cyberbabooshka/bge_large_ft1

SentenceTransformer based on BAAI/bge-large-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5. It maps sentences & paragraphs to a 1024-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: BAAI/bge-large-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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("cyberbabooshka/bge_large_ft1")
# Run inference
sentences = [
    'What is the relationship between the smallest perturbation of a matrix and its rank, as established in theorems regarding matrix perturbations?',
    '"Suppose $A \\in C^{m \\times n}$ has full column rank (= n). Then $\\min _{\\Delta \\in \\mathbb{C}^{m \\times n}}\\left\\{\\|\\Delta\\|_{2} \\mid A+\\Delta \\text { has rank }<n\\right\\}=\\sigma_{n}(A)$."',
    '"If a beam of light enters and then exits the elevator, the observer on Earth and the one accelerating in empty space must observe the same thing, since they cannot distinguish between being on Earth or accelerating in space. The observer in space, who is accelerating, will observe that the beam of light bends as it crosses the elevator... that means that if the path of a beam of light is curved near Earth, it must be because space itself is curved in the presence of a gravitational field!"',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

• Dataset: eval
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6048
cosine_accuracy@3	0.7357
cosine_accuracy@5	0.7833
cosine_accuracy@10	0.8286
cosine_precision@1	0.6048
cosine_precision@3	0.2452
cosine_precision@5	0.1567
cosine_precision@10	0.0829
cosine_recall@1	0.6048
cosine_recall@3	0.7357
cosine_recall@5	0.7833
cosine_recall@10	0.8286
cosine_ndcg@10	0.7135
cosine_mrr@10	0.6768
cosine_map@100	0.6824

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,760 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 9 tokens mean: 24.87 tokens max: 70 tokens	min: 11 tokens mean: 68.37 tokens max: 500 tokens

• Samples:

  anchor	positive
  How is a proper coloring of a graph defined in the context of vertices and edges?	"A coloring is called proper if for each edge joining two distinct vertices, the two vertices it joins have different colors."
  What is the relationship between the first excited state of the box model and the p orbitals in a hydrogen atom?	"The p orbitals are similar to the first excited state of the box, i.e. $(n_{x},n_{y},n_{z})=(2,1,1)$ is similar to a $p_{x}$ orbital, $(n_{x},n_{y},n_{z})=(1,2,1)$ is similar to a $p_{y}$ orbital and $(n_{x},n_{y},n_{z})=(1,1,2)$ is similar to a $p_{z}$ orbital."
  How can the behavior of the derivative ( f'(x) ) indicate the presence of a local maximum or minimum at a critical point ( x=a )?	"If there is a local maximum when ( x=a ), the function must be lower near ( x=a ) than it is right at ( x=a ). If the derivative exists near ( x=a ), this means ( f'(x)>0 ) when ( x ) is near ( a ) and ( x < a ), because the function must 'slope up' just to the left of ( a ). Similarly, ( f'(x) < 0 ) when ( x ) is near ( a ) and ( x>a ), because ( f ) slopes down from the local maximum as we move to the right. Using the same reasoning, if there is a local minimum at ( x=a ), the derivative of ( f ) must be negative just to the left of ( a ) and positive just to the right."

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 420 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 420 samples:

  	anchor	positive
  type	string	string
  details	min: 12 tokens mean: 24.97 tokens max: 66 tokens	min: 7 tokens mean: 68.52 tokens max: 452 tokens

• Samples:

  anchor	positive
  What are the two central classes mentioned in the FileSystem framework and what do they represent?	"The class FileReference is the most important entry point to the framework." and "FileSystem is a powerful and elegant library to manipulate files."
  What is the significance of Turing's work in the context of PDE-based models for self-organization of complex systems?	"Turing’s monumental work on the chemical basis of morphogenesis played an important role in igniting researchers’ attention to the PDE-based continuous field models as a mathematical framework to study self-organization of complex systems."
  What are the two options for reducing accelerations as discussed in the passage?	"From the above definitions we see that there are really two options for reducing accelerations. We can reduce the amount that velocity changes, or we can increase the time over which the velocity changes (or both)."

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• weight_decay: 0.05
• num_train_epochs: 10
• warmup_ratio: 0.1
• fp16: True
• eval_on_start: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.05
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 10
• 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: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: 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
• 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: True
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	eval_cosine_ndcg@10
0	0	-	0.1387	0.6755
0.0091	1	0.1723	-	-
0.0182	2	0.1424	-	-
0.0273	3	0.094	-	-
0.0364	4	0.171	-	-
0.0455	5	0.1704	-	-
0.0545	6	0.2255	-	-
0.0636	7	0.0829	-	-
0.0727	8	0.1805	-	-
0.0818	9	0.2143	-	-
0.0909	10	0.1342	-	-
0.1	11	0.1318	-	-
0.1091	12	0.1247	-	-
0.1182	13	0.1459	-	-
0.1273	14	0.1489	-	-
0.1364	15	0.0667	-	-
0.1455	16	0.1029	-	-
0.1545	17	0.0576	-	-
0.1636	18	0.0617	-	-
0.1727	19	0.0622	-	-
0.1818	20	0.1624	-	-
0.1909	21	0.1393	-	-
0.2	22	0.1337	-	-
0.2091	23	0.0386	-	-
0.2182	24	0.0573	-	-
0.2273	25	0.095	-	-
0.2364	26	0.2023	-	-
0.2455	27	0.0318	-	-
0.2545	28	0.1544	-	-
0.2636	29	0.0148	-	-
0.2727	30	0.0235	-	-
0.2818	31	0.319	-	-
0.2909	32	0.0533	-	-
0.3	33	0.1831	-	-
0.3091	34	0.0278	-	-
0.3182	35	0.0693	-	-
0.3273	36	0.0296	-	-
0.3364	37	0.0131	-	-
0.3455	38	0.0279	-	-
0.3545	39	0.0193	-	-
0.3636	40	0.0193	-	-
0.3727	41	0.0884	-	-
0.3818	42	0.0248	-	-
0.3909	43	0.0223	-	-
0.4	44	0.0185	-	-
0.4091	45	0.0243	-	-
0.4182	46	0.1678	-	-
0.4273	47	0.0174	-	-
0.4364	48	0.1237	-	-
0.4455	49	0.0601	-	-
0.4545	50	0.0213	-	-
0.4636	51	0.0171	-	-
0.4727	52	0.0724	-	-
0.4818	53	0.0798	-	-
0.4909	54	0.0051	-	-
0.5	55	0.0035	-	-
0.5091	56	0.0143	-	-
0.5182	57	0.0113	-	-
0.5273	58	0.0393	-	-
0.5364	59	0.0325	-	-
0.5455	60	0.0287	-	-
0.5545	61	0.0143	-	-
0.5636	62	0.0124	-	-
0.5727	63	0.0055	-	-
0.5818	64	0.0215	-	-
0.5909	65	0.1446	-	-
0.6	66	0.3691	-	-
0.6091	67	0.0713	-	-
0.6182	68	0.03	-	-
0.6273	69	0.0348	-	-
0.6364	70	0.0091	-	-
0.6455	71	0.0321	-	-
0.6545	72	0.1088	-	-
0.6636	73	0.0982	-	-
0.6727	74	0.0311	-	-
0.6818	75	0.0727	-	-
0.6909	76	0.015	-	-
0.7	77	0.1231	-	-
0.7091	78	0.0963	-	-
0.7182	79	0.0232	-	-
0.7273	80	0.0256	-	-
0.7364	81	0.0176	-	-
0.7455	82	0.0494	-	-
0.7545	83	0.0306	-	-
0.7636	84	0.0427	-	-
0.7727	85	0.1109	-	-
0.7818	86	0.0317	-	-
0.7909	87	0.024	-	-
0.8	88	0.0093	-	-
0.8091	89	0.0224	-	-
0.8182	90	0.0283	-	-
0.8273	91	0.1035	-	-
0.8364	92	0.0067	-	-
0.8455	93	0.0066	-	-
0.8545	94	0.0649	-	-
0.8636	95	0.0054	-	-
0.8727	96	0.008	-	-
0.8818	97	0.0114	-	-
0.8909	98	0.0083	-	-
0.9	99	0.0472	-	-
0.9091	100	0.159	-	-
0.9182	101	0.032	-	-
0.9273	102	0.0335	-	-
0.9364	103	0.0059	-	-
0.9455	104	0.0038	-	-
0.9545	105	0.0194	-	-
0.9636	106	0.0233	-	-
0.9727	107	0.0699	-	-
0.9818	108	0.0042	-	-
0.9909	109	0.0168	-	-
1.0	110	0.0175	0.0277	0.7131
1.0091	111	0.0185	-	-
1.0182	112	0.0133	-	-
1.0273	113	0.0046	-	-
1.0364	114	0.0074	-	-
1.0455	115	0.0063	-	-
1.0545	116	0.0082	-	-
1.0636	117	0.0745	-	-
1.0727	118	0.0117	-	-
1.0818	119	0.0123	-	-
1.0909	120	0.0226	-	-
1.1	121	0.0031	-	-
1.1091	122	0.0031	-	-
1.1182	123	0.0064	-	-
1.1273	124	0.0048	-	-
1.1364	125	0.0037	-	-
1.1455	126	0.04	-	-
1.1545	127	0.0026	-	-
1.1636	128	0.0043	-	-
1.1727	129	0.1271	-	-
1.1818	130	0.0026	-	-
1.1909	131	0.0027	-	-
1.2	132	0.0699	-	-
1.2091	133	0.0321	-	-
1.2182	134	0.0026	-	-
1.2273	135	0.1767	-	-
1.2364	136	0.0323	-	-
1.2455	137	0.0115	-	-
1.2545	138	0.0026	-	-
1.2636	139	0.0355	-	-
1.2727	140	0.0036	-	-
1.2818	141	0.0036	-	-
1.2909	142	0.003	-	-
1.3	143	0.0043	-	-
1.3091	144	0.0027	-	-
1.3182	145	0.0031	-	-
1.3273	146	0.0224	-	-
1.3364	147	0.0013	-	-
1.3455	148	0.0841	-	-
1.3545	149	0.0045	-	-
1.3636	150	0.0069	-	-
1.3727	151	0.0048	-	-
1.3818	152	0.0023	-	-
1.3909	153	0.0041	-	-
1.4	154	0.0106	-	-
1.4091	155	0.0015	-	-
1.4182	156	0.0015	-	-
1.4273	157	0.001	-	-
1.4364	158	0.005	-	-
1.4455	159	0.0693	-	-
1.4545	160	0.0921	-	-
1.4636	161	0.0033	-	-
1.4727	162	0.0176	-	-
1.4818	163	0.0038	-	-
1.4909	164	0.0024	-	-
1.5	165	0.01	-	-
1.5091	166	0.0009	-	-
1.5182	167	0.0121	-	-
1.5273	168	0.0109	-	-
1.5364	169	0.0099	-	-
1.5455	170	0.0079	-	-
1.5545	171	0.0111	-	-
1.5636	172	0.0113	-	-
1.5727	173	0.0078	-	-
1.5818	174	0.0023	-	-
1.5909	175	0.0036	-	-
1.6	176	0.0233	-	-
1.6091	177	0.0188	-	-
1.6182	178	0.0024	-	-
1.6273	179	0.0008	-	-
1.6364	180	0.0023	-	-
1.6455	181	0.0008	-	-
1.6545	182	0.0037	-	-
1.6636	183	0.0004	-	-
1.6727	184	0.0026	-	-
1.6818	185	0.0045	-	-
1.6909	186	0.0032	-	-
1.7	187	0.0405	-	-
1.7091	188	0.0146	-	-
1.7182	189	0.009	-	-
1.7273	190	0.0067	-	-
1.7364	191	0.01	-	-
1.7455	192	0.0099	-	-
1.7545	193	0.1142	-	-
1.7636	194	0.0213	-	-
1.7727	195	0.0037	-	-
1.7818	196	0.0073	-	-
1.7909	197	0.0236	-	-
1.8	198	0.0165	-	-
1.8091	199	0.002	-	-
1.8182	200	0.0016	-	-
1.8273	201	0.0044	-	-
1.8364	202	0.0151	-	-
1.8455	203	0.0115	-	-
1.8545	204	0.0023	-	-
1.8636	205	0.007	-	-
1.8727	206	0.0426	-	-
1.8818	207	0.0019	-	-
1.8909	208	0.0421	-	-
1.9	209	0.0107	-	-
1.9091	210	0.004	-	-
1.9182	211	0.005	-	-
1.9273	212	0.0038	-	-
1.9364	213	0.0159	-	-
1.9455	214	0.0039	-	-
1.9545	215	0.0032	-	-
1.9636	216	0.0038	-	-
1.9727	217	0.0042	-	-
1.9818	218	0.0077	-	-
1.9909	219	0.0041	-	-
2.0	220	0.0035	0.0231	0.7277
2.0091	221	0.0252	-	-
2.0182	222	0.0031	-	-
2.0273	223	0.0156	-	-
2.0364	224	0.0029	-	-
2.0455	225	0.0027	-	-
2.0545	226	0.004	-	-
2.0636	227	0.0013	-	-
2.0727	228	0.0021	-	-
2.0818	229	0.0616	-	-
2.0909	230	0.0011	-	-
2.1	231	0.0008	-	-
2.1091	232	0.0014	-	-
2.1182	233	0.0068	-	-
2.1273	234	0.0045	-	-
2.1364	235	0.0009	-	-
2.1455	236	0.0009	-	-
2.1545	237	0.0025	-	-
2.1636	238	0.0074	-	-
2.1727	239	0.0021	-	-
2.1818	240	0.0021	-	-
2.1909	241	0.0032	-	-
2.2	242	0.0005	-	-
2.2091	243	0.0025	-	-
2.2182	244	0.001	-	-
2.2273	245	0.0512	-	-
2.2364	246	0.0015	-	-
2.2455	247	0.0023	-	-
2.2545	248	0.0058	-	-
2.2636	249	0.0025	-	-
2.2727	250	0.0016	-	-
2.2818	251	0.0053	-	-
2.2909	252	0.0043	-	-
2.3	253	0.0015	-	-
2.3091	254	0.0022	-	-
2.3182	255	0.0019	-	-
2.3273	256	0.001	-	-
2.3364	257	0.0019	-	-
2.3455	258	0.002	-	-
2.3545	259	0.0037	-	-
2.3636	260	0.0012	-	-
2.3727	261	0.0294	-	-
2.3818	262	0.0306	-	-
2.3909	263	0.0014	-	-
2.4	264	0.001	-	-
2.4091	265	0.0013	-	-
2.4182	266	0.0048	-	-
2.4273	267	0.0071	-	-
2.4364	268	0.0072	-	-
2.4455	269	0.0031	-	-
2.4545	270	0.001	-	-
2.4636	271	0.0009	-	-
2.4727	272	0.0014	-	-
2.4818	273	0.0007	-	-
2.4909	274	0.0035	-	-
2.5	275	0.0124	-	-
2.5091	276	0.0014	-	-
2.5182	277	0.0011	-	-
2.5273	278	0.0007	-	-
2.5364	279	0.0055	-	-
2.5455	280	0.0141	-	-
2.5545	281	0.0013	-	-
2.5636	282	0.0203	-	-
2.5727	283	0.0026	-	-
2.5818	284	0.0006	-	-
2.5909	285	0.0026	-	-
2.6	286	0.001	-	-
2.6091	287	0.0649	-	-
2.6182	288	0.0083	-	-
2.6273	289	0.0011	-	-
2.6364	290	0.002	-	-
2.6455	291	0.0013	-	-
2.6545	292	0.0086	-	-
2.6636	293	0.1389	-	-
2.6727	294	0.0006	-	-
2.6818	295	0.0024	-	-
2.6909	296	0.0013	-	-
2.7	297	0.003	-	-
2.7091	298	0.0016	-	-
2.7182	299	0.001	-	-
2.7273	300	0.0016	-	-
2.7364	301	0.022	-	-
2.7455	302	0.0022	-	-
2.7545	303	0.0014	-	-
2.7636	304	0.0013	-	-
2.7727	305	0.0047	-	-
2.7818	306	0.0015	-	-
2.7909	307	0.0009	-	-
2.8	308	0.0006	-	-
2.8091	309	0.0029	-	-
2.8182	310	0.0009	-	-
2.8273	311	0.0098	-	-
2.8364	312	0.0077	-	-
2.8455	313	0.0018	-	-
2.8545	314	0.0093	-	-
2.8636	315	0.0072	-	-
2.8727	316	0.0013	-	-
2.8818	317	0.0169	-	-
2.8909	318	0.0014	-	-
2.9	319	0.0007	-	-
2.9091	320	0.0013	-	-
2.9182	321	0.001	-	-
2.9273	322	0.0111	-	-
2.9364	323	0.0039	-	-
2.9455	324	0.0014	-	-
2.9545	325	0.0013	-	-
2.9636	326	0.0008	-	-
2.9727	327	0.006	-	-
2.9818	328	0.0006	-	-
2.9909	329	0.0008	-	-
3.0	330	0.0019	0.0281	0.7094
3.0091	331	0.0007	-	-
3.0182	332	0.0013	-	-
3.0273	333	0.0008	-	-
3.0364	334	0.0033	-	-
3.0455	335	0.0007	-	-
3.0545	336	0.0011	-	-
3.0636	337	0.0828	-	-
3.0727	338	0.0023	-	-
3.0818	339	0.0004	-	-
3.0909	340	0.001	-	-
3.1	341	0.0003	-	-
3.1091	342	0.0085	-	-
3.1182	343	0.0009	-	-
3.1273	344	0.0162	-	-
3.1364	345	0.0022	-	-
3.1455	346	0.0049	-	-
3.1545	347	0.0018	-	-
3.1636	348	0.0017	-	-
3.1727	349	0.0061	-	-
3.1818	350	0.0432	-	-
3.1909	351	0.0064	-	-
3.2	352	0.0016	-	-
3.2091	353	0.002	-	-
3.2182	354	0.0157	-	-
3.2273	355	0.0004	-	-
3.2364	356	0.0153	-	-
3.2455	357	0.0016	-	-
3.2545	358	0.0009	-	-
3.2636	359	0.0025	-	-
3.2727	360	0.0049	-	-
3.2818	361	0.0036	-	-
3.2909	362	0.0024	-	-
3.3	363	0.0008	-	-
3.3091	364	0.0042	-	-
3.3182	365	0.0014	-	-
3.3273	366	0.0044	-	-
3.3364	367	0.0547	-	-
3.3455	368	0.0015	-	-
3.3545	369	0.0072	-	-
3.3636	370	0.0012	-	-
3.3727	371	0.0017	-	-
3.3818	372	0.0014	-	-
3.3909	373	0.0038	-	-
3.4	374	0.0071	-	-
3.4091	375	0.0004	-	-
3.4182	376	0.0007	-	-
3.4273	377	0.0014	-	-
3.4364	378	0.0053	-	-
3.4455	379	0.0043	-	-
3.4545	380	0.0017	-	-
3.4636	381	0.0011	-	-
3.4727	382	0.0004	-	-
3.4818	383	0.0034	-	-
3.4909	384	0.0016	-	-
3.5	385	0.0015	-	-
3.5091	386	0.0009	-	-
3.5182	387	0.0045	-	-
3.5273	388	0.0067	-	-
3.5364	389	0.0011	-	-
3.5455	390	0.0024	-	-
3.5545	391	0.0025	-	-
3.5636	392	0.0007	-	-
3.5727	393	0.0008	-	-
3.5818	394	0.0006	-	-
3.5909	395	0.0036	-	-
3.6	396	0.0004	-	-
3.6091	397	0.001	-	-
3.6182	398	0.0009	-	-
3.6273	399	0.0014	-	-
3.6364	400	0.0006	-	-
3.6455	401	0.0053	-	-
3.6545	402	0.0008	-	-
3.6636	403	0.0008	-	-
3.6727	404	0.0016	-	-
3.6818	405	0.0029	-	-
3.6909	406	0.0046	-	-
3.7	407	0.0009	-	-
3.7091	408	0.0107	-	-
3.7182	409	0.001	-	-
3.7273	410	0.005	-	-
3.7364	411	0.0111	-	-
3.7455	412	0.0026	-	-
3.7545	413	0.0003	-	-
3.7636	414	0.0074	-	-
3.7727	415	0.0008	-	-
3.7818	416	0.0007	-	-
3.7909	417	0.0006	-	-
3.8	418	0.0021	-	-
3.8091	419	0.0009	-	-
3.8182	420	0.0373	-	-
3.8273	421	0.0009	-	-
3.8364	422	0.0008	-	-
3.8455	423	0.0004	-	-
3.8545	424	0.0026	-	-
3.8636	425	0.0022	-	-
3.8727	426	0.0016	-	-
3.8818	427	0.0867	-	-
3.8909	428	0.002	-	-
3.9	429	0.0022	-	-
3.9091	430	0.013	-	-
3.9182	431	0.005	-	-
3.9273	432	0.0007	-	-
3.9364	433	0.0079	-	-
3.9455	434	0.0015	-	-
3.9545	435	0.0003	-	-
3.9636	436	0.0009	-	-
3.9727	437	0.0005	-	-
3.9818	438	0.0015	-	-
3.9909	439	0.0113	-	-
4.0	440	0.164	0.0230	0.7213
4.0091	441	0.0032	-	-
4.0182	442	0.0006	-	-
4.0273	443	0.0105	-	-
4.0364	444	0.0007	-	-
4.0455	445	0.0046	-	-
4.0545	446	0.002	-	-
4.0636	447	0.0008	-	-
4.0727	448	0.0008	-	-
4.0818	449	0.0019	-	-
4.0909	450	0.0321	-	-
4.1	451	0.0253	-	-
4.1091	452	0.0008	-	-
4.1182	453	0.0011	-	-
4.1273	454	0.0008	-	-
4.1364	455	0.0047	-	-
4.1455	456	0.0004	-	-
4.1545	457	0.0008	-	-
4.1636	458	0.0085	-	-
4.1727	459	0.0012	-	-
4.1818	460	0.0013	-	-
4.1909	461	0.001	-	-
4.2	462	0.0008	-	-
4.2091	463	0.0259	-	-
4.2182	464	0.0015	-	-
4.2273	465	0.0008	-	-
4.2364	466	0.0043	-	-
4.2455	467	0.0013	-	-
4.2545	468	0.0009	-	-
4.2636	469	0.002	-	-
4.2727	470	0.0041	-	-
4.2818	471	0.0008	-	-
4.2909	472	0.0776	-	-
4.3	473	0.0016	-	-
4.3091	474	0.0014	-	-
4.3182	475	0.0039	-	-
4.3273	476	0.0299	-	-
4.3364	477	0.0017	-	-
4.3455	478	0.002	-	-
4.3545	479	0.001	-	-
4.3636	480	0.0052	-	-
4.3727	481	0.0012	-	-
4.3818	482	0.0005	-	-
4.3909	483	0.0014	-	-
4.4	484	0.0107	-	-
4.4091	485	0.0014	-	-
4.4182	486	0.0008	-	-
4.4273	487	0.0025	-	-
4.4364	488	0.0016	-	-
4.4455	489	0.0006	-	-
4.4545	490	0.0007	-	-
4.4636	491	0.0009	-	-
4.4727	492	0.0012	-	-
4.4818	493	0.001	-	-
4.4909	494	0.0013	-	-
4.5	495	0.0015	-	-
4.5091	496	0.001	-	-
4.5182	497	0.0008	-	-
4.5273	498	0.0008	-	-
4.5364	499	0.0022	-	-
4.5455	500	0.0008	-	-
4.5545	501	0.0006	-	-
4.5636	502	0.005	-	-
4.5727	503	0.0017	-	-
4.5818	504	0.0012	-	-
4.5909	505	0.0021	-	-
4.6	506	0.012	-	-
4.6091	507	0.0021	-	-
4.6182	508	0.0058	-	-
4.6273	509	0.007	-	-
4.6364	510	0.0006	-	-
4.6455	511	0.0021	-	-
4.6545	512	0.0019	-	-
4.6636	513	0.0031	-	-
4.6727	514	0.005	-	-
4.6818	515	0.0004	-	-
4.6909	516	0.0008	-	-
4.7	517	0.0042	-	-
4.7091	518	0.0011	-	-
4.7182	519	0.0003	-	-
4.7273	520	0.0007	-	-
4.7364	521	0.0007	-	-
4.7455	522	0.0006	-	-
4.7545	523	0.0012	-	-
4.7636	524	0.003	-	-
4.7727	525	0.0007	-	-
4.7818	526	0.0005	-	-
4.7909	527	0.0012	-	-
4.8	528	0.0013	-	-
4.8091	529	0.0028	-	-
4.8182	530	0.0004	-	-
4.8273	531	0.0011	-	-
4.8364	532	0.0054	-	-
4.8455	533	0.0006	-	-
4.8545	534	0.0352	-	-
4.8636	535	0.001	-	-
4.8727	536	0.0003	-	-
4.8818	537	0.0017	-	-
4.8909	538	0.0016	-	-
4.9	539	0.0006	-	-
4.9091	540	0.0007	-	-
4.9182	541	0.0006	-	-
4.9273	542	0.0042	-	-
4.9364	543	0.0012	-	-
4.9455	544	0.0016	-	-
4.9545	545	0.0014	-	-
4.9636	546	0.0004	-	-
4.9727	547	0.0006	-	-
4.9818	548	0.0023	-	-
4.9909	549	0.0003	-	-
5.0	550	0.0038	0.0314	0.7022
5.0091	551	0.0006	-	-
5.0182	552	0.0005	-	-
5.0273	553	0.0011	-	-
5.0364	554	0.0024	-	-
5.0455	555	0.0039	-	-
5.0545	556	0.0003	-	-
5.0636	557	0.001	-	-
5.0727	558	0.0017	-	-
5.0818	559	0.0004	-	-
5.0909	560	0.0005	-	-
5.1	561	0.0014	-	-
5.1091	562	0.0029	-	-
5.1182	563	0.0004	-	-
5.1273	564	0.0004	-	-
5.1364	565	0.0009	-	-
5.1455	566	0.007	-	-
5.1545	567	0.0018	-	-
5.1636	568	0.0004	-	-
5.1727	569	0.0003	-	-
5.1818	570	0.0299	-	-
5.1909	571	0.0008	-	-
5.2	572	0.0126	-	-
5.2091	573	0.0002	-	-
5.2182	574	0.0303	-	-
5.2273	575	0.0003	-	-
5.2364	576	0.0005	-	-
5.2455	577	0.0012	-	-
5.2545	578	0.0009	-	-
5.2636	579	0.0074	-	-
5.2727	580	0.0007	-	-
5.2818	581	0.0009	-	-
5.2909	582	0.0007	-	-
5.3	583	0.0063	-	-
5.3091	584	0.0008	-	-
5.3182	585	0.0009	-	-
5.3273	586	0.002	-	-
5.3364	587	0.0003	-	-
5.3455	588	0.0026	-	-
5.3545	589	0.0008	-	-
5.3636	590	0.0008	-	-
5.3727	591	0.0008	-	-
5.3818	592	0.0011	-	-
5.3909	593	0.0003	-	-
5.4	594	0.0009	-	-
5.4091	595	0.0017	-	-
5.4182	596	0.0018	-	-
5.4273	597	0.0004	-	-
5.4364	598	0.001	-	-
5.4455	599	0.0139	-	-
5.4545	600	0.0006	-	-
5.4636	601	0.001	-	-
5.4727	602	0.0011	-	-
5.4818	603	0.0004	-	-
5.4909	604	0.0004	-	-
5.5	605	0.0005	-	-
5.5091	606	0.0061	-	-
5.5182	607	0.0004	-	-
5.5273	608	0.0005	-	-
5.5364	609	0.0045	-	-
5.5455	610	0.0025	-	-
5.5545	611	0.0011	-	-
5.5636	612	0.0019	-	-
5.5727	613	0.0004	-	-
5.5818	614	0.0049	-	-
5.5909	615	0.0006	-	-
5.6	616	0.0017	-	-
5.6091	617	0.0017	-	-
5.6182	618	0.0005	-	-
5.6273	619	0.0003	-	-
5.6364	620	0.0019	-	-
5.6455	621	0.0005	-	-
5.6545	622	0.0009	-	-
5.6636	623	0.0049	-	-
5.6727	624	0.0005	-	-
5.6818	625	0.0006	-	-
5.6909	626	0.0006	-	-
5.7	627	0.0019	-	-
5.7091	628	0.0037	-	-
5.7182	629	0.0005	-	-
5.7273	630	0.0009	-	-
5.7364	631	0.0013	-	-
5.7455	632	0.0008	-	-
5.7545	633	0.0056	-	-
5.7636	634	0.0007	-	-
5.7727	635	0.0003	-	-
5.7818	636	0.001	-	-
5.7909	637	0.0009	-	-
5.8	638	0.0003	-	-
5.8091	639	0.0005	-	-
5.8182	640	0.0006	-	-
5.8273	641	0.003	-	-
5.8364	642	0.0003	-	-
5.8455	643	0.0016	-	-
5.8545	644	0.0015	-	-
5.8636	645	0.001	-	-
5.8727	646	0.0006	-	-
5.8818	647	0.0004	-	-
5.8909	648	0.0399	-	-
5.9	649	0.0004	-	-
5.9091	650	0.0003	-	-
5.9182	651	0.0006	-	-
5.9273	652	0.0015	-	-
5.9364	653	0.0012	-	-
5.9455	654	0.0435	-	-
5.9545	655	0.0007	-	-
5.9636	656	0.0011	-	-
5.9727	657	0.0019	-	-
5.9818	658	0.0023	-	-
5.9909	659	0.0003	-	-
6.0	660	0.0004	0.0282	0.7136
6.0091	661	0.0002	-	-
6.0182	662	0.0019	-	-
6.0273	663	0.0004	-	-
6.0364	664	0.0005	-	-
6.0455	665	0.0018	-	-
6.0545	666	0.0009	-	-
6.0636	667	0.0098	-	-
6.0727	668	0.0003	-	-
6.0818	669	0.0014	-	-
6.0909	670	0.001	-	-
6.1	671	0.0004	-	-
6.1091	672	0.0006	-	-
6.1182	673	0.0085	-	-
6.1273	674	0.0015	-	-
6.1364	675	0.0007	-	-
6.1455	676	0.0082	-	-
6.1545	677	0.0047	-	-
6.1636	678	0.0014	-	-
6.1727	679	0.0042	-	-
6.1818	680	0.0006	-	-
6.1909	681	0.0013	-	-
6.2	682	0.0003	-	-
6.2091	683	0.0007	-	-
6.2182	684	0.0007	-	-
6.2273	685	0.0009	-	-
6.2364	686	0.0017	-	-
6.2455	687	0.0007	-	-
6.2545	688	0.0017	-	-
6.2636	689	0.0006	-	-
6.2727	690	0.0005	-	-
6.2818	691	0.0009	-	-
6.2909	692	0.0275	-	-
6.3	693	0.0008	-	-
6.3091	694	0.0011	-	-
6.3182	695	0.0012	-	-
6.3273	696	0.0012	-	-
6.3364	697	0.0002	-	-
6.3455	698	0.0003	-	-
6.3545	699	0.0009	-	-
6.3636	700	0.0136	-	-
6.3727	701	0.0008	-	-
6.3818	702	0.0003	-	-
6.3909	703	0.0046	-	-
6.4	704	0.0011	-	-
6.4091	705	0.0049	-	-
6.4182	706	0.0005	-	-
6.4273	707	0.0004	-	-
6.4364	708	0.0006	-	-
6.4455	709	0.0024	-	-
6.4545	710	0.0108	-	-
6.4636	711	0.0005	-	-
6.4727	712	0.0081	-	-
6.4818	713	0.0004	-	-
6.4909	714	0.0015	-	-
6.5	715	0.0006	-	-
6.5091	716	0.0007	-	-
6.5182	717	0.0019	-	-
6.5273	718	0.0003	-	-
6.5364	719	0.0006	-	-
6.5455	720	0.0003	-	-
6.5545	721	0.0003	-	-
6.5636	722	0.0005	-	-
6.5727	723	0.0013	-	-
6.5818	724	0.001	-	-
6.5909	725	0.0083	-	-
6.6	726	0.0002	-	-
6.6091	727	0.0006	-	-
6.6182	728	0.0073	-	-
6.6273	729	0.0004	-	-
6.6364	730	0.0009	-	-
6.6455	731	0.0004	-	-
6.6545	732	0.0018	-	-
6.6636	733	0.0002	-	-
6.6727	734	0.0006	-	-
6.6818	735	0.0007	-	-
6.6909	736	0.0061	-	-
6.7	737	0.001	-	-
6.7091	738	0.0008	-	-
6.7182	739	0.0005	-	-
6.7273	740	0.0021	-	-
6.7364	741	0.0012	-	-
6.7455	742	0.0005	-	-
6.7545	743	0.0013	-	-
6.7636	744	0.0013	-	-
6.7727	745	0.0003	-	-
6.7818	746	0.0017	-	-
6.7909	747	0.0002	-	-
6.8	748	0.0004	-	-
6.8091	749	0.0027	-	-
6.8182	750	0.0005	-	-
6.8273	751	0.0004	-	-
6.8364	752	0.0005	-	-
6.8455	753	0.0004	-	-
6.8545	754	0.0005	-	-
6.8636	755	0.001	-	-
6.8727	756	0.0007	-	-
6.8818	757	0.0003	-	-
6.8909	758	0.001	-	-
6.9	759	0.0012	-	-
6.9091	760	0.0009	-	-
6.9182	761	0.0025	-	-
6.9273	762	0.0006	-	-
6.9364	763	0.0011	-	-
6.9455	764	0.0006	-	-
6.9545	765	0.0005	-	-
6.9636	766	0.0014	-	-
6.9727	767	0.0016	-	-
6.9818	768	0.0061	-	-
6.9909	769	0.0111	-	-
7.0	770	0.0003	0.0269	0.7149
7.0091	771	0.0004	-	-
7.0182	772	0.0015	-	-
7.0273	773	0.0007	-	-
7.0364	774	0.0014	-	-
7.0455	775	0.076	-	-
7.0545	776	0.0002	-	-
7.0636	777	0.0379	-	-
7.0727	778	0.0002	-	-
7.0818	779	0.0006	-	-
7.0909	780	0.0007	-	-
7.1	781	0.0017	-	-
7.1091	782	0.0003	-	-
7.1182	783	0.0005	-	-
7.1273	784	0.0062	-	-
7.1364	785	0.0012	-	-
7.1455	786	0.0017	-	-
7.1545	787	0.0003	-	-
7.1636	788	0.0008	-	-
7.1727	789	0.0008	-	-
7.1818	790	0.0008	-	-
7.1909	791	0.0004	-	-
7.2	792	0.0003	-	-
7.2091	793	0.0012	-	-
7.2182	794	0.0007	-	-
7.2273	795	0.0004	-	-
7.2364	796	0.0024	-	-
7.2455	797	0.0003	-	-
7.2545	798	0.002	-	-
7.2636	799	0.0068	-	-
7.2727	800	0.0008	-	-
7.2818	801	0.0003	-	-
7.2909	802	0.0031	-	-
7.3	803	0.0089	-	-
7.3091	804	0.0005	-	-
7.3182	805	0.0041	-	-
7.3273	806	0.002	-	-
7.3364	807	0.0005	-	-
7.3455	808	0.0003	-	-
7.3545	809	0.0005	-	-
7.3636	810	0.0006	-	-
7.3727	811	0.0008	-	-
7.3818	812	0.0003	-	-
7.3909	813	0.0014	-	-
7.4	814	0.0007	-	-
7.4091	815	0.0005	-	-
7.4182	816	0.0015	-	-
7.4273	817	0.0007	-	-
7.4364	818	0.0006	-	-
7.4455	819	0.022	-	-
7.4545	820	0.0034	-	-
7.4636	821	0.0016	-	-
7.4727	822	0.0006	-	-
7.4818	823	0.0061	-	-
7.4909	824	0.0025	-	-
7.5	825	0.0005	-	-
7.5091	826	0.0018	-	-
7.5182	827	0.0003	-	-
7.5273	828	0.0007	-	-
7.5364	829	0.0004	-	-
7.5455	830	0.0006	-	-
7.5545	831	0.0003	-	-
7.5636	832	0.001	-	-
7.5727	833	0.0007	-	-
7.5818	834	0.0007	-	-
7.5909	835	0.0002	-	-
7.6	836	0.0633	-	-
7.6091	837	0.0003	-	-
7.6182	838	0.0006	-	-
7.6273	839	0.0007	-	-
7.6364	840	0.0007	-	-
7.6455	841	0.0011	-	-
7.6545	842	0.0005	-	-
7.6636	843	0.0009	-	-
7.6727	844	0.0002	-	-
7.6818	845	0.0037	-	-
7.6909	846	0.0031	-	-
7.7	847	0.0005	-	-
7.7091	848	0.0005	-	-
7.7182	849	0.0011	-	-
7.7273	850	0.0003	-	-
7.7364	851	0.0002	-	-
7.7455	852	0.0006	-	-
7.7545	853	0.0013	-	-
7.7636	854	0.0003	-	-
7.7727	855	0.0013	-	-
7.7818	856	0.0002	-	-
7.7909	857	0.0007	-	-
7.8	858	0.0002	-	-
7.8091	859	0.0186	-	-
7.8182	860	0.0008	-	-
7.8273	861	0.0003	-	-
7.8364	862	0.0012	-	-
7.8455	863	0.0002	-	-
7.8545	864	0.0004	-	-
7.8636	865	0.0015	-	-
7.8727	866	0.0038	-	-
7.8818	867	0.0006	-	-
7.8909	868	0.0008	-	-
7.9	869	0.0007	-	-
7.9091	870	0.0005	-	-
7.9182	871	0.0008	-	-
7.9273	872	0.0008	-	-
7.9364	873	0.0007	-	-
7.9455	874	0.0027	-	-
7.9545	875	0.0011	-	-
7.9636	876	0.0003	-	-
7.9727	877	0.0007	-	-
7.9818	878	0.0008	-	-
7.9909	879	0.0006	-	-
8.0	880	0.0017	0.0299	0.7149
8.0091	881	0.0041	-	-
8.0182	882	0.0012	-	-
8.0273	883	0.0019	-	-
8.0364	884	0.0007	-	-
8.0455	885	0.0376	-	-
8.0545	886	0.0007	-	-
8.0636	887	0.0005	-	-
8.0727	888	0.0022	-	-
8.0818	889	0.0013	-	-
8.0909	890	0.0006	-	-
8.1	891	0.0009	-	-
8.1091	892	0.0007	-	-
8.1182	893	0.0006	-	-
8.1273	894	0.0019	-	-
8.1364	895	0.0019	-	-
8.1455	896	0.0004	-	-
8.1545	897	0.0004	-	-
8.1636	898	0.0004	-	-
8.1727	899	0.0008	-	-
8.1818	900	0.0006	-	-
8.1909	901	0.0007	-	-
8.2	902	0.0005	-	-
8.2091	903	0.0008	-	-
8.2182	904	0.0002	-	-
8.2273	905	0.0003	-	-
8.2364	906	0.0006	-	-
8.2455	907	0.0005	-	-
8.2545	908	0.0002	-	-
8.2636	909	0.0024	-	-
8.2727	910	0.0006	-	-
8.2818	911	0.0004	-	-
8.2909	912	0.0017	-	-
8.3	913	0.0006	-	-
8.3091	914	0.0005	-	-
8.3182	915	0.0007	-	-
8.3273	916	0.0007	-	-
8.3364	917	0.0016	-	-
8.3455	918	0.0003	-	-
8.3545	919	0.0008	-	-
8.3636	920	0.0006	-	-
8.3727	921	0.0016	-	-
8.3818	922	0.0002	-	-
8.3909	923	0.0003	-	-
8.4	924	0.0178	-	-
8.4091	925	0.0005	-	-
8.4182	926	0.0017	-	-
8.4273	927	0.0007	-	-
8.4364	928	0.0004	-	-
8.4455	929	0.0005	-	-
8.4545	930	0.0039	-	-
8.4636	931	0.0002	-	-
8.4727	932	0.0008	-	-
8.4818	933	0.0005	-	-
8.4909	934	0.0013	-	-
8.5	935	0.0006	-	-
8.5091	936	0.0003	-	-
8.5182	937	0.0003	-	-
8.5273	938	0.0006	-	-
8.5364	939	0.0009	-	-
8.5455	940	0.0005	-	-
8.5545	941	0.0003	-	-
8.5636	942	0.0006	-	-
8.5727	943	0.0004	-	-
8.5818	944	0.0002	-	-
8.5909	945	0.0002	-	-
8.6	946	0.0015	-	-
8.6091	947	0.0008	-	-
8.6182	948	0.0004	-	-
8.6273	949	0.0006	-	-
8.6364	950	0.0002	-	-
8.6455	951	0.0031	-	-
8.6545	952	0.0004	-	-
8.6636	953	0.0006	-	-
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8.6818	955	0.004	-	-
8.6909	956	0.0006	-	-
8.7	957	0.0015	-	-
8.7091	958	0.002	-	-
8.7182	959	0.0004	-	-
8.7273	960	0.0001	-	-
8.7364	961	0.0021	-	-
8.7455	962	0.0038	-	-
8.7545	963	0.0012	-	-
8.7636	964	0.001	-	-
8.7727	965	0.0014	-	-
8.7818	966	0.004	-	-
8.7909	967	0.0011	-	-
8.8	968	0.0002	-	-
8.8091	969	0.0005	-	-
8.8182	970	0.0006	-	-
8.8273	971	0.0005	-	-
8.8364	972	0.0012	-	-
8.8455	973	0.0004	-	-
8.8545	974	0.0006	-	-
8.8636	975	0.0006	-	-
8.8727	976	0.0004	-	-
8.8818	977	0.0002	-	-
8.8909	978	0.0006	-	-
8.9	979	0.0007	-	-
8.9091	980	0.0013	-	-
8.9182	981	0.001	-	-
8.9273	982	0.0046	-	-
8.9364	983	0.0124	-	-
8.9455	984	0.0004	-	-
8.9545	985	0.0005	-	-
8.9636	986	0.0102	-	-
8.9727	987	0.0017	-	-
8.9818	988	0.0004	-	-
8.9909	989	0.0014	-	-
9.0	990	0.0208	0.0292	0.7135
9.0091	991	0.0004	-	-
9.0182	992	0.0005	-	-
9.0273	993	0.0004	-	-
9.0364	994	0.0001	-	-
9.0455	995	0.0008	-	-
9.0545	996	0.0066	-	-
9.0636	997	0.0006	-	-
9.0727	998	0.0006	-	-
9.0818	999	0.0002	-	-
9.0909	1000	0.0006	-	-
9.1	1001	0.0002	-	-
9.1091	1002	0.0006	-	-
9.1182	1003	0.0002	-	-
9.1273	1004	0.0002	-	-
9.1364	1005	0.0017	-	-
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9.1545	1007	0.0015	-	-
9.1636	1008	0.0002	-	-
9.1727	1009	0.0005	-	-
9.1818	1010	0.0003	-	-
9.1909	1011	0.0011	-	-
9.2	1012	0.0018	-	-
9.2091	1013	0.0008	-	-
9.2182	1014	0.0004	-	-
9.2273	1015	0.0024	-	-
9.2364	1016	0.0003	-	-
9.2455	1017	0.0005	-	-
9.2545	1018	0.0003	-	-
9.2636	1019	0.0004	-	-
9.2727	1020	0.0003	-	-
9.2818	1021	0.0013	-	-
9.2909	1022	0.0004	-	-
9.3	1023	0.0002	-	-
9.3091	1024	0.0003	-	-
9.3182	1025	0.0007	-	-
9.3273	1026	0.0011	-	-
9.3364	1027	0.0006	-	-
9.3455	1028	0.0002	-	-
9.3545	1029	0.1605	-	-
9.3636	1030	0.0096	-	-
9.3727	1031	0.0003	-	-
9.3818	1032	0.0006	-	-
9.3909	1033	0.0004	-	-
9.4	1034	0.0014	-	-
9.4091	1035	0.0004	-	-
9.4182	1036	0.0006	-	-
9.4273	1037	0.0616	-	-
9.4364	1038	0.0359	-	-
9.4455	1039	0.0003	-	-
9.4545	1040	0.0008	-	-
9.4636	1041	0.0004	-	-
9.4727	1042	0.0004	-	-
9.4818	1043	0.0005	-	-
9.4909	1044	0.0001	-	-
9.5	1045	0.0004	-	-
9.5091	1046	0.0006	-	-
9.5182	1047	0.0008	-	-
9.5273	1048	0.0002	-	-
9.5364	1049	0.0005	-	-
9.5455	1050	0.0004	-	-
9.5545	1051	0.0009	-	-
9.5636	1052	0.0006	-	-
9.5727	1053	0.0004	-	-
9.5818	1054	0.001	-	-
9.5909	1055	0.0007	-	-
9.6	1056	0.0004	-	-
9.6091	1057	0.0002	-	-
9.6182	1058	0.0005	-	-
9.6273	1059	0.0004	-	-
9.6364	1060	0.0572	-	-
9.6455	1061	0.0002	-	-
9.6545	1062	0.0007	-	-
9.6636	1063	0.0004	-	-
9.6727	1064	0.0072	-	-
9.6818	1065	0.0004	-	-
9.6909	1066	0.0007	-	-
9.7	1067	0.0007	-	-
9.7091	1068	0.0008	-	-
9.7182	1069	0.0003	-	-
9.7273	1070	0.0005	-	-
9.7364	1071	0.0019	-	-
9.7455	1072	0.0059	-	-
9.7545	1073	0.0012	-	-
9.7636	1074	0.0011	-	-
9.7727	1075	0.0004	-	-
9.7818	1076	0.0006	-	-
9.7909	1077	0.0009	-	-
9.8	1078	0.0031	-	-
9.8091	1079	0.0005	-	-
9.8182	1080	0.0014	-	-
9.8273	1081	0.0013	-	-
9.8364	1082	0.0015	-	-
9.8455	1083	0.0002	-	-
9.8545	1084	0.0006	-	-
9.8636	1085	0.0003	-	-
9.8727	1086	0.0003	-	-
9.8818	1087	0.0003	-	-
9.8909	1088	0.0018	-	-
9.9	1089	0.0007	-	-
9.9091	1090	0.0006	-	-
9.9182	1091	0.0003	-	-
9.9273	1092	0.0004	-	-
9.9364	1093	0.001	-	-
9.9455	1094	0.0012	-	-
9.9545	1095	0.0002	-	-
9.9636	1096	0.0003	-	-
9.9727	1097	0.0002	-	-
9.9818	1098	0.0011	-	-
9.9909	1099	0.0016	-	-
10.0	1100	0.0005	-	-

Framework Versions

• Python: 3.12.9
• Sentence Transformers: 4.1.0
• Transformers: 4.52.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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}
}