MatchMiner-AI-0526
Collection
May 2026 MatchMiner-AI updates ('v22'), incorporating switch to Gemma-4-31B-IT , and repetition penalty adjustment to improve patient summarization. • 5 items • Updated
How to use ksg-dfci/TrialSpace-0526 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ksg-dfci/TrialSpace-0526")
sentences = [
"Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 39\nSex: Female\nCancer type: T-cell acute lymphoblastic leukemia (T-ALL)\nHistology: T-cell lymphoblasts\nCurrent extent: Advanced (bone marrow, peripheral blood, cervical lymph nodes, and spleen)\nBiomarkers: CD2 positive, CD3 positive (cytoplasmic and surface), CD5 positive, CD7 positive, NOTCH1 p.T1851I, PTEN homozygous loss, TP53 p.R175H, SETD2 p.V123L\nTreatment history:\n2015-09-18 to 2017-02-11: vincristine, prednisone, and cytarabine; best response complete remission\n2017: allogeneic hematopoietic stem cell transplant\n2019-06-14 to 2020-11-09: cladribine; best response partial response (bone marrow blasts decreased from 12 percent to 6 percent), now refractory",
"Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 6 years. Sex allowed: Both. Cancer type allowed: Central nervous system cancer. Histology allowed: Group 3 medulloblastoma. Cancer burden allowed: Metastatic disease allowed. Prior treatment required: NA. Prior treatment excluded: Radiotherapy and chemotherapy (excluding corticosteroids). Biomarkers required: NA. Biomarkers excluded: NA.",
"Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 6 years. Sex allowed: Both. Cancer type allowed: Central nervous system cancer. Histology allowed: Central nervous system embryonal tumor with rhabdoid features. Cancer burden allowed: Metastatic disease allowed. Prior treatment required: NA. Prior treatment excluded: Radiotherapy and chemotherapy (excluding corticosteroids). Biomarkers required: INI-1 intact. Biomarkers excluded: NA.",
"Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 29 years. Sex allowed: both. Cancer type allowed: T-cell lymphoma. Histology allowed: T-cell lymphoma. Cancer burden allowed: Second or greater relapse or first relapse post-allogeneic hematopoietic stem cell transplant or primary refractory disease or relapsed and refractory disease. Prior treatment required: NA. Prior treatment excluded: CD7 targeted therapy. Biomarkers required: CD7 expression on T-cell lymphoma blasts (at least 90% of blasts positive by flow cytometry or immunohistochemistry) assessed during screening. Biomarkers excluded: NA."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]This is a sentence-transformers model trained on the mnri_dataset and contrastive_dataset datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
(1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True})
(2): Normalize({})
)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 12\nSex: Male\nCancer type: Mediastinal mixed germ cell tumor\nHistology: Embryonal carcinoma, choriocarcinoma, and yolk sac tumor\nCurrent extent: Metastatic (mediastinal mass, mediastinal lymphadenopathy, and brain metastases in the right frontal and left parietal lobes). Most recent tumor markers (2018-03-30): AFP 310 ng/mL, Beta-hCG 850 mIU/mL, LDH 410 U/L; increasing trend.\nBiomarkers: SALL4 positive, AFP positive (yolk sac), Beta-hCG positive (choriocarcinoma), CD30 positive (embryonal carcinoma), Glypican-3 positive (yolk sac), Cytokeratin (AE1/AE3) positive. NGS: KRAS p.G12D pathogenic mutation, PTEN homozygous deletion, TP53 p.P72R (variant of uncertain significance), isochromosome 12p, gain of 20q.\nTreatment history:\n# 2015: bleomycin, etoposide, cisplatin; achieved partial response\n# 2016-02-09: Partial resection of anterior mediastinal mass\n# 2016-12-05-present: ifosfamide, cisplatin; achieved partial response, subsequently progressed',
]
documents = [
'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 1 to 21 years. Sex allowed: Both. Cancer type allowed: mediastinal mixed germ cell tumor. Histology allowed: pediatric malignant solid tumor. Cancer burden allowed: high-risk (expected 5-year Event-Free Survival < 60%) and < 30% chance at long-term cure and disease status of Stable Disease or better. Prior treatment required: prior primary therapy. Prior treatment excluded: NA. Biomarkers required: satisfactory TIL cellular product availability (assessed during screening). Biomarkers excluded: NA.',
'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 50 years. Sex allowed: Male. Cancer type allowed: Any cancer. Histology allowed: Hematologic. Cancer burden allowed: Any. Prior treatment required: NA. Prior treatment excluded: NA. Biomarkers required: NA. Biomarkers excluded: NA.',
'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 29 years. Sex allowed: both. Cancer type allowed: mixed phenotype acute leukemia with T cell dominant phenotype. Histology allowed: mixed phenotype acute leukemia with T cell dominant phenotype. Cancer burden allowed: Second or greater relapse or first relapse post-allogeneic hematopoietic stem cell transplant or primary refractory disease or relapsed and refractory disease. Prior treatment required: NA. Prior treatment excluded: CD7 targeted therapy. Biomarkers required: CD7 expression on leukemic blasts (at least 90% of blasts positive by flow cytometry or immunohistochemistry) assessed during screening. Biomarkers excluded: NA.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.4132, 0.2552, 0.1695]])
patient_summary_trunc and this_space_trunc| patient_summary_trunc | this_space_trunc | |
|---|---|---|
| type | string | string |
| details |
|
|
| patient_summary_trunc | this_space_trunc |
|---|---|
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 6 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA. |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 14 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA. |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 17 |
|
| Sex: Male | |
| Cancer type: T-cell lymphoblastic lymphoma | |
| Histology: T-cell lymphoblastic lymphoma | |
| Current extent: Relapsed and refractory. As of 2019-12-06, anterior mediastinal mass measuring 5.5 cm with associated superior vena cava narrowing and bilateral hilar lymphadenopathy. As of 2020-04-21, bone marrow involvement with 22% lymphoblasts. LDH is elevated (normalized during remission, rose to 412 U/L at relapse in 2019-03-26, and remains elevated as of 2020-08-21). | |
| Biomarkers: TdT positive, CD3 positive, CD7 positive, CD2 positive, CD5 positive, CD19 negative, CD20 negative, CD45 positive, Ki-67 >95%; NOTCH1 p.V1744G, CDKN2A homozygous deletion, PTEN p.R130Q, 12q gain | |
| Treatment history: |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 29 years. Sex allowed: both. Cancer type allowed: T-cell acute lymphoblastic leukemia. Histology allowed: T-cell acute lymphoblastic leukemia. Cancer burden allowed: Second or greater relapse or first relapse post-allogeneic hematopoietic stem cell transplant or primary refractory disease or relapsed and refractory disease. Prior treatment required: NA. Prior treatment excluded: CD7 targeted therapy. Biomarkers required: CD7 expression on leukemic blasts (at least 90% of blasts positive by flow cytometry or immunohistochemistry) assessed during screening. Biomarkers excluded: NA. |
MultipleNegativesRankingLoss with these parameters:{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
patient_summary_trunc, this_space_trunc, and label| patient_summary_trunc | this_space_trunc | label | |
|---|---|---|---|
| type | string | string | float |
| details |
|
|
|
| patient_summary_trunc | this_space_trunc | label |
|---|---|---|
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 6 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA. |
-0.19999999999999996 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 14 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA. |
-0.19999999999999996 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 46 |
Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA. |
-1.0 |
CoSENTLoss with these parameters:{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
per_device_train_batch_size: 10learning_rate: 2e-05warmup_steps: 0.01bf16: Trueper_device_train_batch_size: 10num_train_epochs: 3max_steps: -1learning_rate: 2e-05lr_scheduler_type: linearlr_scheduler_kwargs: Nonewarmup_steps: 0.01optim: adamw_torch_fusedoptim_args: Noneweight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08optim_target_modules: Nonegradient_accumulation_steps: 1average_tokens_across_devices: Truemax_grad_norm: 1.0label_smoothing_factor: 0.0bf16: Truefp16: Falsebf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Nonetorch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneuse_liger_kernel: Falseliger_kernel_config: Noneuse_cache: Falseneftune_noise_alpha: Nonetorch_empty_cache_steps: Noneauto_find_batch_size: Falselog_on_each_node: Truelogging_nan_inf_filter: Trueinclude_num_input_tokens_seen: nolog_level: passivelog_level_replica: warningdisable_tqdm: Falseproject: huggingfacetrackio_space_id: Nonetrackio_bucket_id: Nonetrackio_static_space_id: Noneper_device_eval_batch_size: 8prediction_loss_only: Trueeval_on_start: Falseeval_do_concat_batches: Trueeval_use_gather_object: Falseeval_accumulation_steps: Noneinclude_for_metrics: []batch_eval_metrics: Falsesave_only_model: Falsesave_on_each_node: Falseenable_jit_checkpoint: Falsepush_to_hub: Falsehub_private_repo: Nonehub_model_id: Nonehub_strategy: every_savehub_always_push: Falsehub_revision: Noneload_best_model_at_end: Falseignore_data_skip: Falserestore_callback_states_from_checkpoint: Falsefull_determinism: Falseseed: 42data_seed: Noneuse_cpu: Falseaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}parallelism_config: Nonedataloader_drop_last: Truedataloader_num_workers: 0dataloader_pin_memory: Truedataloader_persistent_workers: Falsedataloader_prefetch_factor: Noneremove_unused_columns: Truelabel_names: Nonetrain_sampling_strategy: randomlength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falseddp_static_graph: Noneddp_backend: Noneddp_timeout: 1800fsdp: []fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}deepspeed: Nonedebug: []skip_memory_metrics: Truedo_predict: Falseresume_from_checkpoint: Nonewarmup_ratio: Nonelocal_rank: -1prompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}| Epoch | Step | Training Loss |
|---|---|---|
| 0.0027 | 100 | 2.1097 |
| 0.0053 | 200 | 2.1026 |
| 0.0080 | 300 | 2.0170 |
| 0.0107 | 400 | 1.9648 |
| 0.0133 | 500 | 2.0097 |
| 0.0160 | 600 | 2.0227 |
| 0.0187 | 700 | 2.0225 |
| 0.0213 | 800 | 2.0278 |
| 0.0240 | 900 | 1.9787 |
| 0.0267 | 1000 | 2.0899 |
| 0.0293 | 1100 | 2.1262 |
| 0.0320 | 1200 | 2.0419 |
| 0.0347 | 1300 | 2.1615 |
| 0.0373 | 1400 | 2.1889 |
| 0.0400 | 1500 | 2.1789 |
| 0.0427 | 1600 | 2.1664 |
| 0.0453 | 1700 | 2.1390 |
| 0.0480 | 1800 | 2.1228 |
| 0.0507 | 1900 | 2.1279 |
| 0.0533 | 2000 | 2.1547 |
| 0.0560 | 2100 | 2.1559 |
| 0.0587 | 2200 | 2.0800 |
| 0.0613 | 2300 | 2.0955 |
| 0.0640 | 2400 | 2.1033 |
| 0.0667 | 2500 | 2.1287 |
| 0.0694 | 2600 | 2.1078 |
| 0.0720 | 2700 | 2.1395 |
| 0.0747 | 2800 | 2.0470 |
| 0.0774 | 2900 | 2.1514 |
| 0.0800 | 3000 | 2.0960 |
| 0.0827 | 3100 | 2.1023 |
| 0.0854 | 3200 | 2.0591 |
| 0.0880 | 3300 | 2.0531 |
| 0.0907 | 3400 | 2.1157 |
| 0.0934 | 3500 | 2.0259 |
| 0.0960 | 3600 | 2.0914 |
| 0.0987 | 3700 | 2.0944 |
| 0.1014 | 3800 | 2.0515 |
| 0.1040 | 3900 | 2.0343 |
| 0.1067 | 4000 | 2.1189 |
| 0.1094 | 4100 | 2.1386 |
| 0.1120 | 4200 | 2.0944 |
| 0.1147 | 4300 | 2.1079 |
| 0.1174 | 4400 | 2.1063 |
| 0.1200 | 4500 | 2.0637 |
| 0.1227 | 4600 | 2.0631 |
| 0.1254 | 4700 | 2.0741 |
| 0.1280 | 4800 | 2.0463 |
| 0.1307 | 4900 | 2.0527 |
| 0.1334 | 5000 | 2.0972 |
| 0.1360 | 5100 | 2.0442 |
| 0.1387 | 5200 | 2.0219 |
| 0.1414 | 5300 | 2.0472 |
| 0.1440 | 5400 | 2.0216 |
| 0.1467 | 5500 | 2.0563 |
| 0.1494 | 5600 | 2.0628 |
| 0.1520 | 5700 | 2.0743 |
| 0.1547 | 5800 | 2.0345 |
| 0.1574 | 5900 | 2.0047 |
| 0.1600 | 6000 | 2.0353 |
| 0.1627 | 6100 | 2.0405 |
| 0.1654 | 6200 | 2.0376 |
| 0.1680 | 6300 | 2.0913 |
| 0.1707 | 6400 | 2.0583 |
| 0.1734 | 6500 | 2.0067 |
| 0.1760 | 6600 | 2.0845 |
| 0.1787 | 6700 | 2.0453 |
| 0.1814 | 6800 | 2.0680 |
| 0.1840 | 6900 | 2.1089 |
| 0.1867 | 7000 | 2.0148 |
| 0.1894 | 7100 | 2.0982 |
| 0.1921 | 7200 | 1.9828 |
| 0.1947 | 7300 | 1.9660 |
| 0.1974 | 7400 | 2.0258 |
| 0.2001 | 7500 | 2.0476 |
| 0.2027 | 7600 | 1.9685 |
| 0.2054 | 7700 | 2.0140 |
| 0.2081 | 7800 | 2.0370 |
| 0.2107 | 7900 | 1.9959 |
| 0.2134 | 8000 | 2.0764 |
| 0.2161 | 8100 | 2.0524 |
| 0.2187 | 8200 | 2.0152 |
| 0.2214 | 8300 | 2.0588 |
| 0.2241 | 8400 | 1.9843 |
| 0.2267 | 8500 | 2.0035 |
| 0.2294 | 8600 | 2.0075 |
| 0.2321 | 8700 | 2.0117 |
| 0.2347 | 8800 | 1.9865 |
| 0.2374 | 8900 | 2.0389 |
| 0.2401 | 9000 | 1.9825 |
| 0.2427 | 9100 | 2.0212 |
| 0.2454 | 9200 | 1.9980 |
| 0.2481 | 9300 | 2.0304 |
| 0.2507 | 9400 | 1.9323 |
| 0.2534 | 9500 | 1.9595 |
| 0.2561 | 9600 | 2.0353 |
| 0.2587 | 9700 | 2.0084 |
| 0.2614 | 9800 | 2.0122 |
| 0.2641 | 9900 | 1.9643 |
| 0.2667 | 10000 | 1.9825 |
| 0.2694 | 10100 | 2.0026 |
| 0.2721 | 10200 | 1.9970 |
| 0.2747 | 10300 | 2.0188 |
| 0.2774 | 10400 | 1.9387 |
| 0.2801 | 10500 | 1.9977 |
| 0.2827 | 10600 | 2.0632 |
| 0.2854 | 10700 | 2.0115 |
| 0.2881 | 10800 | 1.965 |
| 0.2907 | 10900 | 1.9850 |
| 0.2934 | 11000 | 1.9387 |
| 0.2961 | 11100 | 1.9670 |
| 0.2987 | 11200 | 2.0217 |
| 0.3014 | 11300 | 2.0043 |
| 0.3041 | 11400 | 1.9939 |
| 0.3067 | 11500 | 1.9480 |
| 0.3094 | 11600 | 2.0174 |
| 0.3121 | 11700 | 2.0616 |
| 0.3148 | 11800 | 1.9169 |
| 0.3174 | 11900 | 1.9747 |
| 0.3201 | 12000 | 1.9779 |
| 0.3228 | 12100 | 1.9851 |
| 0.3254 | 12200 | 2.0473 |
| 0.3281 | 12300 | 1.9748 |
| 0.3308 | 12400 | 1.9286 |
| 0.3334 | 12500 | 1.9859 |
| 0.3361 | 12600 | 1.9763 |
| 0.3388 | 12700 | 1.9661 |
| 0.3414 | 12800 | 1.9658 |
| 0.3441 | 12900 | 2.0157 |
| 0.3468 | 13000 | 1.9525 |
| 0.3494 | 13100 | 1.9356 |
| 0.3521 | 13200 | 1.9726 |
| 0.3548 | 13300 | 1.9877 |
| 0.3574 | 13400 | 1.9931 |
| 0.3601 | 13500 | 2.0022 |
| 0.3628 | 13600 | 1.9680 |
| 0.3654 | 13700 | 2.0357 |
| 0.3681 | 13800 | 1.9998 |
| 0.3708 | 13900 | 1.9797 |
| 0.3734 | 14000 | 2.0070 |
| 0.3761 | 14100 | 1.9707 |
| 0.3788 | 14200 | 1.9581 |
| 0.3814 | 14300 | 1.9418 |
| 0.3841 | 14400 | 1.9773 |
| 0.3868 | 14500 | 1.9671 |
| 0.3894 | 14600 | 1.9769 |
| 0.3921 | 14700 | 1.9366 |
| 0.3948 | 14800 | 1.9711 |
| 0.3974 | 14900 | 1.9064 |
| 0.4001 | 15000 | 1.9777 |
| 0.4028 | 15100 | 1.9788 |
| 0.4054 | 15200 | 1.9953 |
| 0.4081 | 15300 | 1.9647 |
| 0.4108 | 15400 | 1.9828 |
| 0.4134 | 15500 | 1.9767 |
| 0.4161 | 15600 | 1.9636 |
| 0.4188 | 15700 | 1.9869 |
| 0.4214 | 15800 | 1.9817 |
| 0.4241 | 15900 | 1.9657 |
| 0.4268 | 16000 | 1.9870 |
| 0.4294 | 16100 | 1.9930 |
| 0.4321 | 16200 | 1.9664 |
| 0.4348 | 16300 | 1.9400 |
| 0.4374 | 16400 | 1.9635 |
| 0.4401 | 16500 | 1.9841 |
| 0.4428 | 16600 | 2.0315 |
| 0.4455 | 16700 | 1.9191 |
| 0.4481 | 16800 | 1.9491 |
| 0.4508 | 16900 | 1.8987 |
| 0.4535 | 17000 | 1.9431 |
| 0.4561 | 17100 | 1.9246 |
| 0.4588 | 17200 | 1.9796 |
| 0.4615 | 17300 | 1.9278 |
| 0.4641 | 17400 | 1.9668 |
| 0.4668 | 17500 | 2.0059 |
| 0.4695 | 17600 | 1.9233 |
| 0.4721 | 17700 | 1.9243 |
| 0.4748 | 17800 | 1.9338 |
| 0.4775 | 17900 | 1.9952 |
| 0.4801 | 18000 | 1.9844 |
| 0.4828 | 18100 | 1.8802 |
| 0.4855 | 18200 | 1.9071 |
| 0.4881 | 18300 | 1.9515 |
| 0.4908 | 18400 | 1.9312 |
| 0.4935 | 18500 | 1.9077 |
| 0.4961 | 18600 | 1.9645 |
| 0.4988 | 18700 | 1.9891 |
| 0.5015 | 18800 | 1.9690 |
| 0.5041 | 18900 | 1.9005 |
| 0.5068 | 19000 | 1.9018 |
| 0.5095 | 19100 | 1.9202 |
| 0.5121 | 19200 | 1.9462 |
| 0.5148 | 19300 | 1.8916 |
| 0.5175 | 19400 | 1.9206 |
| 0.5201 | 19500 | 1.9652 |
| 0.5228 | 19600 | 2.0468 |
| 0.5255 | 19700 | 1.9428 |
| 0.5281 | 19800 | 1.9495 |
| 0.5308 | 19900 | 1.9128 |
| 0.5335 | 20000 | 1.9701 |
| 0.5361 | 20100 | 2.0012 |
| 0.5388 | 20200 | 1.9772 |
| 0.5415 | 20300 | 1.8960 |
| 0.5441 | 20400 | 1.9150 |
| 0.5468 | 20500 | 2.0020 |
| 0.5495 | 20600 | 1.9304 |
| 0.5521 | 20700 | 2.0639 |
| 0.5548 | 20800 | 1.8897 |
| 0.5575 | 20900 | 1.9290 |
| 0.5601 | 21000 | 1.9731 |
| 0.5628 | 21100 | 1.9475 |
| 0.5655 | 21200 | 1.9694 |
| 0.5682 | 21300 | 2.0294 |
| 0.5708 | 21400 | 1.9616 |
| 0.5735 | 21500 | 1.9831 |
| 0.5762 | 21600 | 1.9736 |
| 0.5788 | 21700 | 1.9909 |
| 0.5815 | 21800 | 1.9483 |
| 0.5842 | 21900 | 1.9062 |
| 0.5868 | 22000 | 1.9371 |
| 0.5895 | 22100 | 1.9843 |
| 0.5922 | 22200 | 1.9607 |
| 0.5948 | 22300 | 1.9272 |
| 0.5975 | 22400 | 1.9997 |
| 0.6002 | 22500 | 1.9602 |
| 0.6028 | 22600 | 1.9023 |
| 0.6055 | 22700 | 1.9272 |
| 0.6082 | 22800 | 1.9650 |
| 0.6108 | 22900 | 1.9734 |
| 0.6135 | 23000 | 1.9033 |
| 0.6162 | 23100 | 1.9572 |
| 0.6188 | 23200 | 1.9086 |
| 0.6215 | 23300 | 1.9131 |
| 0.6242 | 23400 | 1.9206 |
| 0.6268 | 23500 | 1.9683 |
| 0.6295 | 23600 | 2.0152 |
| 0.6322 | 23700 | 1.9678 |
| 0.6348 | 23800 | 1.8998 |
| 0.6375 | 23900 | 1.9642 |
| 0.6402 | 24000 | 1.9325 |
| 0.6428 | 24100 | 1.9180 |
| 0.6455 | 24200 | 1.9337 |
| 0.6482 | 24300 | 1.9503 |
| 0.6508 | 24400 | 1.9414 |
| 0.6535 | 24500 | 1.9657 |
| 0.6562 | 24600 | 1.9966 |
| 0.6588 | 24700 | 1.9800 |
| 0.6615 | 24800 | 1.9149 |
| 0.6642 | 24900 | 1.8964 |
| 0.6668 | 25000 | 1.9275 |
| 0.6695 | 25100 | 1.8696 |
| 0.6722 | 25200 | 1.9384 |
| 0.6748 | 25300 | 1.8979 |
| 0.6775 | 25400 | 1.9230 |
| 0.6802 | 25500 | 1.9122 |
| 0.6828 | 25600 | 1.9343 |
| 0.6855 | 25700 | 1.8912 |
| 0.6882 | 25800 | 1.9184 |
| 0.6909 | 25900 | 1.9921 |
| 0.6935 | 26000 | 1.9107 |
| 0.6962 | 26100 | 1.8887 |
| 0.6989 | 26200 | 1.9106 |
| 0.7015 | 26300 | 1.9292 |
| 0.7042 | 26400 | 1.9523 |
| 0.7069 | 26500 | 1.9703 |
| 0.7095 | 26600 | 1.9486 |
| 0.7122 | 26700 | 1.9428 |
| 0.7149 | 26800 | 1.8609 |
| 0.7175 | 26900 | 1.8672 |
| 0.7202 | 27000 | 1.9422 |
| 0.7229 | 27100 | 1.9480 |
| 0.7255 | 27200 | 1.9109 |
| 0.7282 | 27300 | 1.9147 |
| 0.7309 | 27400 | 1.8991 |
| 0.7335 | 27500 | 1.8790 |
| 0.7362 | 27600 | 1.9602 |
| 0.7389 | 27700 | 1.9173 |
| 0.7415 | 27800 | 1.9316 |
| 0.7442 | 27900 | 1.8816 |
| 0.7469 | 28000 | 1.9072 |
| 0.7495 | 28100 | 1.8890 |
| 0.7522 | 28200 | 1.8834 |
| 0.7549 | 28300 | 1.8614 |
| 0.7575 | 28400 | 1.9035 |
| 0.7602 | 28500 | 1.8818 |
| 0.7629 | 28600 | 1.9311 |
| 0.7655 | 28700 | 1.9130 |
| 0.7682 | 28800 | 1.9812 |
| 0.7709 | 28900 | 1.9094 |
| 0.7735 | 29000 | 1.8771 |
| 0.7762 | 29100 | 1.9274 |
| 0.7789 | 29200 | 1.8966 |
| 0.7815 | 29300 | 1.8993 |
| 0.7842 | 29400 | 1.9184 |
| 0.7869 | 29500 | 1.8816 |
| 0.7895 | 29600 | 1.9588 |
| 0.7922 | 29700 | 1.9103 |
| 0.7949 | 29800 | 1.9098 |
| 0.7975 | 29900 | 1.9023 |
| 0.8002 | 30000 | 1.9690 |
| 0.8029 | 30100 | 1.9328 |
| 0.8055 | 30200 | 1.9226 |
| 0.8082 | 30300 | 1.9360 |
| 0.8109 | 30400 | 1.9288 |
| 0.8136 | 30500 | 1.8976 |
| 0.8162 | 30600 | 1.9381 |
| 0.8189 | 30700 | 1.9466 |
| 0.8216 | 30800 | 1.8730 |
| 0.8242 | 30900 | 1.9482 |
| 0.8269 | 31000 | 1.8642 |
| 0.8296 | 31100 | 1.8894 |
| 0.8322 | 31200 | 1.9316 |
| 0.8349 | 31300 | 1.9082 |
| 0.8376 | 31400 | 1.9613 |
| 0.8402 | 31500 | 1.8960 |
| 0.8429 | 31600 | 1.9163 |
| 0.8456 | 31700 | 1.9087 |
| 0.8482 | 31800 | 1.8856 |
| 0.8509 | 31900 | 1.9305 |
| 0.8536 | 32000 | 1.9006 |
| 0.8562 | 32100 | 1.9280 |
| 0.8589 | 32200 | 1.9164 |
| 0.8616 | 32300 | 1.9388 |
| 0.8642 | 32400 | 1.9537 |
| 0.8669 | 32500 | 1.9221 |
| 0.8696 | 32600 | 1.8528 |
| 0.8722 | 32700 | 1.9218 |
| 0.8749 | 32800 | 1.8490 |
| 0.8776 | 32900 | 1.8748 |
| 0.8802 | 33000 | 1.9258 |
| 0.8829 | 33100 | 1.9005 |
| 0.8856 | 33200 | 1.8383 |
| 0.8882 | 33300 | 1.8987 |
| 0.8909 | 33400 | 1.8629 |
| 0.8936 | 33500 | 1.9681 |
| 0.8962 | 33600 | 1.8654 |
| 0.8989 | 33700 | 1.9606 |
| 0.9016 | 33800 | 1.9215 |
| 0.9042 | 33900 | 1.9633 |
| 0.9069 | 34000 | 1.9090 |
| 0.9096 | 34100 | 1.8609 |
| 0.9122 | 34200 | 1.8544 |
| 0.9149 | 34300 | 1.9079 |
| 0.9176 | 34400 | 1.9114 |
| 0.9202 | 34500 | 1.8576 |
| 0.9229 | 34600 | 1.8822 |
| 0.9256 | 34700 | 1.9188 |
| 0.9282 | 34800 | 1.9198 |
| 0.9309 | 34900 | 1.9342 |
| 0.9336 | 35000 | 1.8946 |
| 0.9362 | 35100 | 1.9252 |
| 0.9389 | 35200 | 1.9538 |
| 0.9416 | 35300 | 1.9347 |
| 0.9443 | 35400 | 1.9569 |
| 0.9469 | 35500 | 1.8621 |
| 0.9496 | 35600 | 1.8444 |
| 0.9523 | 35700 | 1.8594 |
| 0.9549 | 35800 | 1.9222 |
| 0.9576 | 35900 | 1.9259 |
| 0.9603 | 36000 | 1.8593 |
| 0.9629 | 36100 | 1.9855 |
| 0.9656 | 36200 | 1.9076 |
| 0.9683 | 36300 | 1.8637 |
| 0.9709 | 36400 | 1.9036 |
| 0.9736 | 36500 | 1.9342 |
| 0.9763 | 36600 | 1.9023 |
| 0.9789 | 36700 | 1.9322 |
| 0.9816 | 36800 | 1.9490 |
| 0.9843 | 36900 | 1.8465 |
| 0.9869 | 37000 | 1.9146 |
| 0.9896 | 37100 | 1.9198 |
| 0.9923 | 37200 | 1.8627 |
| 0.9949 | 37300 | 1.8951 |
| 0.9976 | 37400 | 1.9185 |
| 1.0003 | 37500 | 1.8614 |
| 1.0029 | 37600 | 1.9772 |
| 1.0056 | 37700 | 1.9709 |
| 1.0083 | 37800 | 1.8726 |
| 1.0109 | 37900 | 1.8738 |
| 1.0136 | 38000 | 1.9190 |
| 1.0163 | 38100 | 1.8980 |
| 1.0189 | 38200 | 1.8929 |
| 1.0216 | 38300 | 1.8547 |
| 1.0243 | 38400 | 1.7865 |
| 1.0269 | 38500 | 1.8397 |
| 1.0296 | 38600 | 1.8227 |
| 1.0323 | 38700 | 1.7417 |
| 1.0349 | 38800 | 1.8582 |
| 1.0376 | 38900 | 1.8384 |
| 1.0403 | 39000 | 1.8795 |
| 1.0429 | 39100 | 1.8481 |
| 1.0456 | 39200 | 1.8136 |
| 1.0483 | 39300 | 1.8381 |
| 1.0509 | 39400 | 1.8302 |
| 1.0536 | 39500 | 1.8315 |
| 1.0563 | 39600 | 1.8638 |
| 1.0589 | 39700 | 1.7937 |
| 1.0616 | 39800 | 1.8057 |
| 1.0643 | 39900 | 1.8195 |
| 1.0670 | 40000 | 1.8344 |
| 1.0696 | 40100 | 1.8156 |
| 1.0723 | 40200 | 1.8310 |
| 1.0750 | 40300 | 1.7992 |
| 1.0776 | 40400 | 1.8545 |
| 1.0803 | 40500 | 1.8197 |
| 1.0830 | 40600 | 1.8160 |
| 1.0856 | 40700 | 1.7915 |
| 1.0883 | 40800 | 1.7950 |
| 1.0910 | 40900 | 1.8372 |
| 1.0936 | 41000 | 1.7989 |
| 1.0963 | 41100 | 1.8320 |
| 1.0990 | 41200 | 1.8300 |
| 1.1016 | 41300 | 1.7970 |
| 1.1043 | 41400 | 1.7861 |
| 1.1070 | 41500 | 1.8797 |
| 1.1096 | 41600 | 1.8655 |
| 1.1123 | 41700 | 1.8444 |
| 1.1150 | 41800 | 1.8441 |
| 1.1176 | 41900 | 1.8615 |
| 1.1203 | 42000 | 1.8531 |
| 1.1230 | 42100 | 1.8496 |
| 1.1256 | 42200 | 1.8116 |
| 1.1283 | 42300 | 1.8032 |
| 1.1310 | 42400 | 1.8060 |
| 1.1336 | 42500 | 1.8871 |
| 1.1363 | 42600 | 1.8235 |
| 1.1390 | 42700 | 1.7875 |
| 1.1416 | 42800 | 1.8503 |
| 1.1443 | 42900 | 1.7687 |
| 1.1470 | 43000 | 1.8696 |
| 1.1496 | 43100 | 1.8083 |
| 1.1523 | 43200 | 1.9133 |
| 1.1550 | 43300 | 1.8126 |
| 1.1576 | 43400 | 1.7961 |
| 1.1603 | 43500 | 1.8612 |
| 1.1630 | 43600 | 1.8064 |
| 1.1656 | 43700 | 1.8454 |
| 1.1683 | 43800 | 1.9025 |
| 1.1710 | 43900 | 1.8484 |
| 1.1736 | 44000 | 1.8282 |
| 1.1763 | 44100 | 1.8889 |
| 1.1790 | 44200 | 1.8719 |
| 1.1816 | 44300 | 1.8640 |
| 1.1843 | 44400 | 1.9078 |
| 1.1870 | 44500 | 1.8104 |
| 1.1897 | 44600 | 1.8882 |
| 1.1923 | 44700 | 1.8074 |
| 1.1950 | 44800 | 1.7589 |
| 1.1977 | 44900 | 1.8789 |
| 1.2003 | 45000 | 1.8259 |
| 1.2030 | 45100 | 1.7842 |
| 1.2057 | 45200 | 1.8202 |
| 1.2083 | 45300 | 1.8464 |
| 1.2110 | 45400 | 1.8176 |
| 1.2137 | 45500 | 1.8799 |
| 1.2163 | 45600 | 1.8616 |
| 1.2190 | 45700 | 1.8058 |
| 1.2217 | 45800 | 1.8754 |
| 1.2243 | 45900 | 1.8189 |
| 1.2270 | 46000 | 1.8177 |
| 1.2297 | 46100 | 1.8518 |
| 1.2323 | 46200 | 1.8226 |
| 1.2350 | 46300 | 1.8215 |
| 1.2377 | 46400 | 1.8794 |
| 1.2403 | 46500 | 1.8048 |
| 1.2430 | 46600 | 1.8667 |
| 1.2457 | 46700 | 1.8200 |
| 1.2483 | 46800 | 1.8765 |
| 1.2510 | 46900 | 1.7580 |
| 1.2537 | 47000 | 1.8418 |
| 1.2563 | 47100 | 1.8557 |
| 1.2590 | 47200 | 1.8336 |
| 1.2617 | 47300 | 1.8366 |
| 1.2643 | 47400 | 1.7890 |
| 1.2670 | 47500 | 1.8314 |
| 1.2697 | 47600 | 1.8618 |
| 1.2723 | 47700 | 1.8131 |
| 1.2750 | 47800 | 1.8730 |
| 1.2777 | 47900 | 1.7687 |
| 1.2803 | 48000 | 1.8556 |
| 1.2830 | 48100 | 1.9204 |
| 1.2857 | 48200 | 1.8382 |
| 1.2883 | 48300 | 1.8151 |
| 1.2910 | 48400 | 1.8370 |
| 1.2937 | 48500 | 1.7916 |
| 1.2963 | 48600 | 1.8110 |
| 1.2990 | 48700 | 1.8778 |
| 1.3017 | 48800 | 1.8687 |
| 1.3043 | 48900 | 1.8057 |
| 1.3070 | 49000 | 1.8284 |
| 1.3097 | 49100 | 1.8691 |
| 1.3123 | 49200 | 1.8976 |
| 1.3150 | 49300 | 1.7885 |
| 1.3177 | 49400 | 1.8400 |
| 1.3204 | 49500 | 1.8136 |
| 1.3230 | 49600 | 1.8196 |
| 1.3257 | 49700 | 1.9024 |
| 1.3284 | 49800 | 1.8515 |
| 1.3310 | 49900 | 1.7781 |
| 1.3337 | 50000 | 1.8426 |
| 1.3364 | 50100 | 1.8586 |
| 1.3390 | 50200 | 1.7932 |
| 1.3417 | 50300 | 1.8657 |
| 1.3444 | 50400 | 1.8406 |
| 1.3470 | 50500 | 1.7984 |
| 1.3497 | 50600 | 1.8332 |
| 1.3524 | 50700 | 1.8358 |
| 1.3550 | 50800 | 1.8439 |
| 1.3577 | 50900 | 1.8598 |
| 1.3604 | 51000 | 1.8534 |
| 1.3630 | 51100 | 1.8160 |
| 1.3657 | 51200 | 1.9035 |
| 1.3684 | 51300 | 1.8598 |
| 1.3710 | 51400 | 1.8512 |
| 1.3737 | 51500 | 1.8658 |
| 1.3764 | 51600 | 1.8173 |
| 1.3790 | 51700 | 1.8557 |
| 1.3817 | 51800 | 1.7987 |
| 1.3844 | 51900 | 1.8562 |
| 1.3870 | 52000 | 1.8463 |
| 1.3897 | 52100 | 1.8268 |
| 1.3924 | 52200 | 1.8132 |
| 1.3950 | 52300 | 1.8554 |
| 1.3977 | 52400 | 1.7591 |
| 1.4004 | 52500 | 1.8339 |
| 1.4030 | 52600 | 1.8737 |
| 1.4057 | 52700 | 1.8681 |
| 1.4084 | 52800 | 1.8370 |
| 1.4110 | 52900 | 1.8646 |
| 1.4137 | 53000 | 1.8577 |
| 1.4164 | 53100 | 1.8094 |
| 1.4190 | 53200 | 1.8677 |
| 1.4217 | 53300 | 1.8559 |
| 1.4244 | 53400 | 1.8482 |
| 1.4270 | 53500 | 1.8827 |
| 1.4297 | 53600 | 1.8684 |
| 1.4324 | 53700 | 1.8333 |
| 1.4350 | 53800 | 1.8234 |
| 1.4377 | 53900 | 1.8558 |
| 1.4404 | 54000 | 1.8440 |
| 1.4431 | 54100 | 1.8837 |
| 1.4457 | 54200 | 1.8305 |
| 1.4484 | 54300 | 1.8136 |
| 1.4511 | 54400 | 1.7802 |
| 1.4537 | 54500 | 1.8196 |
| 1.4564 | 54600 | 1.8428 |
| 1.4591 | 54700 | 1.8440 |
| 1.4617 | 54800 | 1.8014 |
| 1.4644 | 54900 | 1.8560 |
| 1.4671 | 55000 | 1.8997 |
| 1.4697 | 55100 | 1.7862 |
| 1.4724 | 55200 | 1.8211 |
| 1.4751 | 55300 | 1.8356 |
| 1.4777 | 55400 | 1.8672 |
| 1.4804 | 55500 | 1.8816 |
| 1.4831 | 55600 | 1.7562 |
| 1.4857 | 55700 | 1.8308 |
| 1.4884 | 55800 | 1.8207 |
| 1.4911 | 55900 | 1.8277 |
| 1.4937 | 56000 | 1.7980 |
| 1.4964 | 56100 | 1.8584 |
| 1.4991 | 56200 | 1.8757 |
| 1.5017 | 56300 | 1.8284 |
| 1.5044 | 56400 | 1.8085 |
| 1.5071 | 56500 | 1.7842 |
| 1.5097 | 56600 | 1.8400 |
| 1.5124 | 56700 | 1.8218 |
| 1.5151 | 56800 | 1.7970 |
| 1.5177 | 56900 | 1.8180 |
| 1.5204 | 57000 | 1.8729 |
| 1.5231 | 57100 | 1.9142 |
| 1.5257 | 57200 | 1.8359 |
| 1.5284 | 57300 | 1.8463 |
| 1.5311 | 57400 | 1.8027 |
| 1.5337 | 57500 | 1.8745 |
| 1.5364 | 57600 | 1.9057 |
| 1.5391 | 57700 | 1.8654 |
| 1.5417 | 57800 | 1.7925 |
| 1.5444 | 57900 | 1.8224 |
| 1.5471 | 58000 | 1.8788 |
| 1.5497 | 58100 | 1.8322 |
| 1.5524 | 58200 | 1.9628 |
| 1.5551 | 58300 | 1.7916 |
| 1.5577 | 58400 | 1.8030 |
| 1.5604 | 58500 | 1.8823 |
| 1.5631 | 58600 | 1.8487 |
| 1.5658 | 58700 | 1.8621 |
| 1.5684 | 58800 | 1.9351 |
| 1.5711 | 58900 | 1.8800 |
| 1.5738 | 59000 | 1.8521 |
| 1.5764 | 59100 | 1.8772 |
| 1.5791 | 59200 | 1.9035 |
| 1.5818 | 59300 | 1.8420 |
| 1.5844 | 59400 | 1.7975 |
| 1.5871 | 59500 | 1.8422 |
| 1.5898 | 59600 | 1.8699 |
| 1.5924 | 59700 | 1.8528 |
| 1.5951 | 59800 | 1.8295 |
| 1.5978 | 59900 | 1.9045 |
| 1.6004 | 60000 | 1.8748 |
| 1.6031 | 60100 | 1.8008 |
| 1.6058 | 60200 | 1.8234 |
| 1.6084 | 60300 | 1.8810 |
| 1.6111 | 60400 | 1.8776 |
| 1.6138 | 60500 | 1.8461 |
| 1.6164 | 60600 | 1.8055 |
| 1.6191 | 60700 | 1.8469 |
| 1.6218 | 60800 | 1.8356 |
| 1.6244 | 60900 | 1.8140 |
| 1.6271 | 61000 | 1.8829 |
| 1.6298 | 61100 | 1.9126 |
| 1.6324 | 61200 | 1.8489 |
| 1.6351 | 61300 | 1.7840 |
| 1.6378 | 61400 | 1.9130 |
| 1.6404 | 61500 | 1.8085 |
| 1.6431 | 61600 | 1.8279 |
| 1.6458 | 61700 | 1.8472 |
| 1.6484 | 61800 | 1.8493 |
| 1.6511 | 61900 | 1.8564 |
| 1.6538 | 62000 | 1.8752 |
| 1.6564 | 62100 | 1.9311 |
| 1.6591 | 62200 | 1.8751 |
| 1.6618 | 62300 | 1.8161 |
| 1.6644 | 62400 | 1.8233 |
| 1.6671 | 62500 | 1.8336 |
| 1.6698 | 62600 | 1.7873 |
| 1.6724 | 62700 | 1.8519 |
| 1.6751 | 62800 | 1.8140 |
| 1.6778 | 62900 | 1.8382 |
| 1.6804 | 63000 | 1.8331 |
| 1.6831 | 63100 | 1.8269 |
| 1.6858 | 63200 | 1.8305 |
| 1.6885 | 63300 | 1.8499 |
| 1.6911 | 63400 | 1.9022 |
| 1.6938 | 63500 | 1.8062 |
| 1.6965 | 63600 | 1.8024 |
| 1.6991 | 63700 | 1.8230 |
| 1.7018 | 63800 | 1.8482 |
| 1.7045 | 63900 | 1.8544 |
| 1.7071 | 64000 | 1.8832 |
| 1.7098 | 64100 | 1.8721 |
| 1.7125 | 64200 | 1.8399 |
| 1.7151 | 64300 | 1.7700 |
| 1.7178 | 64400 | 1.8113 |
| 1.7205 | 64500 | 1.8538 |
| 1.7231 | 64600 | 1.8499 |
| 1.7258 | 64700 | 1.8007 |
| 1.7285 | 64800 | 1.8360 |
| 1.7311 | 64900 | 1.8158 |
| 1.7338 | 65000 | 1.7951 |
| 1.7365 | 65100 | 1.8827 |
| 1.7391 | 65200 | 1.8397 |
| 1.7418 | 65300 | 1.8682 |
| 1.7445 | 65400 | 1.7731 |
| 1.7471 | 65500 | 1.8405 |
| 1.7498 | 65600 | 1.8278 |
| 1.7525 | 65700 | 1.7933 |
| 1.7551 | 65800 | 1.7989 |
| 1.7578 | 65900 | 1.7939 |
| 1.7605 | 66000 | 1.7997 |
| 1.7631 | 66100 | 1.8810 |
| 1.7658 | 66200 | 1.8345 |
| 1.7685 | 66300 | 1.8866 |
| 1.7711 | 66400 | 1.8133 |
| 1.7738 | 66500 | 1.8060 |
| 1.7765 | 66600 | 1.8350 |
| 1.7791 | 66700 | 1.8264 |
| 1.7818 | 66800 | 1.8077 |
| 1.7845 | 66900 | 1.8380 |
| 1.7871 | 67000 | 1.8098 |
| 1.7898 | 67100 | 1.8663 |
| 1.7925 | 67200 | 1.8404 |
| 1.7951 | 67300 | 1.8340 |
| 1.7978 | 67400 | 1.8439 |
| 1.8005 | 67500 | 1.8760 |
| 1.8031 | 67600 | 1.8443 |
| 1.8058 | 67700 | 1.8637 |
| 1.8085 | 67800 | 1.8704 |
| 1.8111 | 67900 | 1.8472 |
| 1.8138 | 68000 | 1.8012 |
| 1.8165 | 68100 | 1.8613 |
| 1.8192 | 68200 | 1.8638 |
| 1.8218 | 68300 | 1.8209 |
| 1.8245 | 68400 | 1.8510 |
| 1.8272 | 68500 | 1.8044 |
| 1.8298 | 68600 | 1.8287 |
| 1.8325 | 68700 | 1.8645 |
| 1.8352 | 68800 | 1.8263 |
| 1.8378 | 68900 | 1.8947 |
| 1.8405 | 69000 | 1.8261 |
| 1.8432 | 69100 | 1.8396 |
| 1.8458 | 69200 | 1.8444 |
| 1.8485 | 69300 | 1.8096 |
| 1.8512 | 69400 | 1.8723 |
| 1.8538 | 69500 | 1.8146 |
| 1.8565 | 69600 | 1.8658 |
| 1.8592 | 69700 | 1.8497 |
| 1.8618 | 69800 | 1.8669 |
| 1.8645 | 69900 | 1.8801 |
| 1.8672 | 70000 | 1.8469 |
| 1.8698 | 70100 | 1.7717 |
| 1.8725 | 70200 | 1.8477 |
| 1.8752 | 70300 | 1.7864 |
| 1.8778 | 70400 | 1.7908 |
| 1.8805 | 70500 | 1.8637 |
| 1.8832 | 70600 | 1.8518 |
| 1.8858 | 70700 | 1.7551 |
| 1.8885 | 70800 | 1.8541 |
| 1.8912 | 70900 | 1.8103 |
| 1.8938 | 71000 | 1.8809 |
| 1.8965 | 71100 | 1.7924 |
| 1.8992 | 71200 | 1.9046 |
| 1.9018 | 71300 | 1.8694 |
| 1.9045 | 71400 | 1.8865 |
| 1.9072 | 71500 | 1.8323 |
| 1.9098 | 71600 | 1.8018 |
| 1.9125 | 71700 | 1.7537 |
| 1.9152 | 71800 | 1.8723 |
| 1.9178 | 71900 | 1.8311 |
| 1.9205 | 72000 | 1.7864 |
| 1.9232 | 72100 | 1.8192 |
| 1.9258 | 72200 | 1.8748 |
| 1.9285 | 72300 | 1.8712 |
| 1.9312 | 72400 | 1.8425 |
| 1.9338 | 72500 | 1.8295 |
| 1.9365 | 72600 | 1.8716 |
| 1.9392 | 72700 | 1.8894 |
| 1.9419 | 72800 | 1.8683 |
| 1.9445 | 72900 | 1.8784 |
| 1.9472 | 73000 | 1.7923 |
| 1.9499 | 73100 | 1.8025 |
| 1.9525 | 73200 | 1.7947 |
| 1.9552 | 73300 | 1.8662 |
| 1.9579 | 73400 | 1.8656 |
| 1.9605 | 73500 | 1.7899 |
| 1.9632 | 73600 | 1.9105 |
| 1.9659 | 73700 | 1.8512 |
| 1.9685 | 73800 | 1.7723 |
| 1.9712 | 73900 | 1.8619 |
| 1.9739 | 74000 | 1.8696 |
| 1.9765 | 74100 | 1.8602 |
| 1.9792 | 74200 | 1.8635 |
| 1.9819 | 74300 | 1.8524 |
| 1.9845 | 74400 | 1.8099 |
| 1.9872 | 74500 | 1.8604 |
| 1.9899 | 74600 | 1.8461 |
| 1.9925 | 74700 | 1.8293 |
| 1.9952 | 74800 | 1.8306 |
| 1.9979 | 74900 | 1.8504 |
| 2.0005 | 75000 | 1.8202 |
| 2.0032 | 75100 | 1.9263 |
| 2.0059 | 75200 | 1.8941 |
| 2.0085 | 75300 | 1.8230 |
| 2.0112 | 75400 | 1.8222 |
| 2.0139 | 75500 | 1.8579 |
| 2.0165 | 75600 | 1.8358 |
| 2.0192 | 75700 | 1.8119 |
| 2.0219 | 75800 | 1.8189 |
| 2.0245 | 75900 | 1.7333 |
| 2.0272 | 76000 | 1.7699 |
| 2.0299 | 76100 | 1.7620 |
| 2.0325 | 76200 | 1.7001 |
| 2.0352 | 76300 | 1.8153 |
| 2.0379 | 76400 | 1.7664 |
| 2.0405 | 76500 | 1.8328 |
| 2.0432 | 76600 | 1.7963 |
| 2.0459 | 76700 | 1.7330 |
| 2.0485 | 76800 | 1.8102 |
| 2.0512 | 76900 | 1.7770 |
| 2.0539 | 77000 | 1.7805 |
| 2.0565 | 77100 | 1.8052 |
| 2.0592 | 77200 | 1.7231 |
| 2.0619 | 77300 | 1.7935 |
| 2.0646 | 77400 | 1.7651 |
| 2.0672 | 77500 | 1.7917 |
| 2.0699 | 77600 | 1.7616 |
| 2.0726 | 77700 | 1.7889 |
| 2.0752 | 77800 | 1.7619 |
| 2.0779 | 77900 | 1.7971 |
| 2.0806 | 78000 | 1.7745 |
| 2.0832 | 78100 | 1.7524 |
| 2.0859 | 78200 | 1.7389 |
| 2.0886 | 78300 | 1.7494 |
| 2.0912 | 78400 | 1.7828 |
| 2.0939 | 78500 | 1.7675 |
| 2.0966 | 78600 | 1.7728 |
| 2.0992 | 78700 | 1.7754 |
| 2.1019 | 78800 | 1.7449 |
| 2.1046 | 78900 | 1.7580 |
| 2.1072 | 79000 | 1.8307 |
| 2.1099 | 79100 | 1.8150 |
| 2.1126 | 79200 | 1.7712 |
| 2.1152 | 79300 | 1.7857 |
| 2.1179 | 79400 | 1.8535 |
| 2.1206 | 79500 | 1.8069 |
| 2.1232 | 79600 | 1.7623 |
| 2.1259 | 79700 | 1.7808 |
| 2.1286 | 79800 | 1.7470 |
| 2.1312 | 79900 | 1.7814 |
| 2.1339 | 80000 | 1.8345 |
| 2.1366 | 80100 | 1.7616 |
| 2.1392 | 80200 | 1.7684 |
| 2.1419 | 80300 | 1.7794 |
| 2.1446 | 80400 | 1.7299 |
| 2.1472 | 80500 | 1.8270 |
| 2.1499 | 80600 | 1.7535 |
| 2.1526 | 80700 | 1.8717 |
| 2.1552 | 80800 | 1.7574 |
| 2.1579 | 80900 | 1.7529 |
| 2.1606 | 81000 | 1.8264 |
| 2.1632 | 81100 | 1.7779 |
| 2.1659 | 81200 | 1.7997 |
| 2.1686 | 81300 | 1.8335 |
| 2.1712 | 81400 | 1.8031 |
| 2.1739 | 81500 | 1.8069 |
| 2.1766 | 81600 | 1.8500 |
| 2.1792 | 81700 | 1.8142 |
| 2.1819 | 81800 | 1.8152 |
| 2.1846 | 81900 | 1.8551 |
| 2.1872 | 82000 | 1.7885 |
| 2.1899 | 82100 | 1.8232 |
| 2.1926 | 82200 | 1.7444 |
| 2.1953 | 82300 | 1.7459 |
| 2.1979 | 82400 | 1.8384 |
| 2.2006 | 82500 | 1.7598 |
| 2.2033 | 82600 | 1.7411 |
| 2.2059 | 82700 | 1.7776 |
| 2.2086 | 82800 | 1.7877 |
| 2.2113 | 82900 | 1.7756 |
| 2.2139 | 83000 | 1.8439 |
| 2.2166 | 83100 | 1.8309 |
| 2.2193 | 83200 | 1.7564 |
| 2.2219 | 83300 | 1.8235 |
| 2.2246 | 83400 | 1.7765 |
| 2.2273 | 83500 | 1.7871 |
| 2.2299 | 83600 | 1.8013 |
| 2.2326 | 83700 | 1.7686 |
| 2.2353 | 83800 | 1.8158 |
| 2.2379 | 83900 | 1.8119 |
| 2.2406 | 84000 | 1.7801 |
| 2.2433 | 84100 | 1.8089 |
| 2.2459 | 84200 | 1.7878 |
| 2.2486 | 84300 | 1.8202 |
| 2.2513 | 84400 | 1.7257 |
| 2.2539 | 84500 | 1.8143 |
| 2.2566 | 84600 | 1.8262 |
| 2.2593 | 84700 | 1.7646 |
| 2.2619 | 84800 | 1.7853 |
| 2.2646 | 84900 | 1.7693 |
| 2.2673 | 85000 | 1.8198 |
| 2.2699 | 85100 | 1.8046 |
| 2.2726 | 85200 | 1.7617 |
| 2.2753 | 85300 | 1.8327 |
| 2.2779 | 85400 | 1.7294 |
| 2.2806 | 85500 | 1.8334 |
| 2.2833 | 85600 | 1.8365 |
| 2.2859 | 85700 | 1.8031 |
| 2.2886 | 85800 | 1.7972 |
| 2.2913 | 85900 | 1.7824 |
| 2.2939 | 86000 | 1.7606 |
| 2.2966 | 86100 | 1.7807 |
| 2.2993 | 86200 | 1.8345 |
| 2.3019 | 86300 | 1.8260 |
| 2.3046 | 86400 | 1.7557 |
| 2.3073 | 86500 | 1.7963 |
| 2.3099 | 86600 | 1.8558 |
| 2.3126 | 86700 | 1.8281 |
| 2.3153 | 86800 | 1.7574 |
| 2.3180 | 86900 | 1.8075 |
| 2.3206 | 87000 | 1.7538 |
| 2.3233 | 87100 | 1.8053 |
| 2.3260 | 87200 | 1.8609 |
| 2.3286 | 87300 | 1.7974 |
| 2.3313 | 87400 | 1.7565 |
| 2.3340 | 87500 | 1.8076 |
| 2.3366 | 87600 | 1.8045 |
| 2.3393 | 87700 | 1.7845 |
| 2.3420 | 87800 | 1.8142 |
| 2.3446 | 87900 | 1.7983 |
| 2.3473 | 88000 | 1.7967 |
| 2.3500 | 88100 | 1.7636 |
| 2.3526 | 88200 | 1.8073 |
| 2.3553 | 88300 | 1.8007 |
| 2.3580 | 88400 | 1.8328 |
| 2.3606 | 88500 | 1.8135 |
| 2.3633 | 88600 | 1.7648 |
| 2.3660 | 88700 | 1.8763 |
| 2.3686 | 88800 | 1.8237 |
| 2.3713 | 88900 | 1.7864 |
| 2.3740 | 89000 | 1.8495 |
| 2.3766 | 89100 | 1.7829 |
| 2.3793 | 89200 | 1.8384 |
| 2.3820 | 89300 | 1.7370 |
| 2.3846 | 89400 | 1.8295 |
| 2.3873 | 89500 | 1.8166 |
| 2.3900 | 89600 | 1.7961 |
| 2.3926 | 89700 | 1.7690 |
| 2.3953 | 89800 | 1.8209 |
| 2.3980 | 89900 | 1.7119 |
| 2.4006 | 90000 | 1.8118 |
| 2.4033 | 90100 | 1.8259 |
| 2.4060 | 90200 | 1.8236 |
| 2.4086 | 90300 | 1.8203 |
| 2.4113 | 90400 | 1.7988 |
| 2.4140 | 90500 | 1.8402 |
| 2.4166 | 90600 | 1.7771 |
| 2.4193 | 90700 | 1.8321 |
| 2.4220 | 90800 | 1.8349 |
| 2.4246 | 90900 | 1.7891 |
| 2.4273 | 91000 | 1.8733 |
| 2.4300 | 91100 | 1.8250 |
| 2.4326 | 91200 | 1.8019 |
| 2.4353 | 91300 | 1.7883 |
| 2.4380 | 91400 | 1.8209 |
| 2.4407 | 91500 | 1.8197 |
| 2.4433 | 91600 | 1.8663 |
| 2.4460 | 91700 | 1.7811 |
| 2.4487 | 91800 | 1.7915 |
| 2.4513 | 91900 | 1.7481 |
| 2.4540 | 92000 | 1.7740 |
| 2.4567 | 92100 | 1.8187 |
| 2.4593 | 92200 | 1.7998 |
| 2.4620 | 92300 | 1.7546 |
| 2.4647 | 92400 | 1.8557 |
| 2.4673 | 92500 | 1.8474 |
| 2.4700 | 92600 | 1.7558 |
| 2.4727 | 92700 | 1.8080 |
| 2.4753 | 92800 | 1.7917 |
| 2.4780 | 92900 | 1.8455 |
| 2.4807 | 93000 | 1.8509 |
| 2.4833 | 93100 | 1.7336 |
| 2.4860 | 93200 | 1.7913 |
| 2.4887 | 93300 | 1.7689 |
| 2.4913 | 93400 | 1.7965 |
| 2.4940 | 93500 | 1.7875 |
| 2.4967 | 93600 | 1.8321 |
| 2.4993 | 93700 | 1.8368 |
| 2.5020 | 93800 | 1.7925 |
| 2.5047 | 93900 | 1.7799 |
| 2.5073 | 94000 | 1.7484 |
| 2.5100 | 94100 | 1.8128 |
| 2.5127 | 94200 | 1.7827 |
| 2.5153 | 94300 | 1.7652 |
| 2.5180 | 94400 | 1.8157 |
| 2.5207 | 94500 | 1.8556 |
| 2.5233 | 94600 | 1.8455 |
| 2.5260 | 94700 | 1.8112 |
| 2.5287 | 94800 | 1.8226 |
| 2.5313 | 94900 | 1.7865 |
| 2.5340 | 95000 | 1.8493 |
| 2.5367 | 95100 | 1.8811 |
| 2.5393 | 95200 | 1.7795 |
| 2.5420 | 95300 | 1.7889 |
| 2.5447 | 95400 | 1.7988 |
| 2.5473 | 95500 | 1.8428 |
| 2.5500 | 95600 | 1.8152 |
| 2.5527 | 95700 | 1.9170 |
| 2.5553 | 95800 | 1.7970 |
| 2.5580 | 95900 | 1.7632 |
| 2.5607 | 96000 | 1.8478 |
| 2.5634 | 96100 | 1.8122 |
| 2.5660 | 96200 | 1.8398 |
| 2.5687 | 96300 | 1.8991 |
| 2.5714 | 96400 | 1.8492 |
| 2.5740 | 96500 | 1.8292 |
| 2.5767 | 96600 | 1.8307 |
| 2.5794 | 96700 | 1.8925 |
| 2.5820 | 96800 | 1.7966 |
| 2.5847 | 96900 | 1.7471 |
| 2.5874 | 97000 | 1.8360 |
| 2.5900 | 97100 | 1.8376 |
| 2.5927 | 97200 | 1.8224 |
| 2.5954 | 97300 | 1.8072 |
| 2.5980 | 97400 | 1.8520 |
| 2.6007 | 97500 | 1.8354 |
| 2.6034 | 97600 | 1.8002 |
| 2.6060 | 97700 | 1.8160 |
| 2.6087 | 97800 | 1.8330 |
| 2.6114 | 97900 | 1.8631 |
| 2.6140 | 98000 | 1.7841 |
| 2.6167 | 98100 | 1.7863 |
| 2.6194 | 98200 | 1.8270 |
| 2.6220 | 98300 | 1.7986 |
| 2.6247 | 98400 | 1.8201 |
| 2.6274 | 98500 | 1.8385 |
| 2.6300 | 98600 | 1.8882 |
| 2.6327 | 98700 | 1.8095 |
| 2.6354 | 98800 | 1.7705 |
| 2.6380 | 98900 | 1.8642 |
| 2.6407 | 99000 | 1.8042 |
| 2.6434 | 99100 | 1.7884 |
| 2.6460 | 99200 | 1.8382 |
| 2.6487 | 99300 | 1.8377 |
| 2.6514 | 99400 | 1.8242 |
| 2.6540 | 99500 | 1.8438 |
| 2.6567 | 99600 | 1.9048 |
| 2.6594 | 99700 | 1.8271 |
| 2.6620 | 99800 | 1.8029 |
| 2.6647 | 99900 | 1.7928 |
| 2.6674 | 100000 | 1.8209 |
| 2.6700 | 100100 | 1.7523 |
| 2.6727 | 100200 | 1.8372 |
| 2.6754 | 100300 | 1.7920 |
| 2.6780 | 100400 | 1.8048 |
| 2.6807 | 100500 | 1.7839 |
| 2.6834 | 100600 | 1.8397 |
| 2.6860 | 100700 | 1.7909 |
| 2.6887 | 100800 | 1.8315 |
| 2.6914 | 100900 | 1.8664 |
| 2.6941 | 101000 | 1.7952 |
| 2.6967 | 101100 | 1.7601 |
| 2.6994 | 101200 | 1.8030 |
| 2.7021 | 101300 | 1.8255 |
| 2.7047 | 101400 | 1.8253 |
| 2.7074 | 101500 | 1.8620 |
| 2.7101 | 101600 | 1.8428 |
| 2.7127 | 101700 | 1.8043 |
| 2.7154 | 101800 | 1.7545 |
| 2.7181 | 101900 | 1.8081 |
| 2.7207 | 102000 | 1.8124 |
| 2.7234 | 102100 | 1.8276 |
| 2.7261 | 102200 | 1.7798 |
| 2.7287 | 102300 | 1.7884 |
| 2.7314 | 102400 | 1.8055 |
| 2.7341 | 102500 | 1.7687 |
| 2.7367 | 102600 | 1.8625 |
| 2.7394 | 102700 | 1.8328 |
| 2.7421 | 102800 | 1.8266 |
| 2.7447 | 102900 | 1.7522 |
| 2.7474 | 103000 | 1.8277 |
| 2.7501 | 103100 | 1.8045 |
| 2.7527 | 103200 | 1.7603 |
| 2.7554 | 103300 | 1.7622 |
| 2.7581 | 103400 | 1.7891 |
| 2.7607 | 103500 | 1.7807 |
| 2.7634 | 103600 | 1.8527 |
| 2.7661 | 103700 | 1.8053 |
| 2.7687 | 103800 | 1.8769 |
| 2.7714 | 103900 | 1.7766 |
| 2.7741 | 104000 | 1.7880 |
| 2.7767 | 104100 | 1.8081 |
| 2.7794 | 104200 | 1.7957 |
| 2.7821 | 104300 | 1.7770 |
| 2.7847 | 104400 | 1.8168 |
| 2.7874 | 104500 | 1.8015 |
| 2.7901 | 104600 | 1.8250 |
| 2.7927 | 104700 | 1.8207 |
| 2.7954 | 104800 | 1.8015 |
| 2.7981 | 104900 | 1.8492 |
| 2.8007 | 105000 | 1.8411 |
| 2.8034 | 105100 | 1.8239 |
| 2.8061 | 105200 | 1.8281 |
| 2.8087 | 105300 | 1.8667 |
| 2.8114 | 105400 | 1.8387 |
| 2.8141 | 105500 | 1.7910 |
| 2.8168 | 105600 | 1.8158 |
| 2.8194 | 105700 | 1.8675 |
| 2.8221 | 105800 | 1.7868 |
| 2.8248 | 105900 | 1.8229 |
| 2.8274 | 106000 | 1.7866 |
| 2.8301 | 106100 | 1.8143 |
| 2.8328 | 106200 | 1.8395 |
| 2.8354 | 106300 | 1.8093 |
| 2.8381 | 106400 | 1.8648 |
| 2.8408 | 106500 | 1.8174 |
| 2.8434 | 106600 | 1.8094 |
| 2.8461 | 106700 | 1.8048 |
| 2.8488 | 106800 | 1.8320 |
| 2.8514 | 106900 | 1.8567 |
| 2.8541 | 107000 | 1.7734 |
| 2.8568 | 107100 | 1.8563 |
| 2.8594 | 107200 | 1.8314 |
| 2.8621 | 107300 | 1.8382 |
| 2.8648 | 107400 | 1.8337 |
| 2.8674 | 107500 | 1.8409 |
| 2.8701 | 107600 | 1.7627 |
| 2.8728 | 107700 | 1.8070 |
| 2.8754 | 107800 | 1.7735 |
| 2.8781 | 107900 | 1.7653 |
| 2.8808 | 108000 | 1.8433 |
| 2.8834 | 108100 | 1.8314 |
| 2.8861 | 108200 | 1.7397 |
| 2.8888 | 108300 | 1.8554 |
| 2.8914 | 108400 | 1.7889 |
| 2.8941 | 108500 | 1.8494 |
| 2.8968 | 108600 | 1.7799 |
| 2.8994 | 108700 | 1.8705 |
| 2.9021 | 108800 | 1.8713 |
| 2.9048 | 108900 | 1.8773 |
| 2.9074 | 109000 | 1.7961 |
| 2.9101 | 109100 | 1.7701 |
| 2.9128 | 109200 | 1.7617 |
| 2.9154 | 109300 | 1.8439 |
| 2.9181 | 109400 | 1.7984 |
| 2.9208 | 109500 | 1.7584 |
| 2.9234 | 109600 | 1.8262 |
| 2.9261 | 109700 | 1.8536 |
| 2.9288 | 109800 | 1.8403 |
| 2.9314 | 109900 | 1.8340 |
| 2.9341 | 110000 | 1.8080 |
| 2.9368 | 110100 | 1.8559 |
| 2.9395 | 110200 | 1.8585 |
| 2.9421 | 110300 | 1.8512 |
| 2.9448 | 110400 | 1.8567 |
| 2.9475 | 110500 | 1.7880 |
| 2.9501 | 110600 | 1.7924 |
| 2.9528 | 110700 | 1.7786 |
| 2.9555 | 110800 | 1.8575 |
| 2.9581 | 110900 | 1.8359 |
| 2.9608 | 111000 | 1.7925 |
| 2.9635 | 111100 | 1.8890 |
| 2.9661 | 111200 | 1.8050 |
| 2.9688 | 111300 | 1.7856 |
| 2.9715 | 111400 | 1.8279 |
| 2.9741 | 111500 | 1.8478 |
| 2.9768 | 111600 | 1.8645 |
| 2.9795 | 111700 | 1.8395 |
| 2.9821 | 111800 | 1.8266 |
| 2.9848 | 111900 | 1.8055 |
| 2.9875 | 112000 | 1.8340 |
| 2.9901 | 112100 | 1.8107 |
| 2.9928 | 112200 | 1.8081 |
| 2.9955 | 112300 | 1.8026 |
| 2.9981 | 112400 | 1.8928 |
@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",
}
@misc{oord2019representationlearningcontrastivepredictive,
title={Representation Learning with Contrastive Predictive Coding},
author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
year={2019},
eprint={1807.03748},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1807.03748},
}
@article{10531646,
author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
year={2024},
doi={10.1109/TASLP.2024.3402087}
}