4170 | 1.5976 | - | |
| | 0.0418 | 4180 | 1.6213 | - | |
| | 0.0419 | 4190 | 1.5855 | - | |
| | 0.042 | 4200 | 1.6117 | 0.5443 | |
| | 0.0421 | 4210 | 1.6309 | - | |
| | 0.0422 | 4220 | 1.6047 | - | |
| | 0.0423 | 4230 | 1.6154 | - | |
| | 0.0424 | 4240 | 1.6001 | - | |
| | 0.0425 | 4250 | 1.619 | - | |
| | 0.0426 | 4260 | 1.6137 | - | |
| | 0.0427 | 4270 | 1.5896 | - | |
| | 0.0428 | 4280 | 1.622 | - | |
| | 0.0429 | 4290 | 1.6047 | - | |
| | 0.043 | 4300 | 1.6177 | 0.5408 | |
| | 0.0431 | 4310 | 1.6021 | - | |
| | 0.0432 | 4320 | 1.6219 | - | |
| | 0.0433 | 4330 | 1.5891 | - | |
| | 0.0434 | 4340 | 1.6268 | - | |
| | 0.0435 | 4350 | 1.6135 | - | |
| | 0.0436 | 4360 | 1.5985 | - | |
| | 0.0437 | 4370 | 1.6017 | - | |
| | 0.0438 | 4380 | 1.6061 | - | |
| | 0.0439 | 4390 | 1.619 | - | |
| | 0.044 | 4400 | 1.5897 | 0.5449 | |
| | 0.0441 | 4410 | 1.6126 | - | |
| | 0.0442 | 4420 | 1.6143 | - | |
| | 0.0443 | 4430 | 1.6213 | - | |
| | 0.0444 | 4440 | 1.6026 | - | |
| | 0.0445 | 4450 | 1.5946 | - | |
| | 0.0446 | 4460 | 1.6191 | - | |
| | 0.0447 | 4470 | 1.5861 | - | |
| | 0.0448 | 4480 | 1.6075 | - | |
| | 0.0449 | 4490 | 1.5854 | - | |
| | 0.045 | 4500 | 1.5773 | 0.5399 | |
| | 0.0451 | 4510 | 1.6214 | - | |
| | 0.0452 | 4520 | 1.6005 | - | |
| | 0.0453 | 4530 | 1.579 | - | |
| | 0.0454 | 4540 | 1.6039 | - | |
| | 0.0455 | 4550 | 1.5778 | - | |
| | 0.0456 | 4560 | 1.6141 | - | |
| | 0.0457 | 4570 | 1.6088 | - | |
| | 0.0458 | 4580 | 1.5981 | - | |
| | 0.0459 | 4590 | 1.5863 | - | |
| | 0.046 | 4600 | 1.5873 | 0.5438 | |
| | 0.0461 | 4610 | 1.595 | - | |
| | 0.0462 | 4620 | 1.5976 | - | |
| | 0.0463 | 4630 | 1.5772 | - | |
| | 0.0464 | 4640 | 1.5724 | - | |
| | 0.0465 | 4650 | 1.5905 | - | |
| | 0.0466 | 4660 | 1.6146 | - | |
| | 0.0467 | 4670 | 1.6097 | - | |
| | 0.0468 | 4680 | 1.6069 | - | |
| | 0.0469 | 4690 | 1.5915 | - | |
| | 0.047 | 4700 | 1.5934 | 0.5478 | |
| | 0.0471 | 4710 | 1.6011 | - | |
| | 0.0472 | 4720 | 1.6017 | - | |
| | 0.0473 | 4730 | 1.6104 | - | |
| | 0.0474 | 4740 | 1.5903 | - | |
| | 0.0475 | 4750 | 1.6011 | - | |
| | 0.0476 | 4760 | 1.5801 | - | |
| | 0.0477 | 4770 | 1.5784 | - | |
| | 0.0478 | 4780 | 1.6015 | - | |
| | 0.0479 | 4790 | 1.591 | - | |
| | 0.048 | 4800 | 1.6159 | 0.5480 | |
| | 0.0481 | 4810 | 1.5994 | - | |
| | 0.0482 | 4820 | 1.5687 | - | |
| | 0.0483 | 4830 | 1.5986 | - | |
| | 0.0484 | 4840 | 1.6035 | - | |
| | 0.0485 | 4850 | 1.5855 | - | |
| | 0.0486 | 4860 | 1.5647 | - | |
| | 0.0487 | 4870 | 1.602 | - | |
| | 0.0488 | 4880 | 1.5805 | - | |
| | 0.0489 | 4890 | 1.5876 | - | |
| | 0.049 | 4900 | 1.6101 | 0.5447 | |
| | 0.0491 | 4910 | 1.5817 | - | |
| | 0.0492 | 4920 | 1.5869 | - | |
| | 0.0493 | 4930 | 1.5883 | - | |
| | 0.0494 | 4940 | 1.5646 | - | |
| | 0.0495 | 4950 | 1.571 | - | |
| | 0.0496 | 4960 | 1.5814 | - | |
| | 0.0497 | 4970 | 1.5863 | - | |
| | 0.0498 | 4980 | 1.5599 | - | |
| | 0.0499 | 4990 | 1.6061 | - | |
| | 0.05 | 5000 | 1.5672 | 0.5471 | |
| | 0.0501 | 5010 | 1.5785 | - | |
| | 0.0502 | 5020 | 1.5813 | - | |
| | 0.0503 | 5030 | 1.5932 | - | |
| | 0.0504 | 5040 | 1.5807 | - | |
| | 0.0505 | 5050 | 1.5704 | - | |
| | 0.0506 | 5060 | 1.5986 | - | |
| | 0.0507 | 5070 | 1.5812 | - | |
| | 0.0508 | 5080 | 1.5922 | - | |
| | 0.0509 | 5090 | 1.5789 | - | |
| | 0.051 | 5100 | 1.5829 | 0.5490 | |
| | 0.0511 | 5110 | 1.5634 | - | |
| | 0.0512 | 5120 | 1.5989 | - | |
| | 0.0513 | 5130 | 1.5928 | - | |
| | 0.0514 | 5140 | 1.5686 | - | |
| | 0.0515 | 5150 | 1.5548 | - | |
| | 0.0516 | 5160 | 1.599 | - | |
| | 0.0517 | 5170 | 1.5904 | - | |
| | 0.0518 | 5180 | 1.5898 | - | |
| | 0.0519 | 5190 | 1.578 | - | |
| | 0.052 | 5200 | 1.5721 | 0.5484 | |
| | 0.0521 | 5210 | 1.5729 | - | |
| | 0.0522 | 5220 | 1.5905 | - | |
| | 0.0523 | 5230 | 1.5629 | - | |
| | 0.0524 | 5240 | 1.5498 | - | |
| | 0.0525 | 5250 | 1.577 | - | |
| | 0.0526 | 5260 | 1.5995 | - | |
| | 0.0527 | 5270 | 1.5591 | - | |
| | 0.0528 | 5280 | 1.5772 | - | |
| | 0.0529 | 5290 | 1.5927 | - | |
| | 0.053 | 5300 | 1.5812 | 0.5467 | |
| | 0.0531 | 5310 | 1.5703 | - | |
| | 0.0532 | 5320 | 1.5885 | - | |
| | 0.0533 | 5330 | 1.5879 | - | |
| | 0.0534 | 5340 | 1.5884 | - | |
| | 0.0535 | 5350 | 1.5692 | - | |
| | 0.0536 | 5360 | 1.6 | - | |
| | 0.0537 | 5370 | 1.5907 | - | |
| | 0.0538 | 5380 | 1.5728 | - | |
| | 0.0539 | 5390 | 1.5876 | - | |
| | 0.054 | 5400 | 1.5661 | 0.5485 | |
| | 0.0541 | 5410 | 1.5525 | - | |
| | 0.0542 | 5420 | 1.5855 | - | |
| | 0.0543 | 5430 | 1.5752 | - | |
| | 0.0544 | 5440 | 1.5785 | - | |
| | 0.0545 | 5450 | 1.5825 | - | |
| | 0.0546 | 5460 | 1.5828 | - | |
| | 0.0547 | 5470 | 1.5743 | - | |
| | 0.0548 | 5480 | 1.5873 | - | |
| | 0.0549 | 5490 | 1.5909 | - | |
| | 0.055 | 5500 | 1.5811 | 0.5538 | |
| | 0.0551 | 5510 | 1.5871 | - | |
| | 0.0552 | 5520 | 1.5569 | - | |
| | 0.0553 | 5530 | 1.5748 | - | |
| | 0.0554 | 5540 | 1.6094 | - | |
| | 0.0555 | 5550 | 1.5623 | - | |
| | 0.0556 | 5560 | 1.5732 | - | |
| | 0.0557 | 5570 | 1.5724 | - | |
| | 0.0558 | 5580 | 1.5731 | - | |
| | 0.0559 | 5590 | 1.5873 | - | |
| | 0.056 | 5600 | 1.5913 | 0.5512 | |
| | 0.0561 | 5610 | 1.5916 | - | |
| | 0.0562 | 5620 | 1.5684 | - | |
| | 0.0563 | 5630 | 1.5526 | - | |
| | 0.0564 | 5640 | 1.5699 | - | |
| | 0.0565 | 5650 | 1.5732 | - | |
| | 0.0566 | 5660 | 1.5797 | - | |
| | 0.0567 | 5670 | 1.5723 | - | |
| | 0.0568 | 5680 | 1.5719 | - | |
| | 0.0569 | 5690 | 1.572 | - | |
| | 0.057 | 5700 | 1.5484 | 0.5491 | |
| | 0.0571 | 5710 | 1.5927 | - | |
| | 0.0572 | 5720 | 1.5614 | - | |
| | 0.0573 | 5730 | 1.5723 | - | |
| | 0.0574 | 5740 | 1.5675 | - | |
| | 0.0575 | 5750 | 1.5577 | - | |
| | 0.0576 | 5760 | 1.5801 | - | |
| | 0.0577 | 5770 | 1.5685 | - | |
| | 0.0578 | 5780 | 1.5802 | - | |
| | 0.0579 | 5790 | 1.5707 | - | |
| | 0.058 | 5800 | 1.588 | 0.5508 | |
| | 0.0581 | 5810 | 1.5642 | - | |
| | 0.0582 | 5820 | 1.5649 | - | |
| | 0.0583 | 5830 | 1.5724 | - | |
| | 0.0584 | 5840 | 1.5701 | - | |
| | 0.0585 | 5850 | 1.5731 | - | |
| | 0.0586 | 5860 | 1.5625 | - | |
| | 0.0587 | 5870 | 1.5822 | - | |
| | 0.0588 | 5880 | 1.5668 | - | |
| | 0.0589 | 5890 | 1.5742 | - | |
| | 0.059 | 5900 | 1.5413 | 0.5498 | |
| | 0.0591 | 5910 | 1.5535 | - | |
| | 0.0592 | 5920 | 1.5653 | - | |
| | 0.0593 | 5930 | 1.5604 | - | |
| | 0.0594 | 5940 | 1.5905 | - | |
| | 0.0595 | 5950 | 1.5689 | - | |
| | 0.0596 | 5960 | 1.5578 | - | |
| | 0.0597 | 5970 | 1.5616 | - | |
| | 0.0598 | 5980 | 1.5945 | - | |
| | 0.0599 | 5990 | 1.5721 | - | |
| | 0.06 | 6000 | 1.5701 | 0.5506 | |
| | 0.0601 | 6010 | 1.5486 | - | |
| | 0.0602 | 6020 | 1.5581 | - | |
| | 0.0603 | 6030 | 1.5801 | - | |
| | 0.0604 | 6040 | 1.5684 | - | |
| | 0.0605 | 6050 | 1.583 | - | |
| | 0.0606 | 6060 | 1.5885 | - | |
| | 0.0607 | 6070 | 1.5647 | - | |
| | 0.0608 | 6080 | 1.5826 | - | |
| | 0.0609 | 6090 | 1.5858 | - | |
| | 0.061 | 6100 | 1.5618 | 0.5501 | |
| | 0.0611 | 6110 | 1.5538 | - | |
| | 0.0612 | 6120 | 1.5721 | - | |
| | 0.0613 | 6130 | 1.5919 | - | |
| | 0.0614 | 6140 | 1.5693 | - | |
| | 0.0615 | 6150 | 1.5578 | - | |
| | 0.0616 | 6160 | 1.552 | - | |
| | 0.0617 | 6170 | 1.5584 | - | |
| | 0.0618 | 6180 | 1.5455 | - | |
| | 0.0619 | 6190 | 1.5421 | - | |
| | 0.062 | 6200 | 1.5551 | 0.5552 | |
| | 0.0621 | 6210 | 1.5706 | - | |
| | 0.0622 | 6220 | 1.5732 | - | |
| | 0.0623 | 6230 | 1.5619 | - | |
| | 0.0624 | 6240 | 1.5732 | - | |
| | 0.0625 | 6250 | 1.5554 | - | |
| | 0.0626 | 6260 | 1.5734 | - | |
| | 0.0627 | 6270 | 1.5747 | - | |
| | 0.0628 | 6280 | 1.571 | - | |
| | 0.0629 | 6290 | 1.5616 | - | |
| | 0.063 | 6300 | 1.5575 | 0.5546 | |
| | 0.0631 | 6310 | 1.5586 | - | |
| | 0.0632 | 6320 | 1.5857 | - | |
| | 0.0633 | 6330 | 1.5593 | - | |
| | 0.0634 | 6340 | 1.556 | - | |
| | 0.0635 | 6350 | 1.5558 | - | |
| | 0.0636 | 6360 | 1.5799 | - | |
| | 0.0637 | 6370 | 1.5772 | - | |
| | 0.0638 | 6380 | 1.5477 | - | |
| | 0.0639 | 6390 | 1.5823 | - | |
| | 0.064 | 6400 | 1.5462 | 0.5497 | |
| | 0.0641 | 6410 | 1.57 | - | |
| | 0.0642 | 6420 | 1.5551 | - | |
| | 0.0643 | 6430 | 1.5687 | - | |
| | 0.0644 | 6440 | 1.5703 | - | |
| | 0.0645 | 6450 | 1.579 | - | |
| | 0.0646 | 6460 | 1.5426 | - | |
| | 0.0647 | 6470 | 1.5816 | - | |
| | 0.0648 | 6480 | 1.5712 | - | |
| | 0.0649 | 6490 | 1.5746 | - | |
| | 0.065 | 6500 | 1.5648 | 0.5503 | |
| | 0.0651 | 6510 | 1.5443 | - | |
| | 0.0652 | 6520 | 1.5499 | - | |
| | 0.0653 | 6530 | 1.5648 | - | |
| | 0.0654 | 6540 | 1.5585 | - | |
| | 0.0655 | 6550 | 1.5694 | - | |
| | 0.0656 | 6560 | 1.5579 | - | |
| | 0.0657 | 6570 | 1.5762 | - | |
| | 0.0658 | 6580 | 1.5494 | - | |
| | 0.0659 | 6590 | 1.5752 | - | |
| | 0.066 | 6600 | 1.554 | 0.5493 | |
| | 0.0661 | 6610 | 1.5469 | - | |
| | 0.0662 | 6620 | 1.5532 | - | |
| | 0.0663 | 6630 | 1.535 | - | |
| | 0.0664 | 6640 | 1.5146 | - | |
| | 0.0665 | 6650 | 1.5528 | - | |
| | 0.0666 | 6660 | 1.5626 | - | |
| | 0.0667 | 6670 | 1.5476 | - | |
| | 0.0668 | 6680 | 1.5594 | - | |
| | 0.0669 | 6690 | 1.5441 | - | |
| | 0.067 | 6700 | 1.562 | 0.5504 | |
| | 0.0671 | 6710 | 1.5468 | - | |
| | 0.0672 | 6720 | 1.5462 | - | |
| | 0.0673 | 6730 | 1.5711 | - | |
| | 0.0674 | 6740 | 1.5659 | - | |
| | 0.0675 | 6750 | 1.5413 | - | |
| | 0.0676 | 6760 | 1.5497 | - | |
| | 0.0677 | 6770 | 1.5477 | - | |
| | 0.0678 | 6780 | 1.548 | - | |
| | 0.0679 | 6790 | 1.5639 | - | |
| | 0.068 | 6800 | 1.5676 | 0.5500 | |
| | 0.0681 | 6810 | 1.5647 | - | |
| | 0.0682 | 6820 | 1.5663 | - | |
| | 0.0683 | 6830 | 1.5462 | - | |
| | 0.0684 | 6840 | 1.5675 | - | |
| | 0.0685 | 6850 | 1.5563 | - | |
| | 0.0686 | 6860 | 1.5754 | - | |
| | 0.0687 | 6870 | 1.5513 | - | |
| | 0.0688 | 6880 | 1.5629 | - | |
| | 0.0689 | 6890 | 1.5558 | - | |
| | 0.069 | 6900 | 1.5536 | 0.5476 | |
| | 0.0691 | 6910 | 1.5618 | - | |
| | 0.0692 | 6920 | 1.5559 | - | |
| | 0.0693 | 6930 | 1.5661 | - | |
| | 0.0694 | 6940 | 1.5554 | - | |
| | 0.0695 | 6950 | 1.5501 | - | |
| | 0.0696 | 6960 | 1.581 | - | |
| | 0.0697 | 6970 | 1.5303 | - | |
| | 0.0698 | 6980 | 1.5432 | - | |
| | 0.0699 | 6990 | 1.5486 | - | |
| | 0.07 | 7000 | 1.5517 | 0.5495 | |
| | 0.0701 | 7010 | 1.5765 | - | |
| | 0.0702 | 7020 | 1.5536 | - | |
| | 0.0703 | 7030 | 1.5425 | - | |
| | 0.0704 | 7040 | 1.5593 | - | |
| | 0.0705 | 7050 | 1.548 | - | |
| | 0.0706 | 7060 | 1.5501 | - | |
| | 0.0707 | 7070 | 1.5331 | - | |
| | 0.0708 | 7080 | 1.567 | - | |
| | 0.0709 | 7090 | 1.5906 | - | |
| | 0.071 | 7100 | 1.5586 | 0.5474 | |
| | 0.0711 | 7110 | 1.5468 | - | |
| | 0.0712 | 7120 | 1.5503 | - | |
| | 0.0713 | 7130 | 1.5514 | - | |
| | 0.0714 | 7140 | 1.5584 | - | |
| | 0.0715 | 7150 | 1.5471 | - | |
| | 0.0716 | 7160 | 1.5462 | - | |
| | 0.0717 | 7170 | 1.5498 | - | |
| | 0.0718 | 7180 | 1.558 | - | |
| | 0.0719 | 7190 | 1.5495 | - | |
| | 0.072 | 7200 | 1.5585 | 0.5486 | |
| | 0.0721 | 7210 | 1.536 | - | |
| | 0.0722 | 7220 | 1.5493 | - | |
| | 0.0723 | 7230 | 1.5442 | - | |
| | 0.0724 | 7240 | 1.5409 | - | |
| | 0.0725 | 7250 | 1.536 | - | |
| | 0.0726 | 7260 | 1.5674 | - | |
| | 0.0727 | 7270 | 1.5595 | - | |
| | 0.0728 | 7280 | 1.5184 | - | |
| | 0.0729 | 7290 | 1.5694 | - | |
| | 0.073 | 7300 | 1.5593 | 0.5513 | |
| | 0.0731 | 7310 | 1.5766 | - | |
| | 0.0732 | 7320 | 1.5562 | - | |
| | 0.0733 | 7330 | 1.5872 | - | |
| | 0.0734 | 7340 | 1.5705 | - | |
| | 0.0735 | 7350 | 1.5622 | - | |
| | 0.0736 | 7360 | 1.5577 | - | |
| | 0.0737 | 7370 | 1.5422 | - | |
| | 0.0738 | 7380 | 1.5643 | - | |
| | 0.0739 | 7390 | 1.5713 | - | |
| | 0.074 | 7400 | 1.533 | 0.5500 | |
| | 0.0741 | 7410 | 1.5204 | - | |
| | 0.0742 | 7420 | 1.5445 | - | |
| | 0.0743 | 7430 | 1.5503 | - | |
| | 0.0744 | 7440 | 1.5243 | - | |
| | 0.0745 | 7450 | 1.5465 | - | |
| | 0.0746 | 7460 | 1.559 | - | |
| | 0.0747 | 7470 | 1.5493 | - | |
| | 0.0748 | 7480 | 1.5589 | - | |
| | 0.0749 | 7490 | 1.5637 | - | |
| | 0.075 | 7500 | 1.5547 | 0.5484 | |
| | 0.0751 | 7510 | 1.5296 | - | |
| | 0.0752 | 7520 | 1.5466 | - | |
| | 0.0753 | 7530 | 1.5259 | - | |
| | 0.0754 | 7540 | 1.5496 | - | |
| | 0.0755 | 7550 | 1.5273 | - | |
| | 0.0756 | 7560 | 1.5606 | - | |
| | 0.0757 | 7570 | 1.5408 | - | |
| | 0.0758 | 7580 | 1.5529 | - | |
| | 0.0759 | 7590 | 1.5481 | - | |
| | 0.076 | 7600 | 1.5571 | 0.5541 | |
| | 0.0761 | 7610 | 1.5322 | - | |
| | 0.0762 | 7620 | 1.567 | - | |
| | 0.0763 | 7630 | 1.5387 | - | |
| | 0.0764 | 7640 | 1.554 | - | |
| | 0.0765 | 7650 | 1.5284 | - | |
| | 0.0766 | 7660 | 1.5255 | - | |
| | 0.0767 | 7670 | 1.5421 | - | |
| | 0.0768 | 7680 | 1.5538 | - | |
| | 0.0769 | 7690 | 1.5309 | - | |
| | 0.077 | 7700 | 1.549 | 0.5519 | |
| | 0.0771 | 7710 | 1.5373 | - | |
| | 0.0772 | 7720 | 1.5315 | - | |
| | 0.0773 | 7730 | 1.5345 | - | |
| | 0.0774 | 7740 | 1.56 | - | |
| | 0.0775 | 7750 | 1.5678 | - | |
| | 0.0776 | 7760 | 1.5653 | - | |
| | 0.0777 | 7770 | 1.521 | - | |
| | 0.0778 | 7780 | 1.5377 | - | |
| | 0.0779 | 7790 | 1.5518 | - | |
| | 0.078 | 7800 | 1.5454 | 0.5490 | |
| | 0.0781 | 7810 | 1.5227 | - | |
| | 0.0782 | 7820 | 1.5604 | - | |
| | 0.0783 | 7830 | 1.5283 | - | |
| | 0.0784 | 7840 | 1.5448 | - | |
| | 0.0785 | 7850 | 1.5116 | - | |
| | 0.0786 | 7860 | 1.5223 | - | |
| | 0.0787 | 7870 | 1.5497 | - | |
| | 0.0788 | 7880 | 1.5417 | - | |
| | 0.0789 | 7890 | 1.5358 | - | |
| | 0.079 | 7900 | 1.5504 | 0.5544 | |
| | 0.0791 | 7910 | 1.516 | - | |
| | 0.0792 | 7920 | 1.5422 | - | |
| | 0.0793 | 7930 | 1.537 | - | |
| | 0.0794 | 7940 | 1.5479 | - | |
| | 0.0795 | 7950 | 1.5579 | - | |
| | 0.0796 | 7960 | 1.5369 | - | |
| | 0.0797 | 7970 | 1.5321 | - | |
| | 0.0798 | 7980 | 1.522 | - | |
| | 0.0799 | 7990 | 1.5389 | - | |
| | 0.08 | 8000 | 1.5274 | 0.5526 | |
| | 0.0801 | 8010 | 1.5537 | - | |
| | 0.0802 | 8020 | 1.5397 | - | |
| | 0.0803 | 8030 | 1.5671 | - | |
| | 0.0804 | 8040 | 1.5349 | - | |
| | 0.0805 | 8050 | 1.5407 | - | |
| | 0.0806 | 8060 | 1.5563 | - | |
| | 0.0807 | 8070 | 1.5581 | - | |
| | 0.0808 | 8080 | 1.5423 | - | |
| | 0.0809 | 8090 | 1.5148 | - | |
| | 0.081 | 8100 | 1.5557 | 0.5544 | |
| | 0.0811 | 8110 | 1.5404 | - | |
| | 0.0812 | 8120 | 1.5368 | - | |
| | 0.0813 | 8130 | 1.5161 | - | |
| | 0.0814 | 8140 | 1.5595 | - | |
| | 0.0815 | 8150 | 1.5493 | - | |
| | 0.0816 | 8160 | 1.5312 | - | |
| | 0.0817 | 8170 | 1.5326 | - | |
| | 0.0818 | 8180 | 1.5424 | - | |
| | 0.0819 | 8190 | 1.5325 | - | |
| | 0.082 | 8200 | 1.5458 | 0.5561 | |
| | 0.0821 | 8210 | 1.5397 | - | |
| | 0.0822 | 8220 | 1.5438 | - | |
| | 0.0823 | 8230 | 1.5237 | - | |
| | 0.0824 | 8240 | 1.5396 | - | |
| | 0.0825 | 8250 | 1.5365 | - | |
| | 0.0826 | 8260 | 1.5609 | - | |
| | 0.0827 | 8270 | 1.533 | - | |
| | 0.0828 | 8280 | 1.5367 | - | |
| | 0.0829 | 8290 | 1.5316 | - | |
| | 0.083 | 8300 | 1.5386 | 0.5528 | |
| | 0.0831 | 8310 | 1.5259 | - | |
| | 0.0832 | 8320 | 1.5205 | - | |
| | 0.0833 | 8330 | 1.5561 | - | |
| | 0.0834 | 8340 | 1.533 | - | |
| | 0.0835 | 8350 | 1.5684 | - | |
| | 0.0836 | 8360 | 1.5475 | - | |
| | 0.0837 | 8370 | 1.5195 | - | |
| | 0.0838 | 8380 | 1.5388 | - | |
| | 0.0839 | 8390 | 1.564 | - | |
| | 0.084 | 8400 | 1.5572 | 0.5490 | |
| | 0.0841 | 8410 | 1.5567 | - | |
| | 0.0842 | 8420 | 1.5383 | - | |
| | 0.0843 | 8430 | 1.5645 | - | |
| | 0.0844 | 8440 | 1.5499 | - | |
| | 0.0845 | 8450 | 1.5267 | - | |
| | 0.0846 | 8460 | 1.5538 | - | |
| | 0.0847 | 8470 | 1.5635 | - | |
| | 0.0848 | 8480 | 1.5365 | - | |
| | 0.0849 | 8490 | 1.5374 | - | |
| | 0.085 | 8500 | 1.5453 | 0.5507 | |
| | 0.0851 | 8510 | 1.5155 | - | |
| | 0.0852 | 8520 | 1.5505 | - | |
| | 0.0853 | 8530 | 1.5381 | - | |
| | 0.0854 | 8540 | 1.5337 | - | |
| | 0.0855 | 8550 | 1.5475 | - | |
| | 0.0856 | 8560 | 1.5421 | - | |
| | 0.0857 | 8570 | 1.5318 | - | |
| | 0.0858 | 8580 | 1.5404 | - | |
| | 0.0859 | 8590 | 1.5227 | - | |
| | 0.086 | 8600 | 1.5323 | 0.5498 | |
| | 0.0861 | 8610 | 1.5245 | - | |
| | 0.0862 | 8620 | 1.5435 | - | |
| | 0.0863 | 8630 | 1.5516 | - | |
| | 0.0864 | 8640 | 1.5394 | - | |
| | 0.0865 | 8650 | 1.5141 | - | |
| | 0.0866 | 8660 | 1.5289 | - | |
| | 0.0867 | 8670 | 1.5191 | - | |
| | 0.0868 | 8680 | 1.5349 | - | |
| | 0.0869 | 8690 | 1.5507 | - | |
| | 0.087 | 8700 | 1.5337 | 0.5532 | |
| | 0.0871 | 8710 | 1.5471 | - | |
| | 0.0872 | 8720 | 1.5267 | - | |
| | 0.0873 | 8730 | 1.5308 | - | |
| | 0.0874 | 8740 | 1.5576 | - | |
| | 0.0875 | 8750 | 1.5424 | - | |
| | 0.0876 | 8760 | 1.5518 | - | |
| | 0.0877 | 8770 | 1.5316 | - | |
| | 0.0878 | 8780 | 1.5369 | - | |
| | 0.0879 | 8790 | 1.5412 | - | |
| | 0.088 | 8800 | 1.5407 | 0.5487 | |
| | 0.0881 | 8810 | 1.5257 | - | |
| | 0.0882 | 8820 | 1.5318 | - | |
| | 0.0883 | 8830 | 1.5214 | - | |
| | 0.0884 | 8840 | 1.5321 | - | |
| | 0.0885 | 8850 | 1.5282 | - | |
| | 0.0886 | 8860 | 1.5262 | - | |
| | 0.0887 | 8870 | 1.5545 | - | |
| | 0.0888 | 8880 | 1.5407 | - | |
| | 0.0889 | 8890 | 1.564 | - | |
| | 0.089 | 8900 | 1.5287 | 0.5518 | |
| | 0.0891 | 8910 | 1.5353 | - | |
| | 0.0892 | 8920 | 1.5155 | - | |
| | 0.0893 | 8930 | 1.5416 | - | |
| | 0.0894 | 8940 | 1.546 | - | |
| | 0.0895 | 8950 | 1.5349 | - | |
| | 0.0896 | 8960 | 1.5203 | - | |
| | 0.0897 | 8970 | 1.5282 | - | |
| | 0.0898 | 8980 | 1.5111 | - | |
| | 0.0899 | 8990 | 1.5121 | - | |
| | 0.09 | 9000 | 1.5209 | 0.5519 | |
| | 0.0901 | 9010 | 1.5333 | - | |
| | 0.0902 | 9020 | 1.5305 | - | |
| | 0.0903 | 9030 | 1.5397 | - | |
| | 0.0904 | 9040 | 1.523 | - | |
| | 0.0905 | 9050 | 1.5446 | - | |
| | 0.0906 | 9060 | 1.5378 | - | |
| | 0.0907 | 9070 | 1.533 | - | |
| | 0.0908 | 9080 | 1.5271 | - | |
| | 0.0909 | 9090 | 1.5201 | - | |
| | 0.091 | 9100 | 1.526 | 0.5524 | |
| | 0.0911 | 9110 | 1.5307 | - | |
| | 0.0912 | 9120 | 1.572 | - | |
| | 0.0913 | 9130 | 1.5016 | - | |
| | 0.0914 | 9140 | 1.526 | - | |
| | 0.0915 | 9150 | 1.5326 | - | |
| | 0.0916 | 9160 | 1.5189 | - | |
| | 0.0917 | 9170 | 1.5298 | - | |
| | 0.0918 | 9180 | 1.5211 | - | |
| | 0.0919 | 9190 | 1.5237 | - | |
| | 0.092 | 9200 | 1.5121 | 0.5497 | |
| | 0.0921 | 9210 | 1.4938 | - | |
| | 0.0922 | 9220 | 1.5094 | - | |
| | 0.0923 | 9230 | 1.5265 | - | |
| | 0.0924 | 9240 | 1.5278 | - | |
| | 0.0925 | 9250 | 1.5255 | - | |
| | 0.0926 | 9260 | 1.4975 | - | |
| | 0.0927 | 9270 | 1.5117 | - | |
| | 0.0928 | 9280 | 1.5378 | - | |
| | 0.0929 | 9290 | 1.5248 | - | |
| | 0.093 | 9300 | 1.5222 | 0.5531 | |
| | 0.0931 | 9310 | 1.5056 | - | |
| | 0.0932 | 9320 | 1.5361 | - | |
| | 0.0933 | 9330 | 1.5426 | - | |
| | 0.0934 | 9340 | 1.5023 | - | |
| | 0.0935 | 9350 | 1.5056 | - | |
| | 0.0936 | 9360 | 1.5058 | - | |
| | 0.0937 | 9370 | 1.5299 | - | |
| | 0.0938 | 9380 | 1.5178 | - | |
| | 0.0939 | 9390 | 1.532 | - | |
| | 0.094 | 9400 | 1.5248 | 0.5577 | |
| | 0.0941 | 9410 | 1.5374 | - | |
| | 0.0942 | 9420 | 1.518 | - | |
| | 0.0943 | 9430 | 1.5299 | - | |
| | 0.0944 | 9440 | 1.5432 | - | |
| | 0.0945 | 9450 | 1.5164 | - | |
| | 0.0946 | 9460 | 1.5252 | - | |
| | 0.0947 | 9470 | 1.5327 | - | |
| | 0.0948 | 9480 | 1.5519 | - | |
| | 0.0949 | 9490 | 1.5077 | - | |
| | 0.095 | 9500 | 1.5322 | 0.5550 | |
| | 0.0951 | 9510 | 1.5358 | - | |
| | 0.0952 | 9520 | 1.5362 | - | |
| | 0.0953 | 9530 | 1.5262 | - | |
| | 0.0954 | 9540 | 1.5286 | - | |
| | 0.0955 | 9550 | 1.5205 | - | |
| | 0.0956 | 9560 | 1.5372 | - | |
| | 0.0957 | 9570 | 1.5248 | - | |
| | 0.0958 | 9580 | 1.5457 | - | |
| | 0.0959 | 9590 | 1.5087 | - | |
| | 0.096 | 9600 | 1.531 | 0.5523 | |
| | 0.0961 | 9610 | 1.5057 | - | |
| | 0.0962 | 9620 | 1.5295 | - | |
| | 0.0963 | 9630 | 1.52 | - | |
| | 0.0964 | 9640 | 1.5131 | - | |
| | 0.0965 | 9650 | 1.5272 | - | |
| | 0.0966 | 9660 | 1.5161 | - | |
| | 0.0967 | 9670 | 1.5178 | - | |
| | 0.0968 | 9680 | 1.5452 | - | |
| | 0.0969 | 9690 | 1.5216 | - | |
| | 0.097 | 9700 | 1.5471 | 0.5541 | |
| | 0.0971 | 9710 | 1.5233 | - | |
| | 0.0972 | 9720 | 1.5388 | - | |
| | 0.0973 | 9730 | 1.5173 | - | |
| | 0.0974 | 9740 | 1.5223 | - | |
| | 0.0975 | 9750 | 1.5193 | - | |
| | 0.0976 | 9760 | 1.5143 | - | |
| | 0.0977 | 9770 | 1.5245 | - | |
| | 0.0978 | 9780 | 1.5368 | - | |
| | 0.0979 | 9790 | 1.5237 | - | |
| | 0.098 | 9800 | 1.5077 | 0.5545 | |
| | 0.0981 | 9810 | 1.5276 | - | |
| | 0.0982 | 9820 | 1.5117 | - | |
| | 0.0983 | 9830 | 1.5174 | - | |
| | 0.0984 | 9840 | 1.5359 | - | |
| | 0.0985 | 9850 | 1.5145 | - | |
| | 0.0986 | 9860 | 1.5355 | - | |
| | 0.0987 | 9870 | 1.4959 | - | |
| | 0.0988 | 9880 | 1.5106 | - | |
| | 0.0989 | 9890 | 1.5567 | - | |
| | 0.099 | 9900 | 1.5102 | 0.5508 | |
| | 0.0991 | 9910 | 1.5255 | - | |
| | 0.0992 | 9920 | 1.4878 | - | |
| | 0.0993 | 9930 | 1.522 | - | |
| | 0.0994 | 9940 | 1.5296 | - | |
| | 0.0995 | 9950 | 1.4935 | - | |
| | 0.0996 | 9960 | 1.5081 | - | |
| | 0.0997 | 9970 | 1.5163 | - | |
| | 0.0998 | 9980 | 1.5267 | - | |
| | 0.0999 | 9990 | 1.5361 | - | |
| | 0.1 | 10000 | 1.5067 | 0.5510 | |
| |
| </details> |
| |
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.0 |
| - Transformers: 4.57.3 |
| - PyTorch: 2.9.0+cu128 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.4.2 |
| - Tokenizers: 0.22.1 |
| |
| ## 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", |
| } |
| ``` |
| |
| #### CachedMultipleNegativesRankingLoss |
| ```bibtex |
| @misc{gao2021scaling, |
| title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
| author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
| year={2021}, |
| eprint={2101.06983}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
| |
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