| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:30705 |
| - loss:NormalizedMultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-mpnet-base-v2 |
| widget: |
| - source_sentence: According to Mitchell et al (1997) ______, the perceived ability |
| of a stakeholder to influence organisational action, ________ whether the organisation |
| perceives the stakeholder's actions as desirable, proper and appropriate and ________, |
| the immediacy of attention the stakeholder claims require, determine stakeholder |
| ________. |
| sentences: |
| - Mr. Williams, upon filing a petition for bankruptcy, stated that he had a total |
| of only $2,240 in assets, with liabilities amounting to $5,600. How much money |
| can Mr. Johnson, a creditor, expect to receive if he has a claim of $1,725? |
| - What is the difference in cost between a 3-year policy and 3 one-year policies |
| for $22,000 worth of coverage, if the rate is $1.19 per $1,000? |
| - Williamsville has a total assessed valuation of property of $6,250,000.The town |
| requires $360,000 for educational purposesand $115,000 for health and welfare |
| needs. What isthe town's tax rate in dollars per $100. |
| - source_sentence: Suppose there is a 50-50 chance that an individual with logarithmic |
| utility from wealth and with a current wealth of $20,000 will suffer a loss of |
| $10,000 from a car accident. Insurance is competitively provided at actuarially |
| fair rates. Compute the utility if the individual buys full insurance. |
| sentences: |
| - How much must be invested in $1,000 5% bonds to have an annual income from interest |
| of $3,000 if the bonds sell at 74(7/8)? Assume a brokerage fee of $5 a bond. |
| - _______ locate morality beyond the sphere of rationality in an emotional 'moral |
| impulse' towards others. |
| - Of what is individual freedom to schedule work an example? |
| - source_sentence: Mr.Allynreceived a note for $1800 bearing interest at 6% for 90 |
| days, dated March 31 and due June 29. On April 20, his bank discounted the note |
| at 6%. What were the proceeds? |
| sentences: |
| - _______ such as bitcoin are becoming increasingly mainstream and have a whole |
| host of associated ethical implications, for example, they are______ and more |
| ______. However, they have also been used to engage in _______. |
| - The ABC Corporation has issued 200 bonds, each with a $1,000 face value, redeemable |
| at par after 15 years.In order toaccumulate the funds required for redemption, |
| ABC has establisheda fund of annual deposits earning 4% interest peryear. What |
| will be the principal in the fund at the endof 12 years?Round your answer to the |
| nearest dollar. |
| - What is the rate of return on a 5(1/2) % preferred stock having a par value of |
| $50 and selling for 52(3/4). Give answer to nearest (1 / 10)%. |
| - source_sentence: 'These store goods for moderate to long periods:' |
| sentences: |
| - Suppose the demand curve for oPads is given by $p=\frac{500-x}{10}, What is the |
| elasticity value of this demand function. |
| - 'Given the above statement, find what would happen to the free amount if the reserve |
| for contingencies to were to increase by $10,000.Retained Earnings: Reserved for |
| contingencies $25,000 Reserved for plant expansion $20,000 Total reserves $45,000 |
| Free retained earnings $50,000 Total retained earnings $95,000' |
| - Mr. Smith purchased a car for $4250. He was allowed $500 for his old car as a |
| down payment. The balance was paid in 25 equal monthly payments of $180. What |
| was the interest rate (nearest 10th of 1%)? Use the constant ratio formula. |
| - source_sentence: 'ABC Plumbing has the following current assets and liabilities: |
| Cash, $7,300; Marketable Securities, $14,200, Accounts Receivable, $2,120; Notes |
| Payable, $1,400; Accounts Payable, $1,850. Find the acid-test ratio for ABC, correct |
| to the nearest hundredth.' |
| sentences: |
| - What is the net price of a calculator list-priced at $100.00 and discounted at |
| 40% and 25%? |
| - InBrowningtown, water is sold to home owners by the cubic foot at the rate of |
| $15.31 for up to and including 3,600 cubic feet, and $.15 for each 100 cubic feet |
| over 3,600 cubic feet. Local taxes on water usage are 4%. If the Thomas family |
| recently received a bill for 35,700 cubic feet of water, how much were they charged? |
| - 'These are events when groups of sellers meet collectively with the key purpose |
| of attracting buyers:' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| - cosine_accuracy_threshold |
| - cosine_f1 |
| - cosine_f1_threshold |
| - cosine_precision |
| - cosine_recall |
| - cosine_ap |
| - cosine_mcc |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
| results: |
| - task: |
| type: binary-classification |
| name: Binary Classification |
| dataset: |
| name: domain val |
| type: domain-val |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9136996904024768 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.6191838979721069 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.911925175370226 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.5618531703948975 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.9183673469387755 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.9055727554179567 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.9693997091237498 |
| name: Cosine Ap |
| - type: cosine_mcc |
| value: 0.8251574837769956 |
| name: Cosine Mcc |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 --> |
| - **Maximum Sequence Length:** 384 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'}) |
| (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}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'ABC Plumbing has the following current assets and liabilities: Cash, $7,300; Marketable Securities, $14,200, Accounts Receivable, $2,120; Notes Payable, $1,400; Accounts Payable, $1,850. Find the acid-test ratio for ABC, correct to the nearest hundredth.', |
| 'What is the net price of a calculator list-priced at $100.00 and discounted at 40% and 25%?', |
| 'InBrowningtown, water is sold to home owners by the cubic foot at the rate of $15.31 for up to and including 3,600 cubic feet, and $.15 for each 100 cubic feet over 3,600 cubic feet. Local taxes on water usage are 4%. If the Thomas family recently received a bill for 35,700 cubic feet of water, how much were they charged?', |
| ] |
| 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, 0.9725, 0.9790], |
| # [0.9725, 1.0000, 0.9825], |
| # [0.9790, 0.9825, 1.0001]]) |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Binary Classification |
|
|
| * Dataset: `domain-val` |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------------|:-----------| |
| | cosine_accuracy | 0.9137 | |
| | cosine_accuracy_threshold | 0.6192 | |
| | cosine_f1 | 0.9119 | |
| | cosine_f1_threshold | 0.5619 | |
| | cosine_precision | 0.9184 | |
| | cosine_recall | 0.9056 | |
| | **cosine_ap** | **0.9694** | |
| | cosine_mcc | 0.8252 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 30,705 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 57.59 tokens</li><li>max: 282 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 57.45 tokens</li><li>max: 282 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>A furniture manufacturer wants to find out how many end tables he produced during a certain week. He knows that 8 employees produced 16 end tables each, 21 employees produced 23 each, 7 produced 27 each, and 4 produced 29 each, Find the total number of end 'tables produced during that week.</code> | <code>What does PEST stand for?</code> | |
| | <code>A furniture manufacturer wants to find out how many end tables he produced during a certain week. He knows that 8 employees produced 16 end tables each, 21 employees produced 23 each, 7 produced 27 each, and 4 produced 29 each, Find the total number of end 'tables produced during that week.</code> | <code>On August 4, a store purchased five sofas invoiced at $7,000, terms 2/10 , n/30 . The invoice was paid August 13. The store paid</code> | |
| | <code>A furniture manufacturer wants to find out how many end tables he produced during a certain week. He knows that 8 employees produced 16 end tables each, 21 employees produced 23 each, 7 produced 27 each, and 4 produced 29 each, Find the total number of end 'tables produced during that week.</code> | <code>$ .01(1/4) a share for stocks under $5 a share par value $ .02(1/2) a share for stocks from $5-$10 a share par value $ .03(3/4) a share for stocks from $10-$20 a share par value $ .05 a share for stocks over $20 a share par value Mr. Carr sold 300 shares of stock having a par value of $50 per share. What was the New York State transfer tax?</code> | |
| * Loss: <code>domain_encoder_ft.losses.NormalizedMultipleNegativesRankingLoss</code> |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 24 |
| - `num_train_epochs`: 10 |
| - `learning_rate`: 2e-05 |
| - `warmup_steps`: 0.1 |
| - `weight_decay`: 0.01 |
| - `bf16`: True |
| - `eval_strategy`: epoch |
| - `load_best_model_at_end`: True |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 24 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `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`: epoch |
| - `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`: True |
| - `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`: None |
| - `local_rank`: -1 |
| - `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 | domain-val_cosine_ap | |
| |:-------:|:--------:|:-------------:|:--------------------:| |
| | 0.0156 | 20 | 1.2836 | - | |
| | 0.0312 | 40 | 1.1540 | - | |
| | 0.0469 | 60 | 1.1903 | - | |
| | 0.0625 | 80 | 1.1989 | - | |
| | 0.0781 | 100 | 1.2550 | - | |
| | 0.0938 | 120 | 1.1286 | - | |
| | 0.1094 | 140 | 1.1315 | - | |
| | 0.125 | 160 | 1.0759 | - | |
| | 0.1406 | 180 | 0.8953 | - | |
| | 0.1562 | 200 | 0.8962 | - | |
| | 0.1719 | 220 | 0.8871 | - | |
| | 0.1875 | 240 | 0.8370 | - | |
| | 0.2031 | 260 | 0.8041 | - | |
| | 0.2188 | 280 | 0.6277 | - | |
| | 0.2344 | 300 | 0.6101 | - | |
| | 0.25 | 320 | 0.5950 | - | |
| | 0.2656 | 340 | 0.5216 | - | |
| | 0.2812 | 360 | 0.4675 | - | |
| | 0.2969 | 380 | 0.4305 | - | |
| | 0.3125 | 400 | 0.4532 | - | |
| | 0.3281 | 420 | 0.3666 | - | |
| | 0.3438 | 440 | 0.3723 | - | |
| | 0.3594 | 460 | 0.3453 | - | |
| | 0.375 | 480 | 0.3500 | - | |
| | 0.3906 | 500 | 0.3192 | - | |
| | 0.4062 | 520 | 0.3321 | - | |
| | 0.4219 | 540 | 0.3488 | - | |
| | 0.4375 | 560 | 0.3250 | - | |
| | 0.4531 | 580 | 0.3098 | - | |
| | 0.4688 | 600 | 0.3055 | - | |
| | 0.4844 | 620 | 0.2813 | - | |
| | 0.5 | 640 | 0.2846 | - | |
| | 0.5156 | 660 | 0.2823 | - | |
| | 0.5312 | 680 | 0.2812 | - | |
| | 0.5469 | 700 | 0.2627 | - | |
| | 0.5625 | 720 | 0.2721 | - | |
| | 0.5781 | 740 | 0.2726 | - | |
| | 0.5938 | 760 | 0.2653 | - | |
| | 0.6094 | 780 | 0.2627 | - | |
| | 0.625 | 800 | 0.2451 | - | |
| | 0.6406 | 820 | 0.2637 | - | |
| | 0.6562 | 840 | 0.2668 | - | |
| | 0.6719 | 860 | 0.2378 | - | |
| | 0.6875 | 880 | 0.2364 | - | |
| | 0.7031 | 900 | 0.2344 | - | |
| | 0.7188 | 920 | 0.2188 | - | |
| | 0.7344 | 940 | 0.2302 | - | |
| | 0.75 | 960 | 0.2237 | - | |
| | 0.7656 | 980 | 0.2228 | - | |
| | 0.7812 | 1000 | 0.2042 | - | |
| | 0.7969 | 1020 | 0.2001 | - | |
| | 0.8125 | 1040 | 0.2066 | - | |
| | 0.8281 | 1060 | 0.1777 | - | |
| | 0.8438 | 1080 | 0.2129 | - | |
| | 0.8594 | 1100 | 0.2227 | - | |
| | 0.875 | 1120 | 0.2038 | - | |
| | 0.8906 | 1140 | 0.2077 | - | |
| | 0.9062 | 1160 | 0.1987 | - | |
| | 0.9219 | 1180 | 0.2186 | - | |
| | 0.9375 | 1200 | 0.1873 | - | |
| | 0.9531 | 1220 | 0.1997 | - | |
| | 0.9688 | 1240 | 0.1670 | - | |
| | 0.9844 | 1260 | 0.1695 | - | |
| | 1.0 | 1280 | 0.1889 | 0.9535 | |
| | 1.0156 | 1300 | 0.1660 | - | |
| | 1.0312 | 1320 | 0.1624 | - | |
| | 1.0469 | 1340 | 0.1670 | - | |
| | 1.0625 | 1360 | 0.1693 | - | |
| | 1.0781 | 1380 | 0.1527 | - | |
| | 1.0938 | 1400 | 0.1505 | - | |
| | 1.1094 | 1420 | 0.1529 | - | |
| | 1.125 | 1440 | 0.1662 | - | |
| | 1.1406 | 1460 | 0.1521 | - | |
| | 1.1562 | 1480 | 0.1396 | - | |
| | 1.1719 | 1500 | 0.1603 | - | |
| | 1.1875 | 1520 | 0.1616 | - | |
| | 1.2031 | 1540 | 0.1438 | - | |
| | 1.2188 | 1560 | 0.1542 | - | |
| | 1.2344 | 1580 | 0.1377 | - | |
| | 1.25 | 1600 | 0.1512 | - | |
| | 1.2656 | 1620 | 0.1412 | - | |
| | 1.2812 | 1640 | 0.1661 | - | |
| | 1.2969 | 1660 | 0.1277 | - | |
| | 1.3125 | 1680 | 0.1344 | - | |
| | 1.3281 | 1700 | 0.1305 | - | |
| | 1.3438 | 1720 | 0.1464 | - | |
| | 1.3594 | 1740 | 0.1237 | - | |
| | 1.375 | 1760 | 0.1513 | - | |
| | 1.3906 | 1780 | 0.1355 | - | |
| | 1.4062 | 1800 | 0.1259 | - | |
| | 1.4219 | 1820 | 0.1200 | - | |
| | 1.4375 | 1840 | 0.1434 | - | |
| | 1.4531 | 1860 | 0.1437 | - | |
| | 1.4688 | 1880 | 0.1253 | - | |
| | 1.4844 | 1900 | 0.1275 | - | |
| | 1.5 | 1920 | 0.1237 | - | |
| | 1.5156 | 1940 | 0.1372 | - | |
| | 1.5312 | 1960 | 0.1231 | - | |
| | 1.5469 | 1980 | 0.1077 | - | |
| | 1.5625 | 2000 | 0.1132 | - | |
| | 1.5781 | 2020 | 0.1202 | - | |
| | 1.5938 | 2040 | 0.1175 | - | |
| | 1.6094 | 2060 | 0.1118 | - | |
| | 1.625 | 2080 | 0.1219 | - | |
| | 1.6406 | 2100 | 0.1097 | - | |
| | 1.6562 | 2120 | 0.1215 | - | |
| | 1.6719 | 2140 | 0.1302 | - | |
| | 1.6875 | 2160 | 0.1175 | - | |
| | 1.7031 | 2180 | 0.1097 | - | |
| | 1.7188 | 2200 | 0.1091 | - | |
| | 1.7344 | 2220 | 0.1104 | - | |
| | 1.75 | 2240 | 0.1237 | - | |
| | 1.7656 | 2260 | 0.1253 | - | |
| | 1.7812 | 2280 | 0.1115 | - | |
| | 1.7969 | 2300 | 0.1119 | - | |
| | 1.8125 | 2320 | 0.1089 | - | |
| | 1.8281 | 2340 | 0.1244 | - | |
| | 1.8438 | 2360 | 0.1030 | - | |
| | 1.8594 | 2380 | 0.1119 | - | |
| | 1.875 | 2400 | 0.1110 | - | |
| | 1.8906 | 2420 | 0.1033 | - | |
| | 1.9062 | 2440 | 0.1165 | - | |
| | 1.9219 | 2460 | 0.0986 | - | |
| | 1.9375 | 2480 | 0.0967 | - | |
| | 1.9531 | 2500 | 0.1081 | - | |
| | 1.9688 | 2520 | 0.1139 | - | |
| | 1.9844 | 2540 | 0.1129 | - | |
| | 2.0 | 2560 | 0.0945 | 0.9688 | |
| | 2.0156 | 2580 | 0.0917 | - | |
| | 2.0312 | 2600 | 0.0804 | - | |
| | 2.0469 | 2620 | 0.0901 | - | |
| | 2.0625 | 2640 | 0.0936 | - | |
| | 2.0781 | 2660 | 0.0944 | - | |
| | 2.0938 | 2680 | 0.1014 | - | |
| | 2.1094 | 2700 | 0.0986 | - | |
| | 2.125 | 2720 | 0.0926 | - | |
| | 2.1406 | 2740 | 0.0985 | - | |
| | 2.1562 | 2760 | 0.0919 | - | |
| | 2.1719 | 2780 | 0.0908 | - | |
| | 2.1875 | 2800 | 0.0810 | - | |
| | 2.2031 | 2820 | 0.0926 | - | |
| | 2.2188 | 2840 | 0.0872 | - | |
| | 2.2344 | 2860 | 0.0989 | - | |
| | 2.25 | 2880 | 0.0883 | - | |
| | 2.2656 | 2900 | 0.0885 | - | |
| | 2.2812 | 2920 | 0.1092 | - | |
| | 2.2969 | 2940 | 0.0962 | - | |
| | 2.3125 | 2960 | 0.0913 | - | |
| | 2.3281 | 2980 | 0.0825 | - | |
| | 2.3438 | 3000 | 0.0953 | - | |
| | 2.3594 | 3020 | 0.0869 | - | |
| | 2.375 | 3040 | 0.0896 | - | |
| | 2.3906 | 3060 | 0.0895 | - | |
| | 2.4062 | 3080 | 0.0934 | - | |
| | 2.4219 | 3100 | 0.0888 | - | |
| | 2.4375 | 3120 | 0.0929 | - | |
| | 2.4531 | 3140 | 0.0882 | - | |
| | 2.4688 | 3160 | 0.0907 | - | |
| | 2.4844 | 3180 | 0.0858 | - | |
| | 2.5 | 3200 | 0.0856 | - | |
| | 2.5156 | 3220 | 0.0851 | - | |
| | 2.5312 | 3240 | 0.0792 | - | |
| | 2.5469 | 3260 | 0.0934 | - | |
| | 2.5625 | 3280 | 0.0916 | - | |
| | 2.5781 | 3300 | 0.0864 | - | |
| | 2.5938 | 3320 | 0.0874 | - | |
| | 2.6094 | 3340 | 0.0995 | - | |
| | 2.625 | 3360 | 0.0810 | - | |
| | 2.6406 | 3380 | 0.0889 | - | |
| | 2.6562 | 3400 | 0.0805 | - | |
| | 2.6719 | 3420 | 0.0898 | - | |
| | 2.6875 | 3440 | 0.0861 | - | |
| | 2.7031 | 3460 | 0.0938 | - | |
| | 2.7188 | 3480 | 0.0729 | - | |
| | 2.7344 | 3500 | 0.0881 | - | |
| | 2.75 | 3520 | 0.0828 | - | |
| | 2.7656 | 3540 | 0.0887 | - | |
| | 2.7812 | 3560 | 0.0795 | - | |
| | 2.7969 | 3580 | 0.0870 | - | |
| | 2.8125 | 3600 | 0.0866 | - | |
| | 2.8281 | 3620 | 0.0896 | - | |
| | 2.8438 | 3640 | 0.0779 | - | |
| | 2.8594 | 3660 | 0.0867 | - | |
| | 2.875 | 3680 | 0.0842 | - | |
| | 2.8906 | 3700 | 0.0878 | - | |
| | 2.9062 | 3720 | 0.0821 | - | |
| | 2.9219 | 3740 | 0.0675 | - | |
| | 2.9375 | 3760 | 0.0857 | - | |
| | 2.9531 | 3780 | 0.0862 | - | |
| | 2.9688 | 3800 | 0.0822 | - | |
| | 2.9844 | 3820 | 0.0866 | - | |
| | 3.0 | 3840 | 0.0776 | 0.9674 | |
| | 3.0156 | 3860 | 0.0857 | - | |
| | 3.0312 | 3880 | 0.0765 | - | |
| | 3.0469 | 3900 | 0.0799 | - | |
| | 3.0625 | 3920 | 0.0807 | - | |
| | 3.0781 | 3940 | 0.0838 | - | |
| | 3.0938 | 3960 | 0.0824 | - | |
| | 3.1094 | 3980 | 0.0691 | - | |
| | 3.125 | 4000 | 0.0819 | - | |
| | 3.1406 | 4020 | 0.0871 | - | |
| | 3.1562 | 4040 | 0.0880 | - | |
| | 3.1719 | 4060 | 0.0823 | - | |
| | 3.1875 | 4080 | 0.0762 | - | |
| | 3.2031 | 4100 | 0.0776 | - | |
| | 3.2188 | 4120 | 0.0794 | - | |
| | 3.2344 | 4140 | 0.0877 | - | |
| | 3.25 | 4160 | 0.0934 | - | |
| | 3.2656 | 4180 | 0.0766 | - | |
| | 3.2812 | 4200 | 0.0797 | - | |
| | 3.2969 | 4220 | 0.0728 | - | |
| | 3.3125 | 4240 | 0.0801 | - | |
| | 3.3281 | 4260 | 0.0744 | - | |
| | 3.3438 | 4280 | 0.0746 | - | |
| | 3.3594 | 4300 | 0.0805 | - | |
| | 3.375 | 4320 | 0.0857 | - | |
| | 3.3906 | 4340 | 0.0924 | - | |
| | 3.4062 | 4360 | 0.0803 | - | |
| | 3.4219 | 4380 | 0.0752 | - | |
| | 3.4375 | 4400 | 0.0750 | - | |
| | 3.4531 | 4420 | 0.0753 | - | |
| | 3.4688 | 4440 | 0.0986 | - | |
| | 3.4844 | 4460 | 0.0820 | - | |
| | 3.5 | 4480 | 0.0830 | - | |
| | 3.5156 | 4500 | 0.0831 | - | |
| | 3.5312 | 4520 | 0.0774 | - | |
| | 3.5469 | 4540 | 0.1006 | - | |
| | 3.5625 | 4560 | 0.0771 | - | |
| | 3.5781 | 4580 | 0.0764 | - | |
| | 3.5938 | 4600 | 0.0843 | - | |
| | 3.6094 | 4620 | 0.0718 | - | |
| | 3.625 | 4640 | 0.0882 | - | |
| | 3.6406 | 4660 | 0.0869 | - | |
| | 3.6562 | 4680 | 0.0776 | - | |
| | 3.6719 | 4700 | 0.0829 | - | |
| | 3.6875 | 4720 | 0.0755 | - | |
| | 3.7031 | 4740 | 0.0882 | - | |
| | 3.7188 | 4760 | 0.0801 | - | |
| | 3.7344 | 4780 | 0.0935 | - | |
| | 3.75 | 4800 | 0.0873 | - | |
| | 3.7656 | 4820 | 0.0751 | - | |
| | 3.7812 | 4840 | 0.0793 | - | |
| | 3.7969 | 4860 | 0.0781 | - | |
| | 3.8125 | 4880 | 0.0793 | - | |
| | 3.8281 | 4900 | 0.0821 | - | |
| | 3.8438 | 4920 | 0.0920 | - | |
| | 3.8594 | 4940 | 0.0701 | - | |
| | 3.875 | 4960 | 0.0851 | - | |
| | 3.8906 | 4980 | 0.0785 | - | |
| | 3.9062 | 5000 | 0.0839 | - | |
| | 3.9219 | 5020 | 0.0700 | - | |
| | 3.9375 | 5040 | 0.0794 | - | |
| | 3.9531 | 5060 | 0.0820 | - | |
| | 3.9688 | 5080 | 0.0777 | - | |
| | 3.9844 | 5100 | 0.0834 | - | |
| | **4.0** | **5120** | **0.0911** | **0.9693** | |
| | 4.0156 | 5140 | 0.0833 | - | |
| | 4.0312 | 5160 | 0.0807 | - | |
| | 4.0469 | 5180 | 0.0748 | - | |
| | 4.0625 | 5200 | 0.0818 | - | |
| | 4.0781 | 5220 | 0.0793 | - | |
| | 4.0938 | 5240 | 0.0879 | - | |
| | 4.1094 | 5260 | 0.0825 | - | |
| | 4.125 | 5280 | 0.0786 | - | |
| | 4.1406 | 5300 | 0.0852 | - | |
| | 4.1562 | 5320 | 0.0813 | - | |
| | 4.1719 | 5340 | 0.0854 | - | |
| | 4.1875 | 5360 | 0.0886 | - | |
| | 4.2031 | 5380 | 0.0753 | - | |
| | 4.2188 | 5400 | 0.0743 | - | |
| | 4.2344 | 5420 | 0.0816 | - | |
| | 4.25 | 5440 | 0.0755 | - | |
| | 4.2656 | 5460 | 0.0712 | - | |
| | 4.2812 | 5480 | 0.0754 | - | |
| | 4.2969 | 5500 | 0.0731 | - | |
| | 4.3125 | 5520 | 0.0799 | - | |
| | 4.3281 | 5540 | 0.0743 | - | |
| | 4.3438 | 5560 | 0.0796 | - | |
| | 4.3594 | 5580 | 0.0731 | - | |
| | 4.375 | 5600 | 0.0743 | - | |
| | 4.3906 | 5620 | 0.0791 | - | |
| | 4.4062 | 5640 | 0.0863 | - | |
| | 4.4219 | 5660 | 0.0879 | - | |
| | 4.4375 | 5680 | 0.0775 | - | |
| | 4.4531 | 5700 | 0.0786 | - | |
| | 4.4688 | 5720 | 0.0820 | - | |
| | 4.4844 | 5740 | 0.0771 | - | |
| | 4.5 | 5760 | 0.0863 | - | |
| | 4.5156 | 5780 | 0.0870 | - | |
| | 4.5312 | 5800 | 0.0761 | - | |
| | 4.5469 | 5820 | 0.0837 | - | |
| | 4.5625 | 5840 | 0.0826 | - | |
| | 4.5781 | 5860 | 0.0721 | - | |
| | 4.5938 | 5880 | 0.0812 | - | |
| | 4.6094 | 5900 | 0.0693 | - | |
| | 4.625 | 5920 | 0.0789 | - | |
| | 4.6406 | 5940 | 0.0807 | - | |
| | 4.6562 | 5960 | 0.0772 | - | |
| | 4.6719 | 5980 | 0.0774 | - | |
| | 4.6875 | 6000 | 0.0833 | - | |
| | 4.7031 | 6020 | 0.0804 | - | |
| | 4.7188 | 6040 | 0.0851 | - | |
| | 4.7344 | 6060 | 0.0753 | - | |
| | 4.75 | 6080 | 0.0795 | - | |
| | 4.7656 | 6100 | 0.0826 | - | |
| | 4.7812 | 6120 | 0.0791 | - | |
| | 4.7969 | 6140 | 0.0758 | - | |
| | 4.8125 | 6160 | 0.0769 | - | |
| | 4.8281 | 6180 | 0.0831 | - | |
| | 4.8438 | 6200 | 0.0753 | - | |
| | 4.8594 | 6220 | 0.0739 | - | |
| | 4.875 | 6240 | 0.0777 | - | |
| | 4.8906 | 6260 | 0.0796 | - | |
| | 4.9062 | 6280 | 0.0786 | - | |
| | 4.9219 | 6300 | 0.0841 | - | |
| | 4.9375 | 6320 | 0.0838 | - | |
| | 4.9531 | 6340 | 0.0737 | - | |
| | 4.9688 | 6360 | 0.0844 | - | |
| | 4.9844 | 6380 | 0.0752 | - | |
| | 5.0 | 6400 | 0.0741 | 0.9711 | |
| | 5.0156 | 6420 | 0.0758 | - | |
| | 5.0312 | 6440 | 0.0760 | - | |
| | 5.0469 | 6460 | 0.0771 | - | |
| | 5.0625 | 6480 | 0.0788 | - | |
| | 5.0781 | 6500 | 0.0832 | - | |
| | 5.0938 | 6520 | 0.0816 | - | |
| | 5.1094 | 6540 | 0.0745 | - | |
| | 5.125 | 6560 | 0.0724 | - | |
| | 5.1406 | 6580 | 0.0721 | - | |
| | 5.1562 | 6600 | 0.0791 | - | |
| | 5.1719 | 6620 | 0.0720 | - | |
| | 5.1875 | 6640 | 0.0787 | - | |
| | 5.2031 | 6660 | 0.0776 | - | |
| | 5.2188 | 6680 | 0.0812 | - | |
| | 5.2344 | 6700 | 0.0743 | - | |
| | 5.25 | 6720 | 0.0806 | - | |
| | 5.2656 | 6740 | 0.0798 | - | |
| | 5.2812 | 6760 | 0.0729 | - | |
| | 5.2969 | 6780 | 0.0740 | - | |
| | 5.3125 | 6800 | 0.0882 | - | |
| | 5.3281 | 6820 | 0.0737 | - | |
| | 5.3438 | 6840 | 0.0734 | - | |
| | 5.3594 | 6860 | 0.0809 | - | |
| | 5.375 | 6880 | 0.0732 | - | |
| | 5.3906 | 6900 | 0.0849 | - | |
| | 5.4062 | 6920 | 0.0806 | - | |
| | 5.4219 | 6940 | 0.0712 | - | |
| | 5.4375 | 6960 | 0.0724 | - | |
| | 5.4531 | 6980 | 0.0782 | - | |
| | 5.4688 | 7000 | 0.0892 | - | |
| | 5.4844 | 7020 | 0.0746 | - | |
| | 5.5 | 7040 | 0.0774 | - | |
| | 5.5156 | 7060 | 0.0755 | - | |
| | 5.5312 | 7080 | 0.0762 | - | |
| | 5.5469 | 7100 | 0.0741 | - | |
| | 5.5625 | 7120 | 0.0836 | - | |
| | 5.5781 | 7140 | 0.0745 | - | |
| | 5.5938 | 7160 | 0.0757 | - | |
| | 5.6094 | 7180 | 0.0820 | - | |
| | 5.625 | 7200 | 0.0802 | - | |
| | 5.6406 | 7220 | 0.0767 | - | |
| | 5.6562 | 7240 | 0.0722 | - | |
| | 5.6719 | 7260 | 0.0861 | - | |
| | 5.6875 | 7280 | 0.0744 | - | |
| | 5.7031 | 7300 | 0.0785 | - | |
| | 5.7188 | 7320 | 0.0867 | - | |
| | 5.7344 | 7340 | 0.0781 | - | |
| | 5.75 | 7360 | 0.0773 | - | |
| | 5.7656 | 7380 | 0.0808 | - | |
| | 5.7812 | 7400 | 0.0776 | - | |
| | 5.7969 | 7420 | 0.0734 | - | |
| | 5.8125 | 7440 | 0.0779 | - | |
| | 5.8281 | 7460 | 0.0773 | - | |
| | 5.8438 | 7480 | 0.0803 | - | |
| | 5.8594 | 7500 | 0.0785 | - | |
| | 5.875 | 7520 | 0.0743 | - | |
| | 5.8906 | 7540 | 0.0811 | - | |
| | 5.9062 | 7560 | 0.0725 | - | |
| | 5.9219 | 7580 | 0.0805 | - | |
| | 5.9375 | 7600 | 0.0788 | - | |
| | 5.9531 | 7620 | 0.0877 | - | |
| | 5.9688 | 7640 | 0.0760 | - | |
| | 5.9844 | 7660 | 0.0713 | - | |
| | 6.0 | 7680 | 0.0784 | 0.9673 | |
| | 6.0156 | 7700 | 0.0771 | - | |
| | 6.0312 | 7720 | 0.0724 | - | |
| | 6.0469 | 7740 | 0.0753 | - | |
| | 6.0625 | 7760 | 0.0763 | - | |
| | 6.0781 | 7780 | 0.0757 | - | |
| | 6.0938 | 7800 | 0.0772 | - | |
| | 6.1094 | 7820 | 0.0716 | - | |
| | 6.125 | 7840 | 0.0715 | - | |
| | 6.1406 | 7860 | 0.0721 | - | |
| | 6.1562 | 7880 | 0.0741 | - | |
| | 6.1719 | 7900 | 0.0787 | - | |
| | 6.1875 | 7920 | 0.0729 | - | |
| | 6.2031 | 7940 | 0.0762 | - | |
| | 6.2188 | 7960 | 0.0738 | - | |
| | 6.2344 | 7980 | 0.0779 | - | |
| | 6.25 | 8000 | 0.0834 | - | |
| | 6.2656 | 8020 | 0.0785 | - | |
| | 6.2812 | 8040 | 0.0753 | - | |
| | 6.2969 | 8060 | 0.0734 | - | |
| | 6.3125 | 8080 | 0.0754 | - | |
| | 6.3281 | 8100 | 0.0848 | - | |
| | 6.3438 | 8120 | 0.0778 | - | |
| | 6.3594 | 8140 | 0.0748 | - | |
| | 6.375 | 8160 | 0.0696 | - | |
| | 6.3906 | 8180 | 0.0717 | - | |
| | 6.4062 | 8200 | 0.0768 | - | |
| | 6.4219 | 8220 | 0.0731 | - | |
| | 6.4375 | 8240 | 0.0744 | - | |
| | 6.4531 | 8260 | 0.0748 | - | |
| | 6.4688 | 8280 | 0.0729 | - | |
| | 6.4844 | 8300 | 0.0794 | - | |
| | 6.5 | 8320 | 0.0776 | - | |
| | 6.5156 | 8340 | 0.0774 | - | |
| | 6.5312 | 8360 | 0.0722 | - | |
| | 6.5469 | 8380 | 0.0761 | - | |
| | 6.5625 | 8400 | 0.0766 | - | |
| | 6.5781 | 8420 | 0.0788 | - | |
| | 6.5938 | 8440 | 0.0742 | - | |
| | 6.6094 | 8460 | 0.0741 | - | |
| | 6.625 | 8480 | 0.0780 | - | |
| | 6.6406 | 8500 | 0.0746 | - | |
| | 6.6562 | 8520 | 0.0723 | - | |
| | 6.6719 | 8540 | 0.0764 | - | |
| | 6.6875 | 8560 | 0.0694 | - | |
| | 6.7031 | 8580 | 0.0815 | - | |
| | 6.7188 | 8600 | 0.0824 | - | |
| | 6.7344 | 8620 | 0.0758 | - | |
| | 6.75 | 8640 | 0.0742 | - | |
| | 6.7656 | 8660 | 0.0779 | - | |
| | 6.7812 | 8680 | 0.0743 | - | |
| | 6.7969 | 8700 | 0.0746 | - | |
| | 6.8125 | 8720 | 0.0755 | - | |
| | 6.8281 | 8740 | 0.0688 | - | |
| | 6.8438 | 8760 | 0.0843 | - | |
| | 6.8594 | 8780 | 0.0801 | - | |
| | 6.875 | 8800 | 0.0692 | - | |
| | 6.8906 | 8820 | 0.0731 | - | |
| | 6.9062 | 8840 | 0.0781 | - | |
| | 6.9219 | 8860 | 0.0760 | - | |
| | 6.9375 | 8880 | 0.0745 | - | |
| | 6.9531 | 8900 | 0.0726 | - | |
| | 6.9688 | 8920 | 0.0739 | - | |
| | 6.9844 | 8940 | 0.0860 | - | |
| | 7.0 | 8960 | 0.0721 | 0.9697 | |
| | 7.0156 | 8980 | 0.0788 | - | |
| | 7.0312 | 9000 | 0.0714 | - | |
| | 7.0469 | 9020 | 0.0724 | - | |
| | 7.0625 | 9040 | 0.0726 | - | |
| | 7.0781 | 9060 | 0.0742 | - | |
| | 7.0938 | 9080 | 0.0731 | - | |
| | 7.1094 | 9100 | 0.0756 | - | |
| | 7.125 | 9120 | 0.0766 | - | |
| | 7.1406 | 9140 | 0.0730 | - | |
| | 7.1562 | 9160 | 0.0761 | - | |
| | 7.1719 | 9180 | 0.0705 | - | |
| | 7.1875 | 9200 | 0.0692 | - | |
| | 7.2031 | 9220 | 0.0707 | - | |
| | 7.2188 | 9240 | 0.0776 | - | |
| | 7.2344 | 9260 | 0.0765 | - | |
| | 7.25 | 9280 | 0.0675 | - | |
| | 7.2656 | 9300 | 0.0677 | - | |
| | 7.2812 | 9320 | 0.0721 | - | |
| | 7.2969 | 9340 | 0.0717 | - | |
| | 7.3125 | 9360 | 0.0692 | - | |
| | 7.3281 | 9380 | 0.0780 | - | |
| | 7.3438 | 9400 | 0.0748 | - | |
| | 7.3594 | 9420 | 0.0804 | - | |
| | 7.375 | 9440 | 0.0781 | - | |
| | 7.3906 | 9460 | 0.0733 | - | |
| | 7.4062 | 9480 | 0.0784 | - | |
| | 7.4219 | 9500 | 0.0773 | - | |
| | 7.4375 | 9520 | 0.0713 | - | |
| | 7.4531 | 9540 | 0.0760 | - | |
| | 7.4688 | 9560 | 0.0705 | - | |
| | 7.4844 | 9580 | 0.0729 | - | |
| | 7.5 | 9600 | 0.0708 | - | |
| | 7.5156 | 9620 | 0.0788 | - | |
| | 7.5312 | 9640 | 0.0734 | - | |
| | 7.5469 | 9660 | 0.0768 | - | |
| | 7.5625 | 9680 | 0.0716 | - | |
| | 7.5781 | 9700 | 0.0730 | - | |
| | 7.5938 | 9720 | 0.0744 | - | |
| | 7.6094 | 9740 | 0.0677 | - | |
| | 7.625 | 9760 | 0.0766 | - | |
| | 7.6406 | 9780 | 0.0790 | - | |
| | 7.6562 | 9800 | 0.0764 | - | |
| | 7.6719 | 9820 | 0.0770 | - | |
| | 7.6875 | 9840 | 0.0792 | - | |
| | 7.7031 | 9860 | 0.0727 | - | |
| | 7.7188 | 9880 | 0.0780 | - | |
| | 7.7344 | 9900 | 0.0702 | - | |
| | 7.75 | 9920 | 0.0779 | - | |
| | 7.7656 | 9940 | 0.0701 | - | |
| | 7.7812 | 9960 | 0.0805 | - | |
| | 7.7969 | 9980 | 0.0758 | - | |
| | 7.8125 | 10000 | 0.0688 | - | |
| | 7.8281 | 10020 | 0.0706 | - | |
| | 7.8438 | 10040 | 0.0739 | - | |
| | 7.8594 | 10060 | 0.0765 | - | |
| | 7.875 | 10080 | 0.0721 | - | |
| | 7.8906 | 10100 | 0.0803 | - | |
| | 7.9062 | 10120 | 0.0714 | - | |
| | 7.9219 | 10140 | 0.0758 | - | |
| | 7.9375 | 10160 | 0.0708 | - | |
| | 7.9531 | 10180 | 0.0748 | - | |
| | 7.9688 | 10200 | 0.0795 | - | |
| | 7.9844 | 10220 | 0.0695 | - | |
| | 8.0 | 10240 | 0.0834 | 0.9700 | |
| | 8.0156 | 10260 | 0.0695 | - | |
| | 8.0312 | 10280 | 0.0696 | - | |
| | 8.0469 | 10300 | 0.0712 | - | |
| | 8.0625 | 10320 | 0.0680 | - | |
| | 8.0781 | 10340 | 0.0687 | - | |
| | 8.0938 | 10360 | 0.0728 | - | |
| | 8.1094 | 10380 | 0.0725 | - | |
| | 8.125 | 10400 | 0.0678 | - | |
| | 8.1406 | 10420 | 0.0645 | - | |
| | 8.1562 | 10440 | 0.0640 | - | |
| | 8.1719 | 10460 | 0.0717 | - | |
| | 8.1875 | 10480 | 0.0745 | - | |
| | 8.2031 | 10500 | 0.0747 | - | |
| | 8.2188 | 10520 | 0.0769 | - | |
| | 8.2344 | 10540 | 0.0725 | - | |
| | 8.25 | 10560 | 0.0720 | - | |
| | 8.2656 | 10580 | 0.0685 | - | |
| | 8.2812 | 10600 | 0.0742 | - | |
| | 8.2969 | 10620 | 0.0670 | - | |
| | 8.3125 | 10640 | 0.0692 | - | |
| | 8.3281 | 10660 | 0.0736 | - | |
| | 8.3438 | 10680 | 0.0722 | - | |
| | 8.3594 | 10700 | 0.0642 | - | |
| | 8.375 | 10720 | 0.0703 | - | |
| | 8.3906 | 10740 | 0.0744 | - | |
| | 8.4062 | 10760 | 0.0671 | - | |
| | 8.4219 | 10780 | 0.0723 | - | |
| | 8.4375 | 10800 | 0.0732 | - | |
| | 8.4531 | 10820 | 0.0765 | - | |
| | 8.4688 | 10840 | 0.0711 | - | |
| | 8.4844 | 10860 | 0.0746 | - | |
| | 8.5 | 10880 | 0.0730 | - | |
| | 8.5156 | 10900 | 0.0758 | - | |
| | 8.5312 | 10920 | 0.0698 | - | |
| | 8.5469 | 10940 | 0.0759 | - | |
| | 8.5625 | 10960 | 0.0737 | - | |
| | 8.5781 | 10980 | 0.0761 | - | |
| | 8.5938 | 11000 | 0.0726 | - | |
| | 8.6094 | 11020 | 0.0798 | - | |
| | 8.625 | 11040 | 0.0722 | - | |
| | 8.6406 | 11060 | 0.0721 | - | |
| | 8.6562 | 11080 | 0.0777 | - | |
| | 8.6719 | 11100 | 0.0719 | - | |
| | 8.6875 | 11120 | 0.0747 | - | |
| | 8.7031 | 11140 | 0.0700 | - | |
| | 8.7188 | 11160 | 0.0741 | - | |
| | 8.7344 | 11180 | 0.0731 | - | |
| | 8.75 | 11200 | 0.0701 | - | |
| | 8.7656 | 11220 | 0.0704 | - | |
| | 8.7812 | 11240 | 0.0675 | - | |
| | 8.7969 | 11260 | 0.0707 | - | |
| | 8.8125 | 11280 | 0.0712 | - | |
| | 8.8281 | 11300 | 0.0667 | - | |
| | 8.8438 | 11320 | 0.0679 | - | |
| | 8.8594 | 11340 | 0.0752 | - | |
| | 8.875 | 11360 | 0.0705 | - | |
| | 8.8906 | 11380 | 0.0713 | - | |
| | 8.9062 | 11400 | 0.0746 | - | |
| | 8.9219 | 11420 | 0.0737 | - | |
| | 8.9375 | 11440 | 0.0735 | - | |
| | 8.9531 | 11460 | 0.0721 | - | |
| | 8.9688 | 11480 | 0.0767 | - | |
| | 8.9844 | 11500 | 0.0706 | - | |
| | 9.0 | 11520 | 0.0707 | 0.9696 | |
| | 9.0156 | 11540 | 0.0701 | - | |
| | 9.0312 | 11560 | 0.0661 | - | |
| | 9.0469 | 11580 | 0.0733 | - | |
| | 9.0625 | 11600 | 0.0690 | - | |
| | 9.0781 | 11620 | 0.0720 | - | |
| | 9.0938 | 11640 | 0.0664 | - | |
| | 9.1094 | 11660 | 0.0742 | - | |
| | 9.125 | 11680 | 0.0659 | - | |
| | 9.1406 | 11700 | 0.0700 | - | |
| | 9.1562 | 11720 | 0.0693 | - | |
| | 9.1719 | 11740 | 0.0704 | - | |
| | 9.1875 | 11760 | 0.0683 | - | |
| | 9.2031 | 11780 | 0.0731 | - | |
| | 9.2188 | 11800 | 0.0688 | - | |
| | 9.2344 | 11820 | 0.0732 | - | |
| | 9.25 | 11840 | 0.0657 | - | |
| | 9.2656 | 11860 | 0.0688 | - | |
| | 9.2812 | 11880 | 0.0673 | - | |
| | 9.2969 | 11900 | 0.0705 | - | |
| | 9.3125 | 11920 | 0.0693 | - | |
| | 9.3281 | 11940 | 0.0663 | - | |
| | 9.3438 | 11960 | 0.0662 | - | |
| | 9.3594 | 11980 | 0.0688 | - | |
| | 9.375 | 12000 | 0.0728 | - | |
| | 9.3906 | 12020 | 0.0666 | - | |
| | 9.4062 | 12040 | 0.0698 | - | |
| | 9.4219 | 12060 | 0.0666 | - | |
| | 9.4375 | 12080 | 0.0731 | - | |
| | 9.4531 | 12100 | 0.0765 | - | |
| | 9.4688 | 12120 | 0.0744 | - | |
| | 9.4844 | 12140 | 0.0697 | - | |
| | 9.5 | 12160 | 0.0742 | - | |
| | 9.5156 | 12180 | 0.0710 | - | |
| | 9.5312 | 12200 | 0.0668 | - | |
| | 9.5469 | 12220 | 0.0709 | - | |
| | 9.5625 | 12240 | 0.0720 | - | |
| | 9.5781 | 12260 | 0.0700 | - | |
| | 9.5938 | 12280 | 0.0750 | - | |
| | 9.6094 | 12300 | 0.0695 | - | |
| | 9.625 | 12320 | 0.0706 | - | |
| | 9.6406 | 12340 | 0.0652 | - | |
| | 9.6562 | 12360 | 0.0721 | - | |
| | 9.6719 | 12380 | 0.0688 | - | |
| | 9.6875 | 12400 | 0.0663 | - | |
| | 9.7031 | 12420 | 0.0655 | - | |
| | 9.7188 | 12440 | 0.0707 | - | |
| | 9.7344 | 12460 | 0.0695 | - | |
| | 9.75 | 12480 | 0.0643 | - | |
| | 9.7656 | 12500 | 0.0657 | - | |
| | 9.7812 | 12520 | 0.0683 | - | |
| | 9.7969 | 12540 | 0.0694 | - | |
| | 9.8125 | 12560 | 0.0732 | - | |
| | 9.8281 | 12580 | 0.0700 | - | |
| | 9.8438 | 12600 | 0.0694 | - | |
| | 9.8594 | 12620 | 0.0700 | - | |
| | 9.875 | 12640 | 0.0711 | - | |
| | 9.8906 | 12660 | 0.0758 | - | |
| | 9.9062 | 12680 | 0.0686 | - | |
| | 9.9219 | 12700 | 0.0687 | - | |
| | 9.9375 | 12720 | 0.0771 | - | |
| | 9.9531 | 12740 | 0.0669 | - | |
| | 9.9688 | 12760 | 0.0720 | - | |
| | 9.9844 | 12780 | 0.0729 | - | |
| | 10.0 | 12800 | 0.0698 | 0.9694 | |
|
|
| * The bold row denotes the saved checkpoint. |
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.12.3 |
| - Sentence Transformers: 5.3.0 |
| - Transformers: 5.3.0 |
| - PyTorch: 2.10.0a0+a36e1d39eb.nv26.01.42222806 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.4.2 |
| - 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", |
| } |
| ``` |
|
|
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