README.md

metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:65749
  - loss:MultipleNegativesRankingLoss
  - loss:ContrastiveLoss
  - loss:CosineSimilarityLoss
  - loss:TripletLoss
base_model: google-bert/bert-base-uncased
widget:
  - source_sentence: Can a US President destroy a city with actions?
    sentences:
      - What are best kids educational games?
      - Can a US president destroy a city through actions?
      - >-
        Why do people ask questions on Quora that are just as, if not more than
        easier to, look up with a search engine?
  - source_sentence: How would you handle stress people?
    sentences:
      - How do I handle stress with a parent?
      - >-
        Why do some people on QUORA ask questions that they can easily findout
        on Google?
      - How do I make a quick right decision?
  - source_sentence: Two women playing field hockey on AstroTurf.
    sentences:
      - Women playing a game of field hockey.
      - The children are outside.
      - Women re-sod a field hockey field.
  - source_sentence: A dog reaches to catch a ball with its mouth.
    sentences:
      - The dog is playing with a rope.
      - The dog is playing with a ball.
      - Someone holding their baby is smiling while sitting down.
  - source_sentence: >-
      There is a very full description of the various types of hormone rooting
      compound here.
    sentences:
      - >-
        The least that can be said is that we must be born with the ability and
        'knowledge' to learn.
      - >-
        It is meant to stimulate root growth - in particular to stimulate the
        creation of roots.
      - A person folds a piece of paper.
datasets:
  - sentence-transformers/all-nli
  - sentence-transformers/stsb
  - sentence-transformers/quora-duplicates
  - sentence-transformers/natural-questions
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on google-bert/bert-base-uncased

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the all-nli-pair, all-nli-pair-class, all-nli-pair-score, all-nli-triplet, stsb, quora and natural-questions datasets. 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: google-bert/bert-base-uncased
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Datasets:
  • all-nli-pair
  • all-nli-pair-class
  • all-nli-pair-score
  • all-nli-triplet
  • stsb
  • quora
  • natural-questions
• Language: en

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': False}) with Transformer model: BertModel 
  (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("kh-li/bert-base-all-nli-stsb-quora-nq")
# Run inference
sentences = [
    'There is a very full description of the various types of hormone rooting compound here.',
    'It is meant to stimulate root growth - in particular to stimulate the creation of roots.',
    "The least that can be said is that we must be born with the ability and 'knowledge' to learn.",
]
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]

Training Details

Training Datasets

all-nli-pair

• Dataset: all-nli-pair at d482672
• Size: 10,000 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 17.03 tokens max: 64 tokens	min: 4 tokens mean: 9.62 tokens max: 31 tokens

• Samples:

  anchor	positive
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.
  Children smiling and waving at camera	There are children present
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

all-nli-pair-class

• Dataset: all-nli-pair-class at d482672
• Size: 10,000 training samples
• Columns: premise, hypothesis, and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	0: ~33.40% 1: ~33.30% 2: ~33.30%

• Samples:

  premise	hypothesis	label
  A person on a horse jumps over a broken down airplane.	A person is training his horse for a competition.	1
  A person on a horse jumps over a broken down airplane.	A person is at a diner, ordering an omelette.	2
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

all-nli-pair-score

• Dataset: all-nli-pair-score at d482672
• Size: 10,000 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	min: 0.0 mean: 0.5 max: 1.0

• Samples:

  sentence1	sentence2	score
  A person on a horse jumps over a broken down airplane.	A person is training his horse for a competition.	0.5
  A person on a horse jumps over a broken down airplane.	A person is at a diner, ordering an omelette.	0.0
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	1.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

all-nli-triplet

• Dataset: all-nli-triplet at d482672
• Size: 10,000 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 10.46 tokens max: 46 tokens	min: 6 tokens mean: 12.81 tokens max: 40 tokens	min: 5 tokens mean: 13.4 tokens max: 50 tokens

• Samples:

  anchor	positive	negative
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	A person is at a diner, ordering an omelette.
  Children smiling and waving at camera	There are children present	The kids are frowning
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.	The boy skates down the sidewalk.

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

stsb

• Dataset: stsb at ab7a5ac
• Size: 5,749 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 10.0 tokens max: 28 tokens	min: 5 tokens mean: 9.95 tokens max: 25 tokens	min: 0.0 mean: 0.54 max: 1.0

• Samples:

  sentence1	sentence2	score
  A plane is taking off.	An air plane is taking off.	1.0
  A man is playing a large flute.	A man is playing a flute.	0.76
  A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	0.76

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

quora

• Dataset: quora at 451a485
• Size: 10,000 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 6 tokens mean: 13.92 tokens max: 42 tokens	min: 6 tokens mean: 14.09 tokens max: 43 tokens

• Samples:

  anchor	positive
  Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?	I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?
  How can I be a good geologist?	What should I do to be a great geologist?
  How do I read and find my YouTube comments?	How can I see all my Youtube comments?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 10,000 training samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 10 tokens mean: 11.74 tokens max: 21 tokens	min: 17 tokens mean: 135.66 tokens max: 512 tokens

• Samples:

  query	answer
  when did richmond last play in a preliminary final	Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.
  who sang what in the world's come over you	Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.
  who produces the most wool in the world	Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Datasets

all-nli-triplet

• Dataset: all-nli-triplet at d482672
• Size: 6,584 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 17.95 tokens max: 63 tokens	min: 4 tokens mean: 9.78 tokens max: 29 tokens	min: 5 tokens mean: 10.35 tokens max: 29 tokens

• Samples:

  anchor	positive	negative
  Two women are embracing while holding to go packages.	Two woman are holding packages.	The men are fighting outside a deli.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.	Two kids in jackets walk to school.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.	A woman drinks her coffee in a small cafe.

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

stsb

• Dataset: stsb at ab7a5ac
• Size: 1,500 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 15.1 tokens max: 45 tokens	min: 6 tokens mean: 15.11 tokens max: 53 tokens	min: 0.0 mean: 0.47 max: 1.0

• Samples:

  sentence1	sentence2	score
  A man with a hard hat is dancing.	A man wearing a hard hat is dancing.	1.0
  A young child is riding a horse.	A child is riding a horse.	0.95
  A man is feeding a mouse to a snake.	The man is feeding a mouse to the snake.	1.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

quora

• Dataset: quora at 451a485
• Size: 1,000 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 6 tokens mean: 14.05 tokens max: 70 tokens	min: 6 tokens mean: 14.11 tokens max: 49 tokens

• Samples:

  anchor	positive
  What is your New Year resolution?	What can be my new year resolution for 2017?
  Should I buy the IPhone 6s or Samsung Galaxy s7?	Which is better: the iPhone 6S Plus or the Samsung Galaxy S7 Edge?
  What are the differences between transgression and regression?	What is the difference between transgression and regression?

• Loss: MultipleNegativesRankingLoss with these parameters:

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

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 1,000 evaluation samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 9 tokens mean: 11.8 tokens max: 21 tokens	min: 19 tokens mean: 138.84 tokens max: 512 tokens

• Samples:

  query	answer
  where does the waikato river begin and end	Waikato River The Waikato River is the longest river in New Zealand, running for 425 kilometres (264 mi) through the North Island. It rises in the eastern slopes of Mount Ruapehu, joining the Tongariro River system and flowing through Lake Taupo, New Zealand's largest lake. It then drains Taupo at the lake's northeastern edge, creates the Huka Falls, and flows northwest through the Waikato Plains. It empties into the Tasman Sea south of Auckland, at Port Waikato. It gives its name to the Waikato Region that surrounds the Waikato Plains. The present course of the river was largely formed about 17,000 years ago. Contributing factors were climate warming, forest being reestablished in the river headwaters and the deepening, rather than widening, of the existing river channel. The channel was gradually eroded as far up river as Piarere, leaving the old Hinuera channel high and dry.[2] The remains of the old river path can be clearly seen at Hinuera where the cliffs mark the ancient river edges. The river's main tributary is the Waipa River, which has its confluence with the Waikato at Ngaruawahia.
  what type of gas is produced during fermentation	Fermentation Fermentation reacts NADH with an endogenous, organic electron acceptor.[1] Usually this is pyruvate formed from sugar through glycolysis. The reaction produces NAD+ and an organic product, typical examples being ethanol, lactic acid, carbon dioxide, and hydrogen gas (H2). However, more exotic compounds can be produced by fermentation, such as butyric acid and acetone. Fermentation products contain chemical energy (they are not fully oxidized), but are considered waste products, since they cannot be metabolized further without the use of oxygen.
  why was star wars episode iv released first	Star Wars (film) Star Wars (later retitled Star Wars: Episode IV – A New Hope) is a 1977 American epic space opera film written and directed by George Lucas. It is the first film in the original Star Wars trilogy and the beginning of the Star Wars franchise. Starring Mark Hamill, Harrison Ford, Carrie Fisher, Peter Cushing, Alec Guinness, David Prowse, James Earl Jones, Anthony Daniels, Kenny Baker, and Peter Mayhew, the film's plot focuses on the Rebel Alliance, led by Princess Leia (Fisher), and its attempt to destroy the Galactic Empire's space station, the Death Star. This conflict disrupts the isolated life of farmhand Luke Skywalker (Hamill), who inadvertently acquires two droids that possess stolen architectural plans for the Death Star. When the Empire begins a destructive search for the missing droids, Skywalker accompanies Jedi Master Obi-Wan Kenobi (Guinness) on a mission to return the plans to the Rebel Alliance and rescue Leia from her imprisonment by the Empire.

• 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: 0.01

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: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	quora loss	all-nli-triplet loss	natural-questions loss	stsb loss
0.0024	10	1.1198	-	-	-	-
0.0049	20	1.8886	-	-	-	-
0.0073	30	0.2303	-	-	-	-
0.0097	40	0.1287	-	-	-	-
0.0122	50	0.4993	-	-	-	-
0.0146	60	0.7388	-	-	-	-
0.0170	70	0.8465	-	-	-	-
0.0195	80	0.8701	-	-	-	-
0.0219	90	0.4349	-	-	-	-
0.0243	100	0.2214	-	-	-	-
0.0268	110	0.1308	-	-	-	-
0.0292	120	0.3163	-	-	-	-
0.0316	130	0.3892	-	-	-	-
0.0341	140	0.2641	-	-	-	-
0.0365	150	0.3359	-	-	-	-
0.0389	160	0.5498	-	-	-	-
0.0414	170	0.2354	-	-	-	-
0.0438	180	0.13	-	-	-	-
0.0462	190	0.2307	-	-	-	-
0.0487	200	0.1271	-	-	-	-
0.0511	210	0.064	-	-	-	-
0.0535	220	0.1842	-	-	-	-
0.0560	230	0.1626	-	-	-	-
0.0584	240	0.1869	-	-	-	-
0.0608	250	0.2147	-	-	-	-
0.0633	260	0.2534	-	-	-	-
0.0657	270	0.1005	-	-	-	-
0.0681	280	0.185	-	-	-	-
0.0706	290	0.1867	-	-	-	-
0.0730	300	0.1905	-	-	-	-
0.0754	310	0.2056	-	-	-	-
0.0779	320	0.2223	-	-	-	-
0.0803	330	0.1499	-	-	-	-
0.0827	340	0.107	-	-	-	-
0.0852	350	0.1481	-	-	-	-
0.0876	360	0.1723	-	-	-	-
0.0900	370	0.2387	-	-	-	-
0.0925	380	0.274	-	-	-	-
0.0949	390	0.1058	-	-	-	-
0.0973	400	0.2053	-	-	-	-
0.0998	410	0.1103	-	-	-	-
0.1022	420	0.1839	-	-	-	-
0.1046	430	0.2341	-	-	-	-
0.1071	440	0.2015	-	-	-	-
0.1095	450	0.1356	-	-	-	-
0.1119	460	0.0793	-	-	-	-
0.1144	470	0.2756	-	-	-	-
0.1168	480	0.0957	-	-	-	-
0.1192	490	0.2549	-	-	-	-
0.1217	500	0.1483	-	-	-	-
0.1241	510	0.2444	-	-	-	-
0.1265	520	0.1665	-	-	-	-
0.1290	530	0.1091	-	-	-	-
0.1314	540	0.1562	-	-	-	-
0.1338	550	0.2385	-	-	-	-
0.1363	560	0.2801	-	-	-	-
0.1387	570	0.2929	-	-	-	-
0.1411	580	0.2027	-	-	-	-
0.1436	590	0.1628	-	-	-	-
0.1460	600	0.1434	-	-	-	-
0.1484	610	0.1009	-	-	-	-
0.1509	620	0.2225	-	-	-	-
0.1533	630	0.1103	-	-	-	-
0.1557	640	0.1945	-	-	-	-
0.1582	650	0.096	-	-	-	-
0.1606	660	0.089	-	-	-	-
0.1630	670	0.1493	-	-	-	-
0.1655	680	0.1297	-	-	-	-
0.1679	690	0.0811	-	-	-	-
0.1703	700	0.1718	-	-	-	-
0.1727	710	0.1139	-	-	-	-
0.1752	720	0.2218	-	-	-	-
0.1776	730	0.1397	-	-	-	-
0.1800	740	0.1163	-	-	-	-
0.1825	750	0.1232	-	-	-	-
0.1849	760	0.1724	-	-	-	-
0.1873	770	0.051	-	-	-	-
0.1898	780	0.1442	-	-	-	-
0.1922	790	0.3022	-	-	-	-
0.1946	800	0.1056	-	-	-	-
0.1971	810	0.1798	-	-	-	-
0.1995	820	0.2234	-	-	-	-
0.2019	830	0.1251	-	-	-	-
0.2044	840	0.2053	-	-	-	-
0.2068	850	0.1332	-	-	-	-
0.2092	860	0.1611	-	-	-	-
0.2117	870	0.0685	-	-	-	-
0.2141	880	0.1434	-	-	-	-
0.2165	890	0.1516	-	-	-	-
0.2190	900	0.1158	-	-	-	-
0.2214	910	0.1235	-	-	-	-
0.2238	920	0.1113	-	-	-	-
0.2263	930	0.2258	-	-	-	-
0.2287	940	0.1003	-	-	-	-
0.2311	950	0.1943	-	-	-	-
0.2336	960	0.1338	-	-	-	-
0.2360	970	0.1892	-	-	-	-
0.2384	980	0.1784	-	-	-	-
0.2409	990	0.1379	-	-	-	-
0.2433	1000	0.1426	-	-	-	-
0.2457	1010	0.1536	-	-	-	-
0.2482	1020	0.118	-	-	-	-
0.2506	1030	0.1463	-	-	-	-
0.2530	1040	0.1821	-	-	-	-
0.2555	1050	0.1829	-	-	-	-
0.2579	1060	0.2086	-	-	-	-
0.2603	1070	0.1066	-	-	-	-
0.2628	1080	0.2072	-	-	-	-
0.2652	1090	0.0754	-	-	-	-
0.2676	1100	0.0863	-	-	-	-
0.2701	1110	0.0821	-	-	-	-
0.2725	1120	0.0978	-	-	-	-
0.2749	1130	0.093	-	-	-	-
0.2774	1140	0.0999	-	-	-	-
0.2798	1150	0.1242	-	-	-	-
0.2822	1160	0.1832	-	-	-	-
0.2847	1170	0.1515	-	-	-	-
0.2871	1180	0.187	-	-	-	-
0.2895	1190	0.1394	-	-	-	-
0.2920	1200	0.1922	-	-	-	-
0.2944	1210	0.1522	-	-	-	-
0.2968	1220	0.2439	-	-	-	-
0.2993	1230	0.0743	-	-	-	-
0.3017	1240	0.101	-	-	-	-
0.3041	1250	0.0736	-	-	-	-
0.3066	1260	0.1892	-	-	-	-
0.3090	1270	0.1031	-	-	-	-
0.3114	1280	0.1348	-	-	-	-
0.3139	1290	0.0839	-	-	-	-
0.3163	1300	0.104	-	-	-	-
0.3187	1310	0.1508	-	-	-	-
0.3212	1320	0.163	-	-	-	-
0.3236	1330	0.1057	-	-	-	-
0.3260	1340	0.0979	-	-	-	-
0.3285	1350	0.1521	-	-	-	-
0.3309	1360	0.0549	-	-	-	-
0.3333	1370	0.1038	-	-	-	-
0.3358	1380	0.1023	-	-	-	-
0.3382	1390	0.0543	-	-	-	-
0.3406	1400	0.1276	-	-	-	-
0.3431	1410	0.0705	-	-	-	-
0.3455	1420	0.1127	-	-	-	-
0.3479	1430	0.0737	-	-	-	-
0.3504	1440	0.066	-	-	-	-
0.3528	1450	0.0864	-	-	-	-
0.3552	1460	0.1299	-	-	-	-
0.3577	1470	0.1171	-	-	-	-
0.3601	1480	0.1578	-	-	-	-
0.3625	1490	0.0774	-	-	-	-
0.3650	1500	0.2007	-	-	-	-
0.3674	1510	0.1538	-	-	-	-
0.3698	1520	0.1343	-	-	-	-
0.3723	1530	0.0861	-	-	-	-
0.3747	1540	0.1305	-	-	-	-
0.3771	1550	0.3199	-	-	-	-
0.3796	1560	0.0887	-	-	-	-
0.3820	1570	0.1275	-	-	-	-
0.3844	1580	0.1526	-	-	-	-
0.3869	1590	0.1412	-	-	-	-
0.3893	1600	0.096	-	-	-	-
0.3917	1610	0.1666	-	-	-	-
0.3942	1620	0.1311	-	-	-	-
0.3966	1630	0.0828	-	-	-	-
0.3990	1640	0.0929	-	-	-	-
0.4015	1650	0.1271	-	-	-	-
0.4039	1660	0.0411	-	-	-	-
0.4063	1670	0.0848	-	-	-	-
0.4088	1680	0.2556	-	-	-	-
0.4112	1690	0.1273	-	-	-	-
0.4136	1700	0.1636	-	-	-	-
0.4161	1710	0.0851	-	-	-	-
0.4185	1720	0.1129	-	-	-	-
0.4209	1730	0.1433	-	-	-	-
0.4234	1740	0.1752	-	-	-	-
0.4258	1750	0.1049	-	-	-	-
0.4282	1760	0.1691	-	-	-	-
0.4307	1770	0.2687	-	-	-	-
0.4331	1780	0.1624	-	-	-	-
0.4355	1790	0.1654	-	-	-	-
0.4380	1800	0.1209	-	-	-	-
0.4404	1810	0.2127	-	-	-	-
0.4428	1820	0.0449	-	-	-	-
0.4453	1830	0.0906	-	-	-	-
0.4477	1840	0.1546	-	-	-	-
0.4501	1850	0.0938	-	-	-	-
0.4526	1860	0.1115	-	-	-	-
0.4550	1870	0.0864	-	-	-	-
0.4574	1880	0.1515	-	-	-	-
0.4599	1890	0.091	-	-	-	-
0.4623	1900	0.1496	-	-	-	-
0.4647	1910	0.1807	-	-	-	-
0.4672	1920	0.1351	-	-	-	-
0.4696	1930	0.114	-	-	-	-
0.4720	1940	0.1673	-	-	-	-
0.4745	1950	0.1655	-	-	-	-
0.4769	1960	0.0662	-	-	-	-
0.4793	1970	0.1377	-	-	-	-
0.4818	1980	0.0512	-	-	-	-
0.4842	1990	0.1399	-	-	-	-
0.4866	2000	0.1613	-	-	-	-
0.4891	2010	0.1326	-	-	-	-
0.4915	2020	0.1201	-	-	-	-
0.4939	2030	0.097	-	-	-	-
0.4964	2040	0.0788	-	-	-	-
0.4988	2050	0.1282	-	-	-	-
0.5012	2060	0.2038	-	-	-	-
0.5036	2070	0.1078	-	-	-	-
0.5061	2080	0.1594	-	-	-	-
0.5085	2090	0.1628	-	-	-	-
0.5109	2100	0.0744	-	-	-	-
0.5134	2110	0.1587	-	-	-	-
0.5158	2120	0.0573	-	-	-	-
0.5182	2130	0.1672	-	-	-	-
0.5207	2140	0.1139	-	-	-	-
0.5231	2150	0.1285	-	-	-	-
0.5255	2160	0.1538	-	-	-	-
0.5280	2170	0.1642	-	-	-	-
0.5304	2180	0.1012	-	-	-	-
0.5328	2190	0.0554	-	-	-	-
0.5353	2200	0.0656	-	-	-	-
0.5377	2210	0.1206	-	-	-	-
0.5401	2220	0.1164	-	-	-	-
0.5426	2230	0.1364	-	-	-	-
0.5450	2240	0.1188	-	-	-	-
0.5474	2250	0.0965	-	-	-	-
0.5499	2260	0.0789	-	-	-	-
0.5523	2270	0.0793	-	-	-	-
0.5547	2280	0.1205	-	-	-	-
0.5572	2290	0.089	-	-	-	-
0.5596	2300	0.1049	-	-	-	-
0.5620	2310	0.0989	-	-	-	-
0.5645	2320	0.1822	-	-	-	-
0.5669	2330	0.1367	-	-	-	-
0.5693	2340	0.1238	-	-	-	-
0.5718	2350	0.1383	-	-	-	-
0.5742	2360	0.184	-	-	-	-
0.5766	2370	0.1254	-	-	-	-
0.5791	2380	0.1046	-	-	-	-
0.5815	2390	0.1175	-	-	-	-
0.5839	2400	0.0698	-	-	-	-
0.5864	2410	0.111	-	-	-	-
0.5888	2420	0.115	-	-	-	-
0.5912	2430	0.1721	-	-	-	-
0.5937	2440	0.0904	-	-	-	-
0.5961	2450	0.1142	-	-	-	-
0.5985	2460	0.1021	-	-	-	-
0.6010	2470	0.0307	-	-	-	-
0.6034	2480	0.1495	-	-	-	-
0.6058	2490	0.1031	-	-	-	-
0.6083	2500	0.0951	-	-	-	-
0.6107	2510	0.0941	-	-	-	-
0.6131	2520	0.2231	-	-	-	-
0.6156	2530	0.1572	-	-	-	-
0.6180	2540	0.2004	-	-	-	-
0.6204	2550	0.0573	-	-	-	-
0.6229	2560	0.156	-	-	-	-
0.6253	2570	0.1244	-	-	-	-
0.6277	2580	0.0996	-	-	-	-
0.6302	2590	0.163	-	-	-	-
0.6326	2600	0.169	-	-	-	-
0.6350	2610	0.1593	-	-	-	-
0.6375	2620	0.098	-	-	-	-
0.6399	2630	0.1133	-	-	-	-
0.6423	2640	0.1267	-	-	-	-
0.6448	2650	0.1006	-	-	-	-
0.6472	2660	0.178	-	-	-	-
0.6496	2670	0.1124	-	-	-	-
0.6521	2680	0.0952	-	-	-	-
0.6545	2690	0.0726	-	-	-	-
0.6569	2700	0.1105	-	-	-	-
0.6594	2710	0.1675	-	-	-	-
0.6618	2720	0.1711	-	-	-	-
0.6642	2730	0.1481	-	-	-	-
0.6667	2740	0.1078	-	-	-	-
0.6691	2750	0.0981	-	-	-	-
0.6715	2760	0.115	-	-	-	-
0.6740	2770	0.0855	-	-	-	-
0.6764	2780	0.0657	-	-	-	-
0.6788	2790	0.0539	-	-	-	-
0.6813	2800	0.0766	-	-	-	-
0.6837	2810	0.1608	-	-	-	-
0.6861	2820	0.1263	-	-	-	-
0.6886	2830	0.0992	-	-	-	-
0.6910	2840	0.1147	-	-	-	-
0.6934	2850	0.1697	-	-	-	-
0.6959	2860	0.1602	-	-	-	-
0.6983	2870	0.083	-	-	-	-
0.7007	2880	0.1068	-	-	-	-
0.7032	2890	0.1074	-	-	-	-
0.7056	2900	0.0695	-	-	-	-
0.7080	2910	0.0529	-	-	-	-
0.7105	2920	0.1381	-	-	-	-
0.7129	2930	0.1418	-	-	-	-
0.7153	2940	0.1506	-	-	-	-
0.7178	2950	0.1069	-	-	-	-
0.7202	2960	0.147	-	-	-	-
0.7226	2970	0.1358	-	-	-	-
0.7251	2980	0.1592	-	-	-	-
0.7275	2990	0.1387	-	-	-	-
0.7299	3000	0.0886	-	-	-	-
0.7324	3010	0.149	-	-	-	-
0.7348	3020	0.1347	-	-	-	-
0.7372	3030	0.1022	-	-	-	-
0.7397	3040	0.0747	-	-	-	-
0.7421	3050	0.0839	-	-	-	-
0.7445	3060	0.1364	-	-	-	-
0.7470	3070	0.1191	-	-	-	-
0.7494	3080	0.0779	-	-	-	-
0.7518	3090	0.0654	-	-	-	-
0.7543	3100	0.0714	-	-	-	-
0.7567	3110	0.1154	-	-	-	-
0.7591	3120	0.0546	-	-	-	-
0.7616	3130	0.0548	-	-	-	-
0.7640	3140	0.0569	-	-	-	-
0.7664	3150	0.0964	-	-	-	-
0.7689	3160	0.0445	-	-	-	-
0.7713	3170	0.1362	-	-	-	-
0.7737	3180	0.1239	-	-	-	-
0.7762	3190	0.0981	-	-	-	-
0.7786	3200	0.0422	-	-	-	-
0.7810	3210	0.1282	-	-	-	-
0.7835	3220	0.0847	-	-	-	-
0.7859	3230	0.1134	-	-	-	-
0.7883	3240	0.1048	-	-	-	-
0.7908	3250	0.1091	-	-	-	-
0.7932	3260	0.0428	-	-	-	-
0.7956	3270	0.0632	-	-	-	-
0.7981	3280	0.0808	-	-	-	-
0.8005	3290	0.0604	-	-	-	-
0.8029	3300	0.1614	-	-	-	-
0.8054	3310	0.1604	-	-	-	-
0.8078	3320	0.0899	-	-	-	-
0.8102	3330	0.1097	-	-	-	-
0.8127	3340	0.1269	-	-	-	-
0.8151	3350	0.0738	-	-	-	-
0.8175	3360	0.0768	-	-	-	-
0.8200	3370	0.0752	-	-	-	-
0.8224	3380	0.1379	-	-	-	-
0.8248	3390	0.0877	-	-	-	-
0.8273	3400	0.1311	-	-	-	-
0.8297	3410	0.1109	-	-	-	-
0.8321	3420	0.1557	-	-	-	-
0.8345	3430	0.1509	-	-	-	-
0.8370	3440	0.0962	-	-	-	-
0.8394	3450	0.0631	-	-	-	-
0.8418	3460	0.0835	-	-	-	-
0.8443	3470	0.1488	-	-	-	-
0.8467	3480	0.0903	-	-	-	-
0.8491	3490	0.0927	-	-	-	-
0.8516	3500	0.1457	-	-	-	-
0.8540	3510	0.0775	-	-	-	-
0.8564	3520	0.1314	-	-	-	-
0.8589	3530	0.1528	-	-	-	-
0.8613	3540	0.0695	-	-	-	-
0.8637	3550	0.0673	-	-	-	-
0.8662	3560	0.1441	-	-	-	-
0.8686	3570	0.135	-	-	-	-
0.8710	3580	0.1595	-	-	-	-
0.8735	3590	0.1125	-	-	-	-
0.8759	3600	0.0709	-	-	-	-
0.8783	3610	0.1191	-	-	-	-
0.8808	3620	0.1614	-	-	-	-
0.8832	3630	0.086	-	-	-	-
0.8856	3640	0.0818	-	-	-	-
0.8881	3650	0.0544	-	-	-	-
0.8905	3660	0.0797	-	-	-	-
0.8929	3670	0.0691	-	-	-	-
0.8954	3680	0.0924	-	-	-	-
0.8978	3690	0.0572	-	-	-	-
0.9002	3700	0.0532	-	-	-	-
0.9027	3710	0.1519	-	-	-	-
0.9051	3720	0.0983	-	-	-	-
0.9075	3730	0.0772	-	-	-	-
0.9100	3740	0.18	-	-	-	-
0.9124	3750	0.0485	-	-	-	-
0.9148	3760	0.0872	-	-	-	-
0.9173	3770	0.1069	-	-	-	-
0.9197	3780	0.0657	-	-	-	-
0.9221	3790	0.1811	-	-	-	-
0.9246	3800	0.1038	-	-	-	-
0.9270	3810	0.087	-	-	-	-
0.9294	3820	0.1569	-	-	-	-
0.9319	3830	0.0404	-	-	-	-
0.9343	3840	0.1468	-	-	-	-
0.9367	3850	0.0974	-	-	-	-
0.9392	3860	0.1231	-	-	-	-
0.9416	3870	0.1511	-	-	-	-
0.9440	3880	0.0386	-	-	-	-
0.9465	3890	0.0918	-	-	-	-
0.9489	3900	0.0661	-	-	-	-
0.9513	3910	0.1355	-	-	-	-
0.9538	3920	0.1182	-	-	-	-
0.9562	3930	0.1254	-	-	-	-
0.9586	3940	0.1999	-	-	-	-
0.9611	3950	0.125	-	-	-	-
0.9635	3960	0.0303	-	-	-	-
0.9659	3970	0.1192	-	-	-	-
0.9684	3980	0.1182	-	-	-	-
0.9708	3990	0.1449	-	-	-	-
0.9732	4000	0.1387	-	-	-	-
0.9757	4010	0.077	-	-	-	-
0.9781	4020	0.1118	-	-	-	-
0.9805	4030	0.0567	-	-	-	-
0.9830	4040	0.0454	-	-	-	-
0.9854	4050	0.1179	-	-	-	-
0.9878	4060	0.0993	-	-	-	-
0.9903	4070	0.1377	-	-	-	-
0.9927	4080	0.1308	-	-	-	-
0.9951	4090	0.0982	-	-	-	-
0.9976	4100	0.1211	-	-	-	-
1.0	4110	0.2036	0.0136	2.3842	0.0331	0.0606
1.0024	4120	0.1825	-	-	-	-
1.0049	4130	0.1088	-	-	-	-
1.0073	4140	0.1301	-	-	-	-
1.0097	4150	0.0549	-	-	-	-
1.0122	4160	0.0714	-	-	-	-
1.0146	4170	0.0743	-	-	-	-
1.0170	4180	0.0531	-	-	-	-
1.0195	4190	0.0749	-	-	-	-
1.0219	4200	0.0868	-	-	-	-
1.0243	4210	0.0544	-	-	-	-
1.0268	4220	0.0894	-	-	-	-
1.0292	4230	0.0971	-	-	-	-
1.0316	4240	0.0709	-	-	-	-
1.0341	4250	0.055	-	-	-	-
1.0365	4260	0.0386	-	-	-	-
1.0389	4270	0.1549	-	-	-	-
1.0414	4280	0.102	-	-	-	-
1.0438	4290	0.0422	-	-	-	-
1.0462	4300	0.0886	-	-	-	-
1.0487	4310	0.0583	-	-	-	-
1.0511	4320	0.0522	-	-	-	-
1.0535	4330	0.0478	-	-	-	-
1.0560	4340	0.0328	-	-	-	-
1.0584	4350	0.028	-	-	-	-
1.0608	4360	0.0129	-	-	-	-
1.0633	4370	0.084	-	-	-	-
1.0657	4380	0.0523	-	-	-	-
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2.8710	11800	0.0615	-	-	-	-
2.8735	11810	0.0581	-	-	-	-
2.8759	11820	0.0352	-	-	-	-
2.8783	11830	0.0814	-	-	-	-
2.8808	11840	0.0641	-	-	-	-
2.8832	11850	0.0364	-	-	-	-
2.8856	11860	0.0388	-	-	-	-
2.8881	11870	0.0479	-	-	-	-
2.8905	11880	0.0349	-	-	-	-
2.8929	11890	0.0557	-	-	-	-
2.8954	11900	0.0437	-	-	-	-
2.8978	11910	0.0157	-	-	-	-
2.9002	11920	0.0304	-	-	-	-
2.9027	11930	0.0377	-	-	-	-
2.9051	11940	0.0626	-	-	-	-
2.9075	11950	0.0672	-	-	-	-
2.9100	11960	0.0835	-	-	-	-
2.9124	11970	0.0377	-	-	-	-
2.9148	11980	0.0623	-	-	-	-
2.9173	11990	0.0375	-	-	-	-
2.9197	12000	0.0182	-	-	-	-
2.9221	12010	0.0464	-	-	-	-
2.9246	12020	0.074	-	-	-	-
2.9270	12030	0.0604	-	-	-	-
2.9294	12040	0.0447	-	-	-	-
2.9319	12050	0.0231	-	-	-	-
2.9343	12060	0.0759	-	-	-	-
2.9367	12070	0.0592	-	-	-	-
2.9392	12080	0.0412	-	-	-	-
2.9416	12090	0.0554	-	-	-	-
2.9440	12100	0.0086	-	-	-	-
2.9465	12110	0.0605	-	-	-	-
2.9489	12120	0.0522	-	-	-	-
2.9513	12130	0.0822	-	-	-	-
2.9538	12140	0.0603	-	-	-	-
2.9562	12150	0.0762	-	-	-	-
2.9586	12160	0.076	-	-	-	-
2.9611	12170	0.0516	-	-	-	-
2.9635	12180	0.0221	-	-	-	-
2.9659	12190	0.0662	-	-	-	-
2.9684	12200	0.0571	-	-	-	-
2.9708	12210	0.0738	-	-	-	-
2.9732	12220	0.0567	-	-	-	-
2.9757	12230	0.0566	-	-	-	-
2.9781	12240	0.077	-	-	-	-
2.9805	12250	0.0353	-	-	-	-
2.9830	12260	0.0313	-	-	-	-
2.9854	12270	0.0628	-	-	-	-
2.9878	12280	0.0536	-	-	-	-
2.9903	12290	0.0972	-	-	-	-
2.9927	12300	0.0393	-	-	-	-
2.9951	12310	0.0461	-	-	-	-
2.9976	12320	0.0585	-	-	-	-
3.0	12330	0.0923	0.0108	2.1017	0.0314	0.0328

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.1
• Transformers: 4.45.2
• PyTorch: 2.5.1+cu124
• Accelerate: 1.1.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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}
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}