README.md

metadata

language:
  - hu
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1044013
  - loss:MultipleNegativesRankingLoss
base_model: tasksource/ModernBERT-base-nli
widget:
  - source_sentence: Emberek várnak a lámpánál kerékpárral.
    sentences:
      - Az emberek piros lámpánál haladnak.
      - Az emberek a kerékpárjukon vannak.
      - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában
  - source_sentence: A kutya a vízben van.
    sentences:
      - >-
        Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik
        pedig a tetőn.
      - A macska a vízben van, és dühös.
      - Egy kutya van a vízben, a szájában egy faág.
  - source_sentence: A nő feketét visel.
    sentences:
      - Egy barna kutya fröcsköl, ahogy úszik a vízben.
      - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre.
      - >-
        Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A
        képen:
  - source_sentence: Az emberek alszanak.
    sentences:
      - Három ember beszélget egy városi utcán.
      - A nő fehéret visel.
      - Egy apa és a fia ölelgeti alvás közben.
  - source_sentence: Az emberek alszanak.
    sentences:
      - >-
        Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében,
        miközben egy idősebb nő átmegy az utcán.
      - >-
        Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy
        bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép
        jobb oldalán lévő erős elmosódás tesz kivehetetlenné.
      - Egy apa és a fia ölelgeti alvás közben.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: ModernBERT-base
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.7102
            name: Cosine Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.67
            name: Cosine Accuracy

ModernBERT-base

This is a sentence-transformers model finetuned from tasksource/ModernBERT-base-nli on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: tasksource/ModernBERT-base-nli
• Maximum Sequence Length: 2048 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • train
• Language: hu
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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("karsar/ModernBERT-base-hu")
# Run inference
sentences = [
    'Az emberek alszanak.',
    'Egy apa és a fia ölelgeti alvás közben.',
    'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

• Datasets: all-nli-dev and all-nli-test
• Evaluated with TripletEvaluator

Metric	all-nli-dev	all-nli-test
cosine_accuracy	0.7102	0.67

Training Details

Training Dataset

train

• Dataset: train
• Size: 1,044,013 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 17.57 tokens max: 95 tokens	min: 8 tokens mean: 23.15 tokens max: 82 tokens	min: 9 tokens mean: 24.71 tokens max: 92 tokens

• Samples:

  anchor	positive	negative
  Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.	Egy ember a szabadban, lóháton.	Egy ember egy étteremben van, és omlettet rendel.
  Gyerekek mosolyogva és integetett a kamera	Gyermekek vannak jelen	A gyerekek homlokot rántanak
  Egy fiú ugrál a gördeszkát a közepén egy piros híd.	A fiú gördeszkás trükköt csinál.	A fiú korcsolyázik a járdán.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

train

• Dataset: train
• Size: 5,000 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 17.57 tokens max: 95 tokens	min: 8 tokens mean: 23.15 tokens max: 82 tokens	min: 9 tokens mean: 24.71 tokens max: 92 tokens

• Samples:

  anchor	positive	negative
  Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.	Egy ember a szabadban, lóháton.	Egy ember egy étteremben van, és omlettet rendel.
  Gyerekek mosolyogva és integetett a kamera	Gyermekek vannak jelen	A gyerekek homlokot rántanak
  Egy fiú ugrál a gördeszkát a közepén egy piros híd.	A fiú gördeszkás trükköt csinál.	A fiú korcsolyázik a járdán.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• 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: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: 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: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	train loss	all-nli-dev_cosine_accuracy	all-nli-test_cosine_accuracy
0	0	-	-	0.5306	-
0.0008	100	2.818	-	-	-
0.0015	200	2.5967	-	-	-
0.0023	300	2.5905	-	-	-
0.0031	400	2.4971	-	-	-
0.0038	500	2.5099	-	-	-
0.0046	600	2.3072	-	-	-
0.0054	700	1.883	-	-	-
0.0061	800	1.3657	-	-	-
0.0069	900	1.5772	-	-	-
0.0077	1000	1.2804	-	-	-
0.0084	1100	1.3332	-	-	-
0.0092	1200	1.0596	-	-	-
0.0100	1300	0.7385	-	-	-
0.0107	1400	0.8946	-	-	-
0.0115	1500	0.7507	-	-	-
0.0123	1600	0.7161	-	-	-
0.0130	1700	0.7176	-	-	-
0.0138	1800	0.8173	-	-	-
0.0146	1900	0.7464	-	-	-
0.0153	2000	0.7542	0.9124	0.7244	-
0.0161	2100	0.6147	-	-	-
0.0169	2200	0.5755	-	-	-
0.0176	2300	1.0103	-	-	-
0.0184	2400	0.6152	-	-	-
0.0192	2500	0.4283	-	-	-
0.0199	2600	0.5124	-	-	-
0.0207	2700	0.7056	-	-	-
0.0215	2800	0.4688	-	-	-
0.0222	2900	0.5029	-	-	-
0.0230	3000	0.5196	-	-	-
0.0238	3100	0.488	-	-	-
0.0245	3200	0.3601	-	-	-
0.0253	3300	0.6175	-	-	-
0.0261	3400	0.7494	-	-	-
0.0268	3500	0.5092	-	-	-
0.0276	3600	0.557	-	-	-
0.0284	3700	0.9289	-	-	-
0.0291	3800	0.6672	-	-	-
0.0299	3900	0.6498	-	-	-
0.0307	4000	0.3891	1.6045	0.6146	-
0.0314	4100	0.3863	-	-	-
0.0322	4200	0.6232	-	-	-
0.0329	4300	0.7755	-	-	-
0.0337	4400	0.4809	-	-	-
0.0345	4500	0.2968	-	-	-
0.0352	4600	0.3402	-	-	-
0.0360	4700	0.4622	-	-	-
0.0368	4800	0.7333	-	-	-
0.0375	4900	0.5718	-	-	-
0.0383	5000	0.7179	-	-	-
0.0391	5100	0.5597	-	-	-
0.0398	5200	0.4884	-	-	-
0.0406	5300	0.3467	-	-	-
0.0414	5400	0.4234	-	-	-
0.0421	5500	0.4149	-	-	-
0.0429	5600	0.4568	-	-	-
0.0437	5700	0.5416	-	-	-
0.0444	5800	0.4317	-	-	-
0.0452	5900	0.5537	-	-	-
0.0460	6000	0.5437	1.2609	0.6716	-
0.0467	6100	0.526	-	-	-
0.0475	6200	0.5891	-	-	-
0.0483	6300	0.5615	-	-	-
0.0490	6400	0.6012	-	-	-
0.0498	6500	0.6295	-	-	-
0.0506	6600	0.7951	-	-	-
0.0513	6700	0.7898	-	-	-
0.0521	6800	0.523	-	-	-
0.0529	6900	0.5871	-	-	-
0.0536	7000	0.6337	-	-	-
0.0544	7100	0.7428	-	-	-
0.0552	7200	0.6299	-	-	-
0.0559	7300	0.389	-	-	-
0.0567	7400	0.384	-	-	-
0.0575	7500	0.7069	-	-	-
0.0582	7600	0.5387	-	-	-
0.0590	7700	0.4936	-	-	-
0.0598	7800	0.579	-	-	-
0.0605	7900	0.8806	-	-	-
0.0613	8000	0.7165	1.3063	0.6654	-
0.0621	8100	0.5884	-	-	-
0.0628	8200	0.7272	-	-	-
0.0636	8300	0.8061	-	-	-
0.0644	8400	0.6705	-	-	-
0.0651	8500	0.79	-	-	-
0.0659	8600	1.0937	-	-	-
0.0667	8700	0.7903	-	-	-
0.0674	8800	0.423	-	-	-
0.0682	8900	0.8756	-	-	-
0.0690	9000	0.7461	-	-	-
0.0697	9100	0.7282	-	-	-
0.0705	9200	0.7343	-	-	-
0.0713	9300	0.5109	-	-	-
0.0720	9400	0.5142	-	-	-
0.0728	9500	0.6688	-	-	-
0.0736	9600	0.8799	-	-	-
0.0743	9700	0.6665	-	-	-
0.0751	9800	0.6979	-	-	-
0.0759	9900	0.6971	-	-	-
0.0766	10000	0.8392	1.5133	0.703	-
0.0774	10100	0.6283	-	-	-
0.0782	10200	0.6315	-	-	-
0.0789	10300	0.4937	-	-	-
0.0797	10400	0.4819	-	-	-
0.0805	10500	0.5177	-	-	-
0.0812	10600	0.637	-	-	-
0.0820	10700	0.584	-	-	-
0.0828	10800	0.9142	-	-	-
0.0835	10900	0.6953	-	-	-
0.0843	11000	0.7623	-	-	-
0.0851	11100	0.6357	-	-	-
0.0858	11200	0.7508	-	-	-
0.0866	11300	0.5425	-	-	-
0.0874	11400	0.596	-	-	-
0.0881	11500	0.8407	-	-	-
0.0889	11600	0.7463	-	-	-
0.0897	11700	0.9188	-	-	-
0.0904	11800	0.6921	-	-	-
0.0912	11900	0.7707	-	-	-
0.0920	12000	0.7206	1.6151	0.6568	-
0.0927	12100	0.7925	-	-	-
0.0935	12200	0.8842	-	-	-
0.0943	12300	0.8328	-	-	-
0.0950	12400	0.5571	-	-	-
0.0958	12500	0.9304	-	-	-
0.0966	12600	0.4566	-	-	-
0.0973	12700	0.5217	-	-	-
0.0981	12800	0.4589	-	-	-
0.0988	12900	0.4216	-	-	-
0.0996	13000	0.7141	-	-	-
0.1004	13100	0.6205	-	-	-
0.1011	13200	0.4045	-	-	-
0.1019	13300	0.3494	-	-	-
0.1027	13400	0.4802	-	-	-
0.1034	13500	0.4482	-	-	-
0.1042	13600	0.5367	-	-	-
0.1050	13700	0.3565	-	-	-
0.1057	13800	0.3069	-	-	-
0.1065	13900	0.3576	-	-	-
0.1073	14000	0.4572	0.6264	0.8504	-
0.1080	14100	0.3922	-	-	-
0.1088	14200	0.289	-	-	-
0.1096	14300	0.5305	-	-	-
0.1103	14400	0.5243	-	-	-
0.1111	14500	0.5738	-	-	-
0.1119	14600	0.3457	-	-	-
0.1126	14700	0.3254	-	-	-
0.1134	14800	0.6328	-	-	-
0.1142	14900	0.4711	-	-	-
0.1149	15000	0.2532	-	-	-
0.1157	15100	0.4379	-	-	-
0.1165	15200	0.4992	-	-	-
0.1172	15300	0.3239	-	-	-
0.1180	15400	0.3294	-	-	-
0.1188	15500	0.332	-	-	-
0.1195	15600	0.3025	-	-	-
0.1203	15700	0.2406	-	-	-
0.1211	15800	0.4625	-	-	-
0.1218	15900	0.5237	-	-	-
0.1226	16000	0.3451	1.2647	0.8454	-
0.1234	16100	0.5763	-	-	-
0.1241	16200	0.8095	-	-	-
0.1249	16300	0.5725	-	-	-
0.1257	16400	0.5191	-	-	-
0.1264	16500	0.3933	-	-	-
0.1272	16600	0.3892	-	-	-
0.1280	16700	0.5239	-	-	-
0.1287	16800	0.6505	-	-	-
0.1295	16900	0.3977	-	-	-
0.1303	17000	0.2333	-	-	-
0.1310	17100	0.3542	-	-	-
0.1318	17200	0.3516	-	-	-
0.1326	17300	0.5825	-	-	-
0.1333	17400	0.4237	-	-	-
0.1341	17500	0.5338	-	-	-
0.1349	17600	0.3754	-	-	-
0.1356	17700	0.4027	-	-	-
0.1364	17800	0.3067	-	-	-
0.1372	17900	0.4103	-	-	-
0.1379	18000	0.3567	1.5228	0.7474	-
0.1387	18100	0.356	-	-	-
0.1395	18200	0.6599	-	-	-
0.1402	18300	0.4607	-	-	-
0.1410	18400	0.5707	-	-	-
0.1418	18500	0.506	-	-	-
0.1425	18600	0.553	-	-	-
0.1433	18700	0.4427	-	-	-
0.1441	18800	0.4758	-	-	-
0.1448	18900	0.4573	-	-	-
0.1456	19000	0.5183	-	-	-
0.1464	19100	0.7152	-	-	-
0.1471	19200	0.6519	-	-	-
0.1479	19300	0.4398	-	-	-
0.1487	19400	0.6364	-	-	-
0.1494	19500	0.5541	-	-	-
0.1502	19600	0.5911	-	-	-
0.1510	19700	0.4827	-	-	-
0.1517	19800	0.3507	-	-	-
0.1525	19900	0.4048	-	-	-
0.1533	20000	0.5348	1.1738	0.7378	-
0.1540	20100	0.5465	-	-	-
0.1548	20200	0.4924	-	-	-
0.1556	20300	0.5436	-	-	-
0.1563	20400	0.7259	-	-	-
0.1571	20500	0.5202	-	-	-
0.1579	20600	0.5634	-	-	-
0.1586	20700	0.6697	-	-	-
0.1594	20800	0.7563	-	-	-
0.1602	20900	0.6669	-	-	-
0.1609	21000	0.8264	-	-	-
0.1617	21100	0.948	-	-	-
0.1624	21200	0.7443	-	-	-
0.1632	21300	0.3897	-	-	-
0.1640	21400	0.7757	-	-	-
0.1647	21500	0.7034	-	-	-
0.1655	21600	0.7031	-	-	-
0.1663	21700	0.6138	-	-	-
0.1670	21800	0.5235	-	-	-
0.1678	21900	0.5078	-	-	-
0.1686	22000	0.7041	1.3245	0.7018	-
0.1693	22100	0.8529	-	-	-
0.1701	22200	0.5939	-	-	-
0.1709	22300	0.642	-	-	-
0.1716	22400	0.5834	-	-	-
0.1724	22500	0.7157	-	-	-
0.1732	22600	0.5561	-	-	-
0.1739	22700	0.5861	-	-	-
0.1747	22800	0.4327	-	-	-
0.1755	22900	0.409	-	-	-
0.1762	23000	0.462	-	-	-
0.1770	23100	0.581	-	-	-
0.1778	23200	0.5704	-	-	-
0.1785	23300	0.8111	-	-	-
0.1793	23400	0.6905	-	-	-
0.1801	23500	0.6811	-	-	-
0.1808	23600	0.6078	-	-	-
0.1816	23700	0.6502	-	-	-
0.1824	23800	0.5575	-	-	-
0.1831	23900	0.5162	-	-	-
0.1839	24000	0.7487	1.5347	0.7036	-
0.1847	24100	0.7011	-	-	-
0.1854	24200	0.7417	-	-	-
0.1862	24300	0.6514	-	-	-
0.1870	24400	0.738	-	-	-
0.1877	24500	0.7296	-	-	-
0.1885	24600	0.6939	-	-	-
0.1893	24700	0.8072	-	-	-
0.1900	24800	0.7847	-	-	-
0.1908	24900	0.5243	-	-	-
0.1916	25000	0.8317	-	-	-
0.1923	25100	0.3981	-	-	-
0.1931	25200	0.4715	-	-	-
0.1939	25300	0.3734	-	-	-
0.1946	25400	0.43	-	-	-
0.1954	25500	0.6921	-	-	-
0.1962	25600	0.724	-	-	-
0.1969	25700	0.4203	-	-	-
0.1977	25800	0.3013	-	-	-
0.1985	25900	0.5666	-	-	-
0.1992	26000	0.454	0.5471	0.9028	-
0.2000	26100	0.4989	-	-	-
0.2008	26200	0.4614	-	-	-
0.2015	26300	0.2856	-	-	-
0.2023	26400	0.458	-	-	-
0.2031	26500	0.5247	-	-	-
0.2038	26600	0.4425	-	-	-
0.2046	26700	0.4603	-	-	-
0.2054	26800	0.6186	-	-	-
0.2061	26900	0.6571	-	-	-
0.2069	27000	0.6305	-	-	-
0.2077	27100	0.5351	-	-	-
0.2084	27200	0.3616	-	-	-
0.2092	27300	0.7269	-	-	-
0.2100	27400	0.4669	-	-	-
0.2107	27500	0.3201	-	-	-
0.2115	27600	0.4897	-	-	-
0.2123	27700	0.4902	-	-	-
0.2130	27800	0.3957	-	-	-
0.2138	27900	0.418	-	-	-
0.2146	28000	0.294	1.1809	0.8098	-
0.2153	28100	0.3014	-	-	-
0.2161	28200	0.2607	-	-	-
0.2169	28300	0.5948	-	-	-
0.2176	28400	0.5235	-	-	-
0.2184	28500	0.2864	-	-	-
0.2192	28600	0.4785	-	-	-
0.2199	28700	0.6624	-	-	-
0.2207	28800	0.4939	-	-	-
0.2215	28900	0.5468	-	-	-
0.2222	29000	0.2632	-	-	-
0.2230	29100	0.2601	-	-	-
0.2238	29200	0.4946	-	-	-
0.2245	29300	0.5398	-	-	-
0.2253	29400	0.3971	-	-	-
0.2261	29500	0.2474	-	-	-
0.2268	29600	0.2803	-	-	-
0.2276	29700	0.3461	-	-	-
0.2283	29800	0.569	-	-	-
0.2291	29900	0.3645	-	-	-
0.2299	30000	0.4675	0.7793	0.82	-
0.2306	30100	0.3114	-	-	-
0.2314	30200	0.2872	-	-	-
0.2322	30300	0.2524	-	-	-
0.2329	30400	0.3419	-	-	-
0.2337	30500	0.3456	-	-	-
0.2345	30600	0.2813	-	-	-
0.2352	30700	0.6423	-	-	-
0.2360	30800	0.5166	-	-	-
0.2368	30900	0.6356	-	-	-
0.2375	31000	0.6066	-	-	-
0.2383	31100	0.3955	-	-	-
0.2391	31200	0.4736	-	-	-
0.2398	31300	0.468	-	-	-
0.2406	31400	0.4473	-	-	-
0.2414	31500	0.5372	-	-	-
0.2421	31600	0.6317	-	-	-
0.2429	31700	0.6729	-	-	-
0.2437	31800	0.4201	-	-	-
0.2444	31900	0.5762	-	-	-
0.2452	32000	0.465	1.2451	0.711	-
0.2460	32100	0.5183	-	-	-
0.2467	32200	0.5787	-	-	-
0.2475	32300	0.2941	-	-	-
0.2483	32400	0.3294	-	-	-
0.2490	32500	0.5425	-	-	-
0.2498	32600	0.4231	-	-	-
0.2506	32700	0.4012	-	-	-
0.2513	32800	0.5048	-	-	-
0.2521	32900	0.7835	-	-	-
0.2529	33000	0.4841	-	-	-
0.2536	33100	0.5165	-	-	-
0.2544	33200	0.616	-	-	-
0.2552	33300	0.7964	-	-	-
0.2559	33400	0.6555	-	-	-
0.2567	33500	0.8277	-	-	-
0.2575	33600	0.968	-	-	-
0.2582	33700	0.7169	-	-	-
0.2590	33800	0.4022	-	-	-
0.2598	33900	0.7319	-	-	-
0.2605	34000	0.6992	1.1376	0.7298	-
0.2613	34100	0.6158	-	-	-
0.2621	34200	0.5864	-	-	-
0.2628	34300	0.4992	-	-	-
0.2636	34400	0.4612	-	-	-
0.2644	34500	0.6673	-	-	-
0.2651	34600	0.7744	-	-	-
0.2659	34700	0.5635	-	-	-
0.2667	34800	0.5776	-	-	-
0.2674	34900	0.5226	-	-	-
0.2682	35000	0.7188	-	-	-
0.2690	35100	0.5005	-	-	-
0.2697	35200	0.5778	-	-	-
0.2705	35300	0.4347	-	-	-
0.2713	35400	0.4557	-	-	-
0.2720	35500	0.4636	-	-	-
0.2728	35600	0.503	-	-	-
0.2736	35700	0.496	-	-	-
0.2743	35800	0.7014	-	-	-
0.2751	35900	0.6399	-	-	-
0.2759	36000	0.5515	1.0167	0.7976	-
0.2766	36100	0.5554	-	-	-
0.2774	36200	0.6826	-	-	-
0.2782	36300	0.588	-	-	-
0.2789	36400	0.5108	-	-	-
0.2797	36500	0.7009	-	-	-
0.2805	36600	0.5777	-	-	-
0.2812	36700	0.68	-	-	-
0.2820	36800	0.5866	-	-	-
0.2828	36900	0.6809	-	-	-
0.2835	37000	0.6074	-	-	-
0.2843	37100	0.5996	-	-	-
0.2851	37200	0.7923	-	-	-
0.2858	37300	0.7223	-	-	-
0.2866	37400	0.4707	-	-	-
0.2874	37500	0.8465	-	-	-
0.2881	37600	0.4312	-	-	-
0.2889	37700	0.4312	-	-	-
0.2897	37800	0.4297	-	-	-
0.2904	37900	0.3338	-	-	-
0.2912	38000	0.7997	0.8492	0.859	-
0.2919	38100	0.7267	-	-	-
0.2927	38200	0.453	-	-	-
0.2935	38300	0.3056	-	-	-
0.2942	38400	0.5767	-	-	-
0.2950	38500	0.4489	-	-	-
0.2958	38600	0.4936	-	-	-
0.2965	38700	0.4155	-	-	-
0.2973	38800	0.2844	-	-	-
0.2981	38900	0.3929	-	-	-
0.2988	39000	0.4238	-	-	-
0.2996	39100	0.4135	-	-	-
0.3004	39200	0.2814	-	-	-
0.3011	39300	0.6305	-	-	-
0.3019	39400	0.5786	-	-	-
0.3027	39500	0.6518	-	-	-
0.3034	39600	0.3739	-	-	-
0.3042	39700	0.2734	-	-	-
0.3050	39800	0.7305	-	-	-
0.3057	39900	0.46	-	-	-
0.3065	40000	0.3369	0.8074	0.8386	-
0.3073	40100	0.3867	-	-	-
0.3080	40200	0.5064	-	-	-
0.3088	40300	0.2786	-	-	-
0.3096	40400	0.2565	-	-	-
0.3103	40500	0.2664	-	-	-
0.3111	40600	0.2573	-	-	-
0.3119	40700	0.2323	-	-	-
0.3126	40800	0.4503	-	-	-
0.3134	40900	0.4634	-	-	-
0.3142	41000	0.2864	-	-	-
0.3149	41100	0.4351	-	-	-
0.3157	41200	0.6829	-	-	-
0.3165	41300	0.4354	-	-	-
0.3172	41400	0.4817	-	-	-
0.3180	41500	0.2635	-	-	-
0.3188	41600	0.3183	-	-	-
0.3195	41700	0.4315	-	-	-
0.3203	41800	0.6398	-	-	-
0.3211	41900	0.3831	-	-	-
0.3218	42000	0.195	0.9463	0.774	-
0.3226	42100	0.2588	-	-	-
0.3234	42200	0.3148	-	-	-
0.3241	42300	0.5142	-	-	-
0.3249	42400	0.3584	-	-	-
0.3257	42500	0.3459	-	-	-
0.3264	42600	0.2535	-	-	-
0.3272	42700	0.3347	-	-	-
0.3280	42800	0.2351	-	-	-
0.3287	42900	0.4061	-	-	-
0.3295	43000	0.3064	-	-	-
0.3303	43100	0.3565	-	-	-
0.3310	43200	0.6056	-	-	-
0.3318	43300	0.4197	-	-	-
0.3326	43400	0.5453	-	-	-
0.3333	43500	0.5166	-	-	-
0.3341	43600	0.3983	-	-	-
0.3349	43700	0.462	-	-	-
0.3356	43800	0.4676	-	-	-
0.3364	43900	0.4021	-	-	-
0.3372	44000	0.5591	0.8470	0.799	-
0.3379	44100	0.588	-	-	-
0.3387	44200	0.6152	-	-	-
0.3395	44300	0.3542	-	-	-
0.3402	44400	0.5048	-	-	-
0.3410	44500	0.4672	-	-	-
0.3418	44600	0.519	-	-	-
0.3425	44700	0.4492	-	-	-
0.3433	44800	0.2612	-	-	-
0.3441	44900	0.3205	-	-	-
0.3448	45000	0.4904	-	-	-
0.3456	45100	0.3813	-	-	-
0.3464	45200	0.4119	-	-	-
0.3471	45300	0.4968	-	-	-
0.3479	45400	0.5634	-	-	-
0.3487	45500	0.4638	-	-	-
0.3494	45600	0.4271	-	-	-
0.3502	45700	0.61	-	-	-
0.3510	45800	0.782	-	-	-
0.3517	45900	0.6051	-	-	-
0.3525	46000	0.8175	0.7792	0.7852	-
0.3533	46100	0.942	-	-	-
0.3540	46200	0.6811	-	-	-
0.3548	46300	0.376	-	-	-
0.3556	46400	0.704	-	-	-
0.3563	46500	0.6783	-	-	-
0.3571	46600	0.5986	-	-	-
0.3578	46700	0.5548	-	-	-
0.3586	46800	0.4983	-	-	-
0.3594	46900	0.402	-	-	-
0.3601	47000	0.6978	-	-	-
0.3609	47100	0.7503	-	-	-
0.3617	47200	0.5298	-	-	-
0.3624	47300	0.6138	-	-	-
0.3632	47400	0.5539	-	-	-
0.3640	47500	0.591	-	-	-
0.3647	47600	0.4999	-	-	-
0.3655	47700	0.4934	-	-	-
0.3663	47800	0.4399	-	-	-
0.3670	47900	0.4822	-	-	-
0.3678	48000	0.4384	1.0635	0.7896	-
0.3686	48100	0.5239	-	-	-
0.3693	48200	0.503	-	-	-
0.3701	48300	0.6655	-	-	-
0.3709	48400	0.6247	-	-	-
0.3716	48500	0.6036	-	-	-
0.3724	48600	0.524	-	-	-
0.3732	48700	0.6682	-	-	-
0.3739	48800	0.5122	-	-	-
0.3747	48900	0.4451	-	-	-
0.3755	49000	0.7625	-	-	-
0.3762	49100	0.6206	-	-	-
0.3770	49200	0.701	-	-	-
0.3778	49300	0.6259	-	-	-
0.3785	49400	0.6891	-	-	-
0.3793	49500	0.5746	-	-	-
0.3801	49600	0.7717	-	-	-
0.3808	49700	0.7877	-	-	-
0.3816	49800	0.7302	-	-	-
0.3824	49900	0.5473	-	-	-
0.3831	50000	0.7718	0.7419	0.8194	-
0.3839	50100	0.5886	-	-	-
0.3847	50200	0.4268	-	-	-
0.3854	50300	0.411	-	-	-
0.3862	50400	0.4831	-	-	-
0.3870	50500	0.5904	-	-	-
0.3877	50600	0.7364	-	-	-
0.3885	50700	0.4386	-	-	-
0.3893	50800	0.3428	-	-	-
0.3900	50900	0.5774	-	-	-
0.3908	51000	0.4221	-	-	-
0.3916	51100	0.4026	-	-	-
0.3923	51200	0.3776	-	-	-
0.3931	51300	0.203	-	-	-
0.3939	51400	0.5251	-	-	-
0.3946	51500	0.4351	-	-	-
0.3954	51600	0.353	-	-	-
0.3962	51700	0.2841	-	-	-
0.3969	51800	0.5105	-	-	-
0.3977	51900	0.4854	-	-	-
0.3985	52000	0.5926	0.4281	0.9072	-
0.3992	52100	0.4137	-	-	-
0.4000	52200	0.2775	-	-	-
0.4008	52300	0.7688	-	-	-
0.4015	52400	0.4428	-	-	-
0.4023	52500	0.289	-	-	-
0.4031	52600	0.414	-	-	-
0.4038	52700	0.3626	-	-	-
0.4046	52800	0.2762	-	-	-
0.4054	52900	0.3207	-	-	-
0.4061	53000	0.2421	-	-	-
0.4069	53100	0.2585	-	-	-
0.4077	53200	0.2573	-	-	-
0.4084	53300	0.439	-	-	-
0.4092	53400	0.4406	-	-	-
0.4100	53500	0.2119	-	-	-
0.4107	53600	0.3343	-	-	-
0.4115	53700	0.6587	-	-	-
0.4123	53800	0.4071	-	-	-
0.4130	53900	0.5403	-	-	-
0.4138	54000	0.2426	0.5609	0.8958	-
0.4146	54100	0.241	-	-	-
0.4153	54200	0.3912	-	-	-
0.4161	54300	0.5871	-	-	-
0.4169	54400	0.358	-	-	-
0.4176	54500	0.1675	-	-	-
0.4184	54600	0.2417	-	-	-
0.4192	54700	0.3239	-	-	-
0.4199	54800	0.3485	-	-	-
0.4207	54900	0.4117	-	-	-
0.4214	55000	0.5028	-	-	-
0.4222	55100	0.265	-	-	-
0.4230	55200	0.3147	-	-	-
0.4237	55300	0.2105	-	-	-
0.4245	55400	0.3083	-	-	-
0.4253	55500	0.2695	-	-	-
0.4260	55600	0.3412	-	-	-
0.4268	55700	0.6009	-	-	-
0.4276	55800	0.4202	-	-	-
0.4283	55900	0.5336	-	-	-
0.4291	56000	0.4002	0.8252	0.8624	-
0.4299	56100	0.4216	-	-	-
0.4306	56200	0.3402	-	-	-
0.4314	56300	0.4168	-	-	-
0.4322	56400	0.4411	-	-	-
0.4329	56500	0.5359	-	-	-
0.4337	56600	0.5151	-	-	-
0.4345	56700	0.6349	-	-	-
0.4352	56800	1.5813	-	-	-
0.4360	56900	1.6637	-	-	-
0.4368	57000	1.7604	-	-	-
0.4375	57100	2.0211	-	-	-
0.4383	57200	1.9293	-	-	-
0.4391	57300	1.9076	-	-	-
0.4398	57400	1.9143	-	-	-
0.4406	57500	1.7937	-	-	-
0.4414	57600	1.8207	-	-	-
0.4421	57700	1.813	-	-	-
0.4429	57800	1.7214	-	-	-
0.4437	57900	1.7229	-	-	-
0.4444	58000	1.6582	0.8077	0.7914	-
0.4452	58100	1.6518	-	-	-
0.4460	58200	1.6449	-	-	-
0.4467	58300	1.568	-	-	-
0.4475	58400	1.5673	-	-	-
0.4483	58500	1.5651	-	-	-
0.4490	58600	1.5778	-	-	-
0.4498	58700	1.5195	-	-	-
0.4506	58800	1.4874	-	-	-
0.4513	58900	1.4405	-	-	-
0.4521	59000	1.4545	-	-	-
0.4529	59100	1.5086	-	-	-
0.4536	59200	1.4828	-	-	-
0.4544	59300	1.4325	-	-	-
0.4552	59400	1.4079	-	-	-
0.4559	59500	1.4081	-	-	-
0.4567	59600	1.3905	-	-	-
0.4575	59700	1.4186	-	-	-
0.4582	59800	1.41	-	-	-
0.4590	59900	1.4407	-	-	-
0.4598	60000	1.3896	0.7229	0.8242	-
0.4605	60100	1.3876	-	-	-
0.4613	60200	1.3545	-	-	-
0.4621	60300	1.3546	-	-	-
0.4628	60400	1.3438	-	-	-
0.4636	60500	1.3356	-	-	-
0.4644	60600	1.3805	-	-	-
0.4651	60700	1.4346	-	-	-
0.4659	60800	1.521	-	-	-
0.4667	60900	1.6184	-	-	-
0.4674	61000	1.5194	-	-	-
0.4682	61100	1.5911	-	-	-
0.4690	61200	1.5137	-	-	-
0.4697	61300	1.5644	-	-	-
0.4705	61400	1.5005	-	-	-
0.4713	61500	1.5356	-	-	-
0.4720	61600	1.4965	-	-	-
0.4728	61700	1.4623	-	-	-
0.4736	61800	1.4455	-	-	-
0.4743	61900	1.4451	-	-	-
0.4751	62000	1.4847	0.7731	0.8068	-
0.4759	62100	1.5076	-	-	-
0.4766	62200	1.4562	-	-	-
0.4774	62300	1.4864	-	-	-
0.4782	62400	1.4849	-	-	-
0.4789	62500	1.4504	-	-	-
0.4797	62600	1.4445	-	-	-
0.4805	62700	1.4533	-	-	-
0.4812	62800	1.3457	-	-	-
0.4820	62900	1.4672	-	-	-
0.4828	63000	1.3975	-	-	-
0.4835	63100	1.4895	-	-	-
0.4843	63200	1.3786	-	-	-
0.4851	63300	1.4722	-	-	-
0.4858	63400	1.4289	-	-	-
0.4866	63500	1.4191	-	-	-
0.4873	63600	1.4131	-	-	-
0.4881	63700	1.4592	-	-	-
0.4889	63800	1.4045	-	-	-
0.4896	63900	1.4086	-	-	-
0.4904	64000	1.3957	0.9342	0.7664	-
0.4912	64100	1.4041	-	-	-
0.4919	64200	1.3465	-	-	-
0.4927	64300	1.3846	-	-	-
0.4935	64400	1.4103	-	-	-
0.4942	64500	1.453	-	-	-
0.4950	64600	1.3668	-	-	-
0.4958	64700	1.426	-	-	-
0.4965	64800	1.4182	-	-	-
0.4973	64900	1.4032	-	-	-
0.4981	65000	1.3454	-	-	-
0.4988	65100	1.3173	-	-	-
0.4996	65200	1.3348	-	-	-
0.5004	65300	1.3542	-	-	-
0.5011	65400	1.3372	-	-	-
0.5019	65500	1.2873	-	-	-
0.5027	65600	1.3441	-	-	-
0.5034	65700	1.3465	-	-	-
0.5042	65800	1.3539	-	-	-
0.5050	65900	1.2753	-	-	-
0.5057	66000	1.349	1.0317	0.7534	-
0.5065	66100	1.3076	-	-	-
0.5073	66200	1.3808	-	-	-
0.5080	66300	1.3185	-	-	-
0.5088	66400	1.334	-	-	-
0.5096	66500	1.35	-	-	-
0.5103	66600	1.3469	-	-	-
0.5111	66700	1.3219	-	-	-
0.5119	66800	1.2597	-	-	-
0.5126	66900	1.3477	-	-	-
0.5134	67000	1.3463	-	-	-
0.5142	67100	1.2914	-	-	-
0.5149	67200	1.3025	-	-	-
0.5157	67300	1.2898	-	-	-
0.5165	67400	1.316	-	-	-
0.5172	67500	1.3588	-	-	-
0.5180	67600	1.3554	-	-	-
0.5188	67700	1.2989	-	-	-
0.5195	67800	1.3239	-	-	-
0.5203	67900	1.3008	-	-	-
0.5211	68000	1.325	1.0629	0.7204	-
0.5218	68100	1.3205	-	-	-
0.5226	68200	1.2946	-	-	-
0.5234	68300	1.3071	-	-	-
0.5241	68400	1.3348	-	-	-
0.5249	68500	1.3374	-	-	-
0.5257	68600	1.323	-	-	-
0.5264	68700	1.2934	-	-	-
0.5272	68800	1.3721	-	-	-
0.5280	68900	1.2767	-	-	-
0.5287	69000	1.2603	-	-	-
0.5295	69100	1.2686	-	-	-
0.5303	69200	1.2532	-	-	-
0.5310	69300	1.2824	-	-	-
0.5318	69400	1.2738	-	-	-
0.5326	69500	1.3338	-	-	-
0.5333	69600	1.2718	-	-	-
0.5341	69700	1.3239	-	-	-
0.5349	69800	1.2581	-	-	-
0.5356	69900	1.2255	-	-	-
0.5364	70000	1.3209	0.9075	0.7632	-
0.5372	70100	1.2825	-	-	-
0.5379	70200	1.2475	-	-	-
0.5387	70300	1.2232	-	-	-
0.5395	70400	1.267	-	-	-
0.5402	70500	1.2777	-	-	-
0.5410	70600	1.2831	-	-	-
0.5418	70700	1.2507	-	-	-
0.5425	70800	1.2428	-	-	-
0.5433	70900	1.2765	-	-	-
0.5441	71000	1.2613	-	-	-
0.5448	71100	1.2286	-	-	-
0.5456	71200	1.2344	-	-	-
0.5464	71300	1.2684	-	-	-
0.5471	71400	1.233	-	-	-
0.5479	71500	1.2488	-	-	-
0.5487	71600	1.2544	-	-	-
0.5494	71700	1.3167	-	-	-
0.5502	71800	1.256	-	-	-
0.5509	71900	1.3045	-	-	-
0.5517	72000	1.2699	0.9015	0.7592	-
0.5525	72100	1.2664	-	-	-
0.5532	72200	1.24	-	-	-
0.5540	72300	1.2285	-	-	-
0.5548	72400	1.2797	-	-	-
0.5555	72500	1.1958	-	-	-
0.5563	72600	1.2537	-	-	-
0.5571	72700	1.255	-	-	-
0.5578	72800	1.2302	-	-	-
0.5586	72900	1.2105	-	-	-
0.5594	73000	1.2941	-	-	-
0.5601	73100	1.2283	-	-	-
0.5609	73200	1.2497	-	-	-
0.5617	73300	1.2425	-	-	-
0.5624	73400	1.1892	-	-	-
0.5632	73500	1.1626	-	-	-
0.5640	73600	1.2128	-	-	-
0.5647	73700	1.1867	-	-	-
0.5655	73800	1.1584	-	-	-
0.5663	73900	1.2393	-	-	-
0.5670	74000	1.2418	0.9238	0.7524	-
0.5678	74100	1.1947	-	-	-
0.5686	74200	1.1706	-	-	-
0.5693	74300	1.2323	-	-	-
0.5701	74400	1.1935	-	-	-
0.5709	74500	1.1717	-	-	-
0.5716	74600	1.1926	-	-	-
0.5724	74700	1.1835	-	-	-
0.5732	74800	1.1998	-	-	-
0.5739	74900	1.1909	-	-	-
0.5747	75000	1.2149	-	-	-
0.5755	75100	1.2231	-	-	-
0.5762	75200	1.1709	-	-	-
0.5770	75300	1.2359	-	-	-
0.5778	75400	1.2179	-	-	-
0.5785	75500	1.2352	-	-	-
0.5793	75600	1.2049	-	-	-
0.5801	75700	1.2066	-	-	-
0.5808	75800	1.1898	-	-	-
0.5816	75900	1.1925	-	-	-
0.5824	76000	1.2601	1.0085	0.7544	-
0.5831	76100	1.1366	-	-	-
0.5839	76200	1.2151	-	-	-
0.5847	76300	1.2266	-	-	-
0.5854	76400	1.1512	-	-	-
0.5862	76500	1.1519	-	-	-
0.5870	76600	1.1471	-	-	-
0.5877	76700	1.1844	-	-	-
0.5885	76800	1.2006	-	-	-
0.5893	76900	1.2498	-	-	-
0.5900	77000	1.2298	-	-	-
0.5908	77100	1.1835	-	-	-
0.5916	77200	1.1874	-	-	-
0.5923	77300	1.2055	-	-	-
0.5931	77400	1.199	-	-	-
0.5939	77500	1.1363	-	-	-
0.5946	77600	1.1442	-	-	-
0.5954	77700	1.204	-	-	-
0.5962	77800	1.1489	-	-	-
0.5969	77900	1.1989	-	-	-
0.5977	78000	1.1938	0.9296	0.7794	-
0.5985	78100	1.2019	-	-	-
0.5992	78200	1.1795	-	-	-
0.6000	78300	1.1761	-	-	-
0.6008	78400	1.1299	-	-	-
0.6015	78500	1.1738	-	-	-
0.6023	78600	1.2198	-	-	-
0.6031	78700	1.1572	-	-	-
0.6038	78800	1.167	-	-	-
0.6046	78900	1.1832	-	-	-
0.6054	79000	1.1631	-	-	-
0.6061	79100	1.1197	-	-	-
0.6069	79200	1.1409	-	-	-
0.6077	79300	1.1452	-	-	-
0.6084	79400	1.1731	-	-	-
0.6092	79500	1.152	-	-	-
0.6100	79600	1.2185	-	-	-
0.6107	79700	1.1443	-	-	-
0.6115	79800	1.1746	-	-	-
0.6123	79900	1.1176	-	-	-
0.6130	80000	1.1944	1.1048	0.7202	-
0.6138	80100	1.1751	-	-	-
0.6145	80200	1.2293	-	-	-
0.6153	80300	1.1731	-	-	-
0.6161	80400	1.1711	-	-	-
0.6168	80500	1.1819	-	-	-
0.6176	80600	1.1692	-	-	-
0.6184	80700	1.1536	-	-	-
0.6191	80800	1.1936	-	-	-
0.6199	80900	1.1829	-	-	-
0.6207	81000	1.1719	-	-	-
0.6214	81100	1.2001	-	-	-
0.6222	81200	1.1501	-	-	-
0.6230	81300	1.1463	-	-	-
0.6237	81400	1.1487	-	-	-
0.6245	81500	1.1625	-	-	-
0.6253	81600	1.1482	-	-	-
0.6260	81700	1.147	-	-	-
0.6268	81800	1.1497	-	-	-
0.6276	81900	1.1446	-	-	-
0.6283	82000	1.1701	1.1050	0.7162	-
0.6291	82100	1.0952	-	-	-
0.6299	82200	1.1361	-	-	-
0.6306	82300	1.1584	-	-	-
0.6314	82400	1.1553	-	-	-
0.6322	82500	1.1621	-	-	-
0.6329	82600	1.1685	-	-	-
0.6337	82700	1.1698	-	-	-
0.6345	82800	1.1936	-	-	-
0.6352	82900	1.1631	-	-	-
0.6360	83000	1.1443	-	-	-
0.6368	83100	1.1511	-	-	-
0.6375	83200	1.1565	-	-	-
0.6383	83300	1.1384	-	-	-
0.6391	83400	1.1279	-	-	-
0.6398	83500	1.1306	-	-	-
0.6406	83600	1.1461	-	-	-
0.6414	83700	1.1621	-	-	-
0.6421	83800	1.1153	-	-	-
0.6429	83900	1.0626	-	-	-
0.6437	84000	1.1729	1.0598	0.724	-
0.6444	84100	1.1122	-	-	-
0.6452	84200	1.0985	-	-	-
0.6460	84300	1.1135	-	-	-
0.6467	84400	1.1886	-	-	-
0.6475	84500	1.1443	-	-	-
0.6483	84600	1.1627	-	-	-
0.6490	84700	1.088	-	-	-
0.6498	84800	1.1294	-	-	-
0.6506	84900	1.1251	-	-	-
0.6513	85000	1.1218	-	-	-
0.6521	85100	1.1628	-	-	-
0.6529	85200	1.1177	-	-	-
0.6536	85300	1.1522	-	-	-
0.6544	85400	1.1203	-	-	-
0.6552	85500	1.06	-	-	-
0.6559	85600	1.1197	-	-	-
0.6567	85700	1.109	-	-	-
0.6575	85800	1.0544	-	-	-
0.6582	85900	1.0987	-	-	-
0.6590	86000	1.0792	1.1465	0.7206	-
0.6598	86100	1.1276	-	-	-
0.6605	86200	1.099	-	-	-
0.6613	86300	1.1688	-	-	-
0.6621	86400	1.0562	-	-	-
0.6628	86500	1.0725	-	-	-
0.6636	86600	1.1031	-	-	-
0.6644	86700	1.0967	-	-	-
0.6651	86800	1.063	-	-	-
0.6659	86900	1.0409	-	-	-
0.6667	87000	1.1811	-	-	-
0.6674	87100	1.1475	-	-	-
0.6682	87200	1.0248	-	-	-
0.6690	87300	1.0646	-	-	-
0.6697	87400	1.0778	-	-	-
0.6705	87500	1.0745	-	-	-
0.6713	87600	1.0975	-	-	-
0.6720	87700	1.0581	-	-	-
0.6728	87800	1.0377	-	-	-
0.6736	87900	1.1017	-	-	-
0.6743	88000	1.0689	1.1524	0.7198	-
0.6751	88100	1.1284	-	-	-
0.6759	88200	1.0736	-	-	-
0.6766	88300	1.0742	-	-	-
0.6774	88400	1.1067	-	-	-
0.6782	88500	1.0623	-	-	-
0.6789	88600	1.0904	-	-	-
0.6797	88700	1.0958	-	-	-
0.6804	88800	1.0329	-	-	-
0.6812	88900	1.0613	-	-	-
0.6820	89000	1.0771	-	-	-
0.6827	89100	1.0558	-	-	-
0.6835	89200	1.0439	-	-	-
0.6843	89300	1.049	-	-	-
0.6850	89400	1.0905	-	-	-
0.6858	89500	1.0629	-	-	-
0.6866	89600	1.0661	-	-	-
0.6873	89700	1.1135	-	-	-
0.6881	89800	1.0671	-	-	-
0.6889	89900	1.0554	-	-	-
0.6896	90000	1.0414	0.9706	0.7508	-
0.6904	90100	1.028	-	-	-
0.6912	90200	1.0442	-	-	-
0.6919	90300	1.0573	-	-	-
0.6927	90400	1.0233	-	-	-
0.6935	90500	1.0326	-	-	-
0.6942	90600	1.0188	-	-	-
0.6950	90700	1.0358	-	-	-
0.6958	90800	1.0623	-	-	-
0.6965	90900	0.9863	-	-	-
0.6973	91000	1.0535	-	-	-
0.6981	91100	1.0135	-	-	-
0.6988	91200	1.1006	-	-	-
0.6996	91300	1.0666	-	-	-
0.7004	91400	0.9967	-	-	-
0.7011	91500	1.0445	-	-	-
0.7019	91600	1.0351	-	-	-
0.7027	91700	1.0524	-	-	-
0.7034	91800	1.0272	-	-	-
0.7042	91900	1.0171	-	-	-
0.7050	92000	1.0912	1.1023	0.725	-
0.7057	92100	1.0064	-	-	-
0.7065	92200	1.0634	-	-	-
0.7073	92300	1.0212	-	-	-
0.7080	92400	1.0396	-	-	-
0.7088	92500	1.0301	-	-	-
0.7096	92600	1.0441	-	-	-
0.7103	92700	1.0298	-	-	-
0.7111	92800	1.0597	-	-	-
0.7119	92900	1.0236	-	-	-
0.7126	93000	1.0165	-	-	-
0.7134	93100	1.0502	-	-	-
0.7142	93200	1.0019	-	-	-
0.7149	93300	1.024	-	-	-
0.7157	93400	1.0316	-	-	-
0.7165	93500	1.019	-	-	-
0.7172	93600	1.0441	-	-	-
0.7180	93700	0.9956	-	-	-
0.7188	93800	1.0471	-	-	-
0.7195	93900	1.0043	-	-	-
0.7203	94000	0.988	1.2226	0.7072	-
0.7211	94100	1.0491	-	-	-
0.7218	94200	0.9958	-	-	-
0.7226	94300	1.0438	-	-	-
0.7234	94400	1.0214	-	-	-
0.7241	94500	1.0251	-	-	-
0.7249	94600	0.9478	-	-	-
0.7257	94700	1.0485	-	-	-
0.7264	94800	0.9732	-	-	-
0.7272	94900	0.9509	-	-	-
0.7280	95000	1.0087	-	-	-
0.7287	95100	1.0122	-	-	-
0.7295	95200	1.0374	-	-	-
0.7303	95300	0.9906	-	-	-
0.7310	95400	1.0056	-	-	-
0.7318	95500	1.0247	-	-	-
0.7326	95600	1.0125	-	-	-
0.7333	95700	0.9832	-	-	-
0.7341	95800	0.9603	-	-	-
0.7349	95900	1.0088	-	-	-
0.7356	96000	0.994	1.1378	0.7204	-
0.7364	96100	0.9937	-	-	-
0.7372	96200	1.0542	-	-	-
0.7379	96300	0.9718	-	-	-
0.7387	96400	0.9873	-	-	-
0.7395	96500	1.0182	-	-	-
0.7402	96600	1.0015	-	-	-
0.7410	96700	1.0489	-	-	-
0.7418	96800	1.0042	-	-	-
0.7425	96900	1.0933	-	-	-
0.7433	97000	0.9977	-	-	-
0.7440	97100	0.9944	-	-	-
0.7448	97200	1.039	-	-	-
0.7456	97300	0.9722	-	-	-
0.7463	97400	1.0472	-	-	-
0.7471	97500	0.9454	-	-	-
0.7479	97600	0.9808	-	-	-
0.7486	97700	0.9875	-	-	-
0.7494	97800	1.0396	-	-	-
0.7502	97900	0.9982	-	-	-
0.7509	98000	0.968	1.1959	0.6948	-
0.7517	98100	1.0557	-	-	-
0.7525	98200	0.9831	-	-	-
0.7532	98300	0.9735	-	-	-
0.7540	98400	1.0383	-	-	-
0.7548	98500	0.9509	-	-	-
0.7555	98600	0.9619	-	-	-
0.7563	98700	0.9943	-	-	-
0.7571	98800	0.961	-	-	-
0.7578	98900	0.9769	-	-	-
0.7586	99000	1.0056	-	-	-
0.7594	99100	1.0257	-	-	-
0.7601	99200	0.9277	-	-	-
0.7609	99300	0.947	-	-	-
0.7617	99400	0.9904	-	-	-
0.7624	99500	0.955	-	-	-
0.7632	99600	0.9978	-	-	-
0.7640	99700	0.938	-	-	-
0.7647	99800	0.9999	-	-	-
0.7655	99900	0.988	-	-	-
0.7663	100000	0.986	1.1583	0.7184	-
0.7670	100100	1.0122	-	-	-
0.7678	100200	0.9872	-	-	-
0.7686	100300	0.9829	-	-	-
0.7693	100400	0.9791	-	-	-
0.7701	100500	1.0423	-	-	-
0.7709	100600	0.9619	-	-	-
0.7716	100700	0.9736	-	-	-
0.7724	100800	0.9769	-	-	-
0.7732	100900	0.9629	-	-	-
0.7739	101000	1.0041	-	-	-
0.7747	101100	0.9206	-	-	-
0.7755	101200	0.9799	-	-	-
0.7762	101300	0.9833	-	-	-
0.7770	101400	0.9734	-	-	-
0.7778	101500	1.0135	-	-	-
0.7785	101600	0.9531	-	-	-
0.7793	101700	0.964	-	-	-
0.7801	101800	0.9997	-	-	-
0.7808	101900	0.9935	-	-	-
0.7816	102000	0.9955	1.1245	0.7108	-
0.7824	102100	1.0063	-	-	-
0.7831	102200	0.9694	-	-	-
0.7839	102300	1.0218	-	-	-
0.7847	102400	0.9829	-	-	-
0.7854	102500	0.9296	-	-	-
0.7862	102600	0.9445	-	-	-
0.7870	102700	1.0018	-	-	-
0.7877	102800	0.9922	-	-	-
0.7885	102900	0.9909	-	-	-
0.7893	103000	1.0044	-	-	-
0.7900	103100	0.9517	-	-	-
0.7908	103200	0.9839	-	-	-
0.7916	103300	0.9815	-	-	-
0.7923	103400	0.9659	-	-	-
0.7931	103500	0.9523	-	-	-
0.7939	103600	0.9765	-	-	-
0.7946	103700	0.9637	-	-	-
0.7954	103800	0.9755	-	-	-
0.7962	103900	0.9678	-	-	-
0.7969	104000	0.9523	1.1226	0.7162	-
0.7977	104100	0.9122	-	-	-
0.7985	104200	0.9592	-	-	-
0.7992	104300	0.9193	-	-	-
0.8000	104400	0.9978	-	-	-
0.8008	104500	0.9319	-	-	-
0.8015	104600	0.9774	-	-	-
0.8023	104700	0.9367	-	-	-
0.8031	104800	1.0004	-	-	-
0.8038	104900	0.9217	-	-	-
0.8046	105000	0.9178	-	-	-
0.8054	105100	0.9663	-	-	-
0.8061	105200	0.954	-	-	-
0.8069	105300	0.9788	-	-	-
0.8077	105400	0.9637	-	-	-
0.8084	105500	0.943	-	-	-
0.8092	105600	0.9638	-	-	-
0.8099	105700	0.9842	-	-	-
0.8107	105800	0.9395	-	-	-
0.8115	105900	0.9389	-	-	-
0.8122	106000	0.9854	1.3013	0.6852	-
0.8130	106100	0.9753	-	-	-
0.8138	106200	0.9149	-	-	-
0.8145	106300	0.9607	-	-	-
0.8153	106400	0.9677	-	-	-
0.8161	106500	0.9344	-	-	-
0.8168	106600	0.9421	-	-	-
0.8176	106700	0.9477	-	-	-
0.8184	106800	0.8942	-	-	-
0.8191	106900	0.9954	-	-	-
0.8199	107000	0.9055	-	-	-
0.8207	107100	0.8938	-	-	-
0.8214	107200	0.9232	-	-	-
0.8222	107300	0.9558	-	-	-
0.8230	107400	0.9662	-	-	-
0.8237	107500	0.9581	-	-	-
0.8245	107600	0.9284	-	-	-
0.8253	107700	0.954	-	-	-
0.8260	107800	0.9625	-	-	-
0.8268	107900	0.9435	-	-	-
0.8276	108000	0.9776	1.2523	0.7004	-
0.8283	108100	0.9071	-	-	-
0.8291	108200	0.9719	-	-	-
0.8299	108300	0.9507	-	-	-
0.8306	108400	0.934	-	-	-
0.8314	108500	0.9362	-	-	-
0.8322	108600	0.8922	-	-	-
0.8329	108700	0.9779	-	-	-
0.8337	108800	0.8624	-	-	-
0.8345	108900	0.9046	-	-	-
0.8352	109000	0.9152	-	-	-
0.8360	109100	0.933	-	-	-
0.8368	109200	0.9054	-	-	-
0.8375	109300	0.9383	-	-	-
0.8383	109400	0.9204	-	-	-
0.8391	109500	0.8786	-	-	-
0.8398	109600	0.933	-	-	-
0.8406	109700	0.8507	-	-	-
0.8414	109800	0.8931	-	-	-
0.8421	109900	0.9045	-	-	-
0.8429	110000	0.926	1.2050	0.7128	-
0.8437	110100	0.9332	-	-	-
0.8444	110200	0.9144	-	-	-
0.8452	110300	0.8979	-	-	-
0.8460	110400	0.9142	-	-	-
0.8467	110500	0.8362	-	-	-
0.8475	110600	0.9579	-	-	-
0.8483	110700	0.8762	-	-	-
0.8490	110800	0.8719	-	-	-
0.8498	110900	0.9494	-	-	-
0.8506	111000	0.8743	-	-	-
0.8513	111100	0.9266	-	-	-
0.8521	111200	0.8943	-	-	-
0.8529	111300	0.908	-	-	-
0.8536	111400	0.8316	-	-	-
0.8544	111500	0.8574	-	-	-
0.8552	111600	0.8587	-	-	-
0.8559	111700	0.8862	-	-	-
0.8567	111800	0.8303	-	-	-
0.8575	111900	0.8252	-	-	-
0.8582	112000	0.8726	1.2884	0.704	-
0.8590	112100	0.8788	-	-	-
0.8598	112200	0.8197	-	-	-
0.8605	112300	0.8406	-	-	-
0.8613	112400	0.8634	-	-	-
0.8621	112500	0.8347	-	-	-
0.8628	112600	0.8558	-	-	-
0.8636	112700	0.8313	-	-	-
0.8644	112800	0.8221	-	-	-
0.8651	112900	0.8508	-	-	-
0.8659	113000	0.8214	-	-	-
0.8667	113100	0.8185	-	-	-
0.8674	113200	0.8198	-	-	-
0.8682	113300	0.8681	-	-	-
0.8690	113400	0.8676	-	-	-
0.8697	113500	0.838	-	-	-
0.8705	113600	0.8215	-	-	-
0.8713	113700	0.8468	-	-	-
0.8720	113800	0.807	-	-	-
0.8728	113900	0.8392	-	-	-
0.8735	114000	0.8318	1.2201	0.7272	-
0.8743	114100	0.824	-	-	-
0.8751	114200	0.7816	-	-	-
0.8758	114300	0.8095	-	-	-
0.8766	114400	0.8412	-	-	-
0.8774	114500	0.8514	-	-	-
0.8781	114600	0.836	-	-	-
0.8789	114700	0.8375	-	-	-
0.8797	114800	0.8267	-	-	-
0.8804	114900	0.7925	-	-	-
0.8812	115000	0.7948	-	-	-
0.8820	115100	0.7908	-	-	-
0.8827	115200	0.8092	-	-	-
0.8835	115300	0.7942	-	-	-
0.8843	115400	0.7551	-	-	-
0.8850	115500	0.7717	-	-	-
0.8858	115600	0.764	-	-	-
0.8866	115700	0.8025	-	-	-
0.8873	115800	0.8182	-	-	-
0.8881	115900	0.7158	-	-	-
0.8889	116000	0.7632	1.2520	0.7258	-
0.8896	116100	0.7593	-	-	-
0.8904	116200	0.8032	-	-	-
0.8912	116300	0.7809	-	-	-
0.8919	116400	0.7251	-	-	-
0.8927	116500	0.802	-	-	-
0.8935	116600	0.7949	-	-	-
0.8942	116700	0.7827	-	-	-
0.8950	116800	0.79	-	-	-
0.8958	116900	0.7668	-	-	-
0.8965	117000	0.8076	-	-	-
0.8973	117100	0.7533	-	-	-
0.8981	117200	0.752	-	-	-
0.8988	117300	0.7225	-	-	-
0.8996	117400	0.7694	-	-	-
0.9004	117500	0.7941	-	-	-
0.9011	117600	0.7548	-	-	-
0.9019	117700	0.7617	-	-	-
0.9027	117800	0.7693	-	-	-
0.9034	117900	0.7304	-	-	-
0.9042	118000	0.7955	1.2687	0.7324	-
0.9050	118100	0.7567	-	-	-
0.9057	118200	0.7253	-	-	-
0.9065	118300	0.756	-	-	-
0.9073	118400	0.7583	-	-	-
0.9080	118500	0.7559	-	-	-
0.9088	118600	0.7462	-	-	-
0.9096	118700	0.7433	-	-	-
0.9103	118800	0.7701	-	-	-
0.9111	118900	0.7541	-	-	-
0.9119	119000	0.7084	-	-	-
0.9126	119100	0.7207	-	-	-
0.9134	119200	0.7435	-	-	-
0.9142	119300	0.7528	-	-	-
0.9149	119400	0.7179	-	-	-
0.9157	119500	0.7276	-	-	-
0.9165	119600	0.7307	-	-	-
0.9172	119700	0.7484	-	-	-
0.9180	119800	1.1472	-	-	-
0.9188	119900	1.318	-	-	-
0.9195	120000	1.4042	1.2499	0.7238	-
0.9203	120100	1.2455	-	-	-
0.9211	120200	1.3026	-	-	-
0.9218	120300	1.0223	-	-	-
0.9226	120400	1.0531	-	-	-
0.9234	120500	1.1072	-	-	-
0.9241	120600	1.0879	-	-	-
0.9249	120700	1.0663	-	-	-
0.9257	120800	0.9457	-	-	-
0.9264	120900	1.1087	-	-	-
0.9272	121000	1.0896	-	-	-
0.9280	121100	1.0722	-	-	-
0.9287	121200	1.0552	-	-	-
0.9295	121300	1.1524	-	-	-
0.9303	121400	1.0925	-	-	-
0.9310	121500	1.0797	-	-	-
0.9318	121600	1.0849	-	-	-
0.9326	121700	1.0821	-	-	-
0.9333	121800	1.0373	-	-	-
0.9341	121900	1.1126	-	-	-
0.9349	122000	1.1339	1.2329	0.7186	-
0.9356	122100	1.1165	-	-	-
0.9364	122200	1.0295	-	-	-
0.9372	122300	0.9693	-	-	-
0.9379	122400	0.9914	-	-	-
0.9387	122500	0.9932	-	-	-
0.9394	122600	0.9796	-	-	-
0.9402	122700	0.9148	-	-	-
0.9410	122800	1.0079	-	-	-
0.9417	122900	0.9644	-	-	-
0.9425	123000	0.975	-	-	-
0.9433	123100	0.9657	-	-	-
0.9440	123200	0.944	-	-	-
0.9448	123300	1.0591	-	-	-
0.9456	123400	1.0175	-	-	-
0.9463	123500	0.9659	-	-	-
0.9471	123600	1.0315	-	-	-
0.9479	123700	0.9716	-	-	-
0.9486	123800	0.9887	-	-	-
0.9494	123900	0.9873	-	-	-
0.9502	124000	1.006	1.2707	0.7128	-
0.9509	124100	0.978	-	-	-
0.9517	124200	0.9194	-	-	-
0.9525	124300	0.8852	-	-	-
0.9532	124400	0.9265	-	-	-
0.9540	124500	0.956	-	-	-
0.9548	124600	0.87	-	-	-
0.9555	124700	0.8636	-	-	-
0.9563	124800	0.8749	-	-	-
0.9571	124900	0.8762	-	-	-
0.9578	125000	0.8432	-	-	-
0.9586	125100	0.8887	-	-	-
0.9594	125200	0.9097	-	-	-
0.9601	125300	0.823	-	-	-
0.9609	125400	0.8976	-	-	-
0.9617	125500	1.1025	-	-	-
0.9624	125600	0.9468	-	-	-
0.9632	125700	0.9733	-	-	-
0.9640	125800	0.9353	-	-	-
0.9647	125900	0.8633	-	-	-
0.9655	126000	0.9196	1.2893	0.7106	-
0.9663	126100	0.8514	-	-	-
0.9670	126200	0.8513	-	-	-
0.9678	126300	0.8597	-	-	-
0.9686	126400	0.929	-	-	-
0.9693	126500	0.9397	-	-	-
0.9701	126600	1.1423	-	-	-
0.9709	126700	1.0804	-	-	-
0.9716	126800	1.035	-	-	-
0.9724	126900	1.046	-	-	-
0.9732	127000	1.0308	-	-	-
0.9739	127100	0.985	-	-	-
0.9747	127200	1.0493	-	-	-
0.9755	127300	1.6257	-	-	-
0.9762	127400	0.9725	-	-	-
0.9770	127500	0.907	-	-	-
0.9778	127600	0.7219	-	-	-
0.9785	127700	0.7454	-	-	-
0.9793	127800	0.8149	-	-	-
0.9801	127900	0.6161	-	-	-
0.9808	128000	0.6559	1.3419	0.7106	-
0.9816	128100	0.6442	-	-	-
0.9824	128200	0.6461	-	-	-
0.9831	128300	0.7225	-	-	-
0.9839	128400	0.7092	-	-	-
0.9847	128500	0.7962	-	-	-
0.9854	128600	0.6362	-	-	-
0.9862	128700	0.815	-	-	-
0.9870	128800	0.7114	-	-	-
0.9877	128900	0.699	-	-	-
0.9885	129000	0.6741	-	-	-
0.9893	129100	0.5924	-	-	-
0.9900	129200	0.6305	-	-	-
0.9908	129300	0.9403	-	-	-
0.9916	129400	0.6837	-	-	-
0.9923	129500	0.8546	-	-	-
0.9931	129600	0.9752	-	-	-
0.9939	129700	0.9003	-	-	-
0.9946	129800	0.9449	-	-	-
0.9954	129900	0.871	-	-	-
0.9962	130000	0.7516	1.3455	0.7102	-
0.9969	130100	0.9024	-	-	-
0.9977	130200	0.8981	-	-	-
0.9985	130300	1.247	-	-	-
0.9992	130400	0.9558	-	-	-
1.0000	130500	0.9919	-	-	-
1.0	130502	-	-	-	0.67

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0.dev0
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}