metadata
base_model: microsoft/mdeberta-v3-base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:498970
- loss:BPRLoss
widget:
- source_sentence: meaning of the prefix em
sentences:
- >-
Word Origin and History for em- Expand. from French assimilation of en-
to following labial (see en- (1)). Also a prefix used to form verbs from
adjectives and nouns. representing Latin ex- assimilated to following
-m- (see ex-).
- >-
Rating Newest Oldest. 1 MO probably has the most insanely complex sales
tax in the country. Not only is there a state level tax (4.225% for most
items and 1.225% for grocery foods) but city and county level sales
taxes. 2 The sales tax is set by county. Go to Missouri Sales Tax
website and look up your county.
- >-
Prefixes: Un, Dis, Im, Mis. A prefix is placed at the beginning of a
word to change its meaning. For example, the suffix re- means either
again or back as in return, repeat or refurbish. The following 4
prefixes are easy to confuse because they all have a negative meaning.
un-.
- source_sentence: is woolwich london safe
sentences:
- >-
SE18 has four train stations Plumstead, Woolwich Arsenal and Woolwich
Dockyard. Plumstead and Woolwich Arsenal are situated in Zone 4,
Woolwich Dockyard in Zone 3.Approximately just under 30 minutes to
Charing Cross from all Stations. Trains are operated buy South-eastern.
Train timetables are available at southeasternrailway.co.uk.here is no
shortage of schools, libraries and colleges in SE18. A short walk from
Plumstead station is Greenwich Community College offering a wide range
of courses from cookery to languages. Notable schools include the newly
re-built Foxfield Primary, Saint Pauls and Plumstead Mannor.
- "In its heyday Woolwich was known better known as the home of Arsenal Football Club, the first McDonalds in the UK and the base for the British Armyâ\x80\x99s artillery. At present, it is safe to say the town would not be found in any London travel guide."
- >-
Income and Qualifications. Car sales consultants often have compensation
packages that include salary, commissions and bonuses. For example, Ford
Motor sales reps earned an average base salary of $37,000, according to
Glassdoor -- with the rest of their $54,600 in earnings comprised of
commissions and benefits.
- source_sentence: who is christopher kyle
sentences:
- >-
Kyle Kulinski is an American Political Activist, progressive talk radio
host, social democratic political commentator, and the co-founder of
Justice Democrats. He is the host and producer of the YouTube show
Secular Talk, an affiliate of The Young Turks network.
- >-
A passport card is valid for travel to and from Canada, Mexico, the
Caribbean and Bermuda at land border crossings and sea ports-of-entry.
It is not valid for air travel. It is valid for 10 years for adults and
5 years for minors under 16. A first passport book costs $135 for adults
and $105 for minors under the age of 16. It costs $110 to renew. A first
passport card costs $55 for adults and $40 for minors under the age of
16. It costs $30 to renew. The cost when applying for both is $165 for
adults and $120 for minors.
- >-
Chris Kyle American Sniper. Christopher Scott Kyle was born and raised
in Texas and was a United States Navy SEAL from 1999 to 2009. He is
currently known as the most successful sniper in American military
history. According to his book American Sniper, he had 160 confirmed
kills (which was from 255 claimed kills).
- source_sentence: do potato chips have sugar
sentences:
- >-
Glycemic Index. White potatoes, whether you have them mashed, baked, as
french fries or potato chips, have a high glycemic index, which means
that their carbohydrates are quickly turned into sugar, which elevates
your blood sugar levels after your meal.ating sweet potatoes in moderate
amounts will help you keep your blood sugar levels in the healthy range
even if you have diabetes. A medium sweet potato contains 26 grams of
carbohydrates, of which 3.8 grams are dietary fiber, while a cup of
mashed sweet potatoes has 58 grams of carbohydrates and 8.2 grams of
fiber.
- >-
So before tying that knot in the morning, consider what personality
traits you are conveying through the color of your tie. Reds are a power
color, symbolizing wealth, strength, and passion. Many cultures also
find special meaning in the color red, such as good luck.
- "Corn chips have a glycemic index score of 42, which is in the low range and indicates they wonâ\x80\x99t spike your blood sugar. Of the total carbohydrates, 1.5 grams are dietary fiber, 16 grams are complex carbs in the form of starches and only 0.3 grams are sugar."
- source_sentence: definition of stoop
sentences:
- >-
Definition of stoop written for English Language Learners from the
Merriam-Webster Learner's Dictionary with audio pronunciations, usage
examples, and count/noncount noun labels. Learner's Dictionary mobile
search
- "Define stoop: to bend the body or a part of the body forward and downward sometimes simultaneously bending the knees â\x80\x94 stoop in a sentence to bend the body or a part of the body forward and downward sometimes simultaneously bending the kneesâ\x80¦ See the full definition"
- >-
Blood plasma is the yellow liquid in which blood cells float. Plasma is
made up of nutrients, electrolytes (salts), gases, non-protein hormones,
waste, lipids, and proteins.These proteins are albumin, antibodies (also
called immunoglobulins), clotting factors, and protein hormones.lood
plasma is the yellow liquid in which blood cells float. Plasma is made
up of nutrients, electrolytes (salts), gases, non-protein hormones,
waste, lipids, and proteins.
SentenceTransformer based on microsoft/mdeberta-v3-base
This is a sentence-transformers model finetuned from microsoft/mdeberta-v3-base. 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: microsoft/mdeberta-v3-base
- Maximum Sequence Length: 1024 tokens
- Output Dimensionality: 768 tokens
- 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': 1024, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 768, '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})
)
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("BlackBeenie/mdeberta-v3-base-msmarco-v3-bpr")
# Run inference
sentences = [
'definition of stoop',
'Define stoop: to bend the body or a part of the body forward and downward sometimes simultaneously bending the knees â\x80\x94 stoop in a sentence to bend the body or a part of the body forward and downward sometimes simultaneously bending the kneesâ\x80¦ See the full definition',
"Definition of stoop written for English Language Learners from the Merriam-Webster Learner's Dictionary with audio pronunciations, usage examples, and count/noncount noun labels. Learner's Dictionary mobile search",
]
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 Dataset
Unnamed Dataset
- Size: 498,970 training samples
- Columns:
sentence_0
,sentence_1
, andsentence_2
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2 type string string string details - min: 4 tokens
- mean: 10.61 tokens
- max: 40 tokens
- min: 17 tokens
- mean: 96.41 tokens
- max: 259 tokens
- min: 14 tokens
- mean: 92.21 tokens
- max: 250 tokens
- Samples:
sentence_0 sentence_1 sentence_2 how much does it cost to paint a interior house
Interior House Painting Cost Factors. Generally, it will take a minimum of two gallons of paint to cover a room. At the highest end, paint will cost anywhere between $30 and $60 per gallon and come in three different finishes: flat, semi-gloss or high-gloss.Flat finishes are the least shiny and are best suited for areas requiring frequent cleaning.rovide a few details about your project and receive competitive quotes from local pros. The average national cost to paint a home interior is $1,671, with most homeowners spending between $966 and $2,426.
How Much to Charge to Paint the Interior of a House (and how much not to charge) Let me give you an example - stay with me here. Imagine you drop all of your painting estimates by 20% to win more jobs. Maybe you'll close $10,000 in sales instead of $6,000 (because you had a better price - you landed an extra job)...
when is s corp taxes due
If you form a corporate entity for your small business, regardless of whether it's taxed as a C or S corporation, a tax return must be filed with the Internal Revenue Service on its due date each year. Corporate tax returns are always due on the 15th day of the third month following the close of the tax year. The actual day that the tax return filing deadline falls on, however, isn't the same for every corporation.
In Summary. 1 S-corporations are pass-through entities. 2 Form 1120S is the form used for an S-corpâs annual tax return. 3 Shareholders do not have to pay self-employment tax on their share of an S-corpâs profits.
what are disaccharides
Disaccharides are formed when two monosaccharides are joined together and a molecule of water is removed, a process known as dehydration reaction. For example; milk sugar (lactose) is made from glucose and galactose whereas the sugar from sugar cane and sugar beets (sucrose) is made from glucose and fructose.altose, another notable disaccharide, is made up of two glucose molecules. The two monosaccharides are bonded via a dehydration reaction (also called a condensation reaction or dehydration synthesis) that leads to the loss of a molecule of water and formation of a glycosidic bond.
No. Sugars and starches are types of carbohydrates,(ex: monosaccharides, disaccharides) Lipids are much different.o. Sugars and starches are types of carbohydrates,(ex: monosaccharides, disaccharides) Lipids are much different.
- Loss:
beir.losses.bpr_loss.BPRLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32num_train_epochs
: 15fp16
: Truemulti_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 15max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Click to expand
Epoch | Step | Training Loss |
---|---|---|
0.0321 | 500 | 7.0196 |
0.0641 | 1000 | 2.0193 |
0.0962 | 1500 | 1.4466 |
0.1283 | 2000 | 1.1986 |
0.1603 | 2500 | 1.0912 |
0.1924 | 3000 | 1.0179 |
0.2245 | 3500 | 0.9659 |
0.2565 | 4000 | 0.9229 |
0.2886 | 4500 | 0.9034 |
0.3207 | 5000 | 0.871 |
0.3527 | 5500 | 0.8474 |
0.3848 | 6000 | 0.8247 |
0.4169 | 6500 | 0.8377 |
0.4489 | 7000 | 0.8119 |
0.4810 | 7500 | 0.8042 |
0.5131 | 8000 | 0.7831 |
0.5451 | 8500 | 0.7667 |
0.5772 | 9000 | 0.7653 |
0.6092 | 9500 | 0.7502 |
0.6413 | 10000 | 0.7615 |
0.6734 | 10500 | 0.7435 |
0.7054 | 11000 | 0.7346 |
0.7375 | 11500 | 0.718 |
0.7696 | 12000 | 0.711 |
0.8016 | 12500 | 0.6963 |
0.8337 | 13000 | 0.6969 |
0.8658 | 13500 | 0.6937 |
0.8978 | 14000 | 0.6721 |
0.9299 | 14500 | 0.6902 |
0.9620 | 15000 | 0.6783 |
0.9940 | 15500 | 0.6669 |
1.0 | 15593 | - |
1.0261 | 16000 | 0.689 |
1.0582 | 16500 | 0.6549 |
1.0902 | 17000 | 0.6354 |
1.1223 | 17500 | 0.6013 |
1.1544 | 18000 | 0.6091 |
1.1864 | 18500 | 0.5907 |
1.2185 | 19000 | 0.5979 |
1.2506 | 19500 | 0.5724 |
1.2826 | 20000 | 0.5718 |
1.3147 | 20500 | 0.5851 |
1.3468 | 21000 | 0.5716 |
1.3788 | 21500 | 0.5568 |
1.4109 | 22000 | 0.5502 |
1.4430 | 22500 | 0.5591 |
1.4750 | 23000 | 0.5688 |
1.5071 | 23500 | 0.5484 |
1.5392 | 24000 | 0.531 |
1.5712 | 24500 | 0.5445 |
1.6033 | 25000 | 0.5269 |
1.6353 | 25500 | 0.55 |
1.6674 | 26000 | 0.537 |
1.6995 | 26500 | 0.5259 |
1.7315 | 27000 | 0.5153 |
1.7636 | 27500 | 0.5184 |
1.7957 | 28000 | 0.5154 |
1.8277 | 28500 | 0.5279 |
1.8598 | 29000 | 0.5267 |
1.8919 | 29500 | 0.4938 |
1.9239 | 30000 | 0.5088 |
1.9560 | 30500 | 0.516 |
1.9881 | 31000 | 0.4998 |
2.0 | 31186 | - |
2.0201 | 31500 | 0.5252 |
2.0522 | 32000 | 0.4998 |
2.0843 | 32500 | 0.484 |
2.1163 | 33000 | 0.4612 |
2.1484 | 33500 | 0.4617 |
2.1805 | 34000 | 0.4441 |
2.2125 | 34500 | 0.4653 |
2.2446 | 35000 | 0.4592 |
2.2767 | 35500 | 0.4347 |
2.3087 | 36000 | 0.4557 |
2.3408 | 36500 | 0.4401 |
2.3729 | 37000 | 0.436 |
2.4049 | 37500 | 0.4315 |
2.4370 | 38000 | 0.4447 |
2.4691 | 38500 | 0.4258 |
2.5011 | 39000 | 0.4275 |
2.5332 | 39500 | 0.4142 |
2.5653 | 40000 | 0.434 |
2.5973 | 40500 | 0.4222 |
2.6294 | 41000 | 0.4284 |
2.6615 | 41500 | 0.4187 |
2.6935 | 42000 | 0.4156 |
2.7256 | 42500 | 0.4054 |
2.7576 | 43000 | 0.4182 |
2.7897 | 43500 | 0.4142 |
2.8218 | 44000 | 0.4152 |
2.8538 | 44500 | 0.421 |
2.8859 | 45000 | 0.403 |
2.9180 | 45500 | 0.4003 |
2.9500 | 46000 | 0.4032 |
2.9821 | 46500 | 0.4072 |
3.0 | 46779 | - |
3.0142 | 47000 | 0.4137 |
3.0462 | 47500 | 0.4151 |
3.0783 | 48000 | 0.3959 |
3.1104 | 48500 | 0.3808 |
3.1424 | 49000 | 0.3701 |
3.1745 | 49500 | 0.3716 |
3.2066 | 50000 | 0.387 |
3.2386 | 50500 | 0.3747 |
3.2707 | 51000 | 0.3488 |
3.3028 | 51500 | 0.3795 |
3.3348 | 52000 | 0.3511 |
3.3669 | 52500 | 0.3469 |
3.3990 | 53000 | 0.3475 |
3.4310 | 53500 | 0.3669 |
3.4631 | 54000 | 0.3428 |
3.4952 | 54500 | 0.3597 |
3.5272 | 55000 | 0.3525 |
3.5593 | 55500 | 0.3502 |
3.5914 | 56000 | 0.3446 |
3.6234 | 56500 | 0.3563 |
3.6555 | 57000 | 0.34 |
3.6876 | 57500 | 0.3385 |
3.7196 | 58000 | 0.335 |
3.7517 | 58500 | 0.3344 |
3.7837 | 59000 | 0.3361 |
3.8158 | 59500 | 0.3285 |
3.8479 | 60000 | 0.3429 |
3.8799 | 60500 | 0.3162 |
3.9120 | 61000 | 0.3279 |
3.9441 | 61500 | 0.3448 |
3.9761 | 62000 | 0.322 |
4.0 | 62372 | - |
4.0082 | 62500 | 0.3356 |
4.0403 | 63000 | 0.3416 |
4.0723 | 63500 | 0.3195 |
4.1044 | 64000 | 0.3033 |
4.1365 | 64500 | 0.2957 |
4.1685 | 65000 | 0.312 |
4.2006 | 65500 | 0.3135 |
4.2327 | 66000 | 0.3193 |
4.2647 | 66500 | 0.2919 |
4.2968 | 67000 | 0.3078 |
4.3289 | 67500 | 0.302 |
4.3609 | 68000 | 0.2973 |
4.3930 | 68500 | 0.2725 |
4.4251 | 69000 | 0.3013 |
4.4571 | 69500 | 0.2936 |
4.4892 | 70000 | 0.3009 |
4.5213 | 70500 | 0.2941 |
4.5533 | 71000 | 0.2957 |
4.5854 | 71500 | 0.288 |
4.6175 | 72000 | 0.3032 |
4.6495 | 72500 | 0.2919 |
4.6816 | 73000 | 0.2843 |
4.7137 | 73500 | 0.2862 |
4.7457 | 74000 | 0.2789 |
4.7778 | 74500 | 0.2843 |
4.8099 | 75000 | 0.2816 |
4.8419 | 75500 | 0.2813 |
4.8740 | 76000 | 0.2839 |
4.9060 | 76500 | 0.2619 |
4.9381 | 77000 | 0.2877 |
4.9702 | 77500 | 0.2693 |
5.0 | 77965 | - |
5.0022 | 78000 | 0.2738 |
5.0343 | 78500 | 0.286 |
5.0664 | 79000 | 0.2754 |
5.0984 | 79500 | 0.2561 |
5.1305 | 80000 | 0.2498 |
5.1626 | 80500 | 0.2563 |
5.1946 | 81000 | 0.2618 |
5.2267 | 81500 | 0.265 |
5.2588 | 82000 | 0.245 |
5.2908 | 82500 | 0.2551 |
5.3229 | 83000 | 0.2653 |
5.3550 | 83500 | 0.2453 |
5.3870 | 84000 | 0.24 |
5.4191 | 84500 | 0.2478 |
5.4512 | 85000 | 0.2444 |
5.4832 | 85500 | 0.2464 |
5.5153 | 86000 | 0.2327 |
5.5474 | 86500 | 0.2376 |
5.5794 | 87000 | 0.2469 |
5.6115 | 87500 | 0.2488 |
5.6436 | 88000 | 0.2467 |
5.6756 | 88500 | 0.2409 |
5.7077 | 89000 | 0.2287 |
5.7398 | 89500 | 0.2288 |
5.7718 | 90000 | 0.2399 |
5.8039 | 90500 | 0.2341 |
5.8360 | 91000 | 0.2352 |
5.8680 | 91500 | 0.2196 |
5.9001 | 92000 | 0.2196 |
5.9321 | 92500 | 0.2246 |
5.9642 | 93000 | 0.2411 |
5.9963 | 93500 | 0.2279 |
6.0 | 93558 | - |
6.0283 | 94000 | 0.2489 |
6.0604 | 94500 | 0.2339 |
6.0925 | 95000 | 0.224 |
6.1245 | 95500 | 0.209 |
6.1566 | 96000 | 0.2262 |
6.1887 | 96500 | 0.2221 |
6.2207 | 97000 | 0.214 |
6.2528 | 97500 | 0.21 |
6.2849 | 98000 | 0.2072 |
6.3169 | 98500 | 0.2204 |
6.3490 | 99000 | 0.2041 |
6.3811 | 99500 | 0.2067 |
6.4131 | 100000 | 0.2102 |
6.4452 | 100500 | 0.2031 |
6.4773 | 101000 | 0.2107 |
6.5093 | 101500 | 0.2009 |
6.5414 | 102000 | 0.2057 |
6.5735 | 102500 | 0.1979 |
6.6055 | 103000 | 0.1994 |
6.6376 | 103500 | 0.2065 |
6.6697 | 104000 | 0.1958 |
6.7017 | 104500 | 0.2074 |
6.7338 | 105000 | 0.1941 |
6.7659 | 105500 | 0.2035 |
6.7979 | 106000 | 0.2003 |
6.8300 | 106500 | 0.2083 |
6.8621 | 107000 | 0.1921 |
6.8941 | 107500 | 0.1893 |
6.9262 | 108000 | 0.2014 |
6.9583 | 108500 | 0.192 |
6.9903 | 109000 | 0.1921 |
7.0 | 109151 | - |
7.0224 | 109500 | 0.2141 |
7.0544 | 110000 | 0.1868 |
7.0865 | 110500 | 0.1815 |
7.1186 | 111000 | 0.1793 |
7.1506 | 111500 | 0.1812 |
7.1827 | 112000 | 0.1853 |
7.2148 | 112500 | 0.1922 |
7.2468 | 113000 | 0.179 |
7.2789 | 113500 | 0.1707 |
7.3110 | 114000 | 0.1829 |
7.3430 | 114500 | 0.1743 |
7.3751 | 115000 | 0.1787 |
7.4072 | 115500 | 0.1815 |
7.4392 | 116000 | 0.1776 |
7.4713 | 116500 | 0.1773 |
7.5034 | 117000 | 0.1753 |
7.5354 | 117500 | 0.1816 |
7.5675 | 118000 | 0.1795 |
7.5996 | 118500 | 0.178 |
7.6316 | 119000 | 0.177 |
7.6637 | 119500 | 0.175 |
7.6958 | 120000 | 0.1701 |
7.7278 | 120500 | 0.1686 |
7.7599 | 121000 | 0.1727 |
7.7920 | 121500 | 0.1733 |
7.8240 | 122000 | 0.1707 |
7.8561 | 122500 | 0.1729 |
7.8882 | 123000 | 0.1569 |
7.9202 | 123500 | 0.1657 |
7.9523 | 124000 | 0.1773 |
7.9844 | 124500 | 0.1625 |
8.0 | 124744 | - |
8.0164 | 125000 | 0.1824 |
8.0485 | 125500 | 0.1852 |
8.0805 | 126000 | 0.1701 |
8.1126 | 126500 | 0.1573 |
8.1447 | 127000 | 0.1614 |
8.1767 | 127500 | 0.1624 |
8.2088 | 128000 | 0.1575 |
8.2409 | 128500 | 0.1481 |
8.2729 | 129000 | 0.1537 |
8.3050 | 129500 | 0.1616 |
8.3371 | 130000 | 0.1544 |
8.3691 | 130500 | 0.1511 |
8.4012 | 131000 | 0.1569 |
8.4333 | 131500 | 0.1535 |
8.4653 | 132000 | 0.1489 |
8.4974 | 132500 | 0.1593 |
8.5295 | 133000 | 0.1552 |
8.5615 | 133500 | 0.1578 |
8.5936 | 134000 | 0.1501 |
8.6257 | 134500 | 0.156 |
8.6577 | 135000 | 0.1455 |
8.6898 | 135500 | 0.1524 |
8.7219 | 136000 | 0.1344 |
8.7539 | 136500 | 0.1513 |
8.7860 | 137000 | 0.141 |
8.8181 | 137500 | 0.1518 |
8.8501 | 138000 | 0.1468 |
8.8822 | 138500 | 0.1416 |
8.9143 | 139000 | 0.1434 |
8.9463 | 139500 | 0.1495 |
8.9784 | 140000 | 0.1364 |
9.0 | 140337 | - |
9.0105 | 140500 | 0.1507 |
9.0425 | 141000 | 0.1496 |
9.0746 | 141500 | 0.1475 |
9.1067 | 142000 | 0.1348 |
9.1387 | 142500 | 0.1282 |
9.1708 | 143000 | 0.1362 |
9.2028 | 143500 | 0.1364 |
9.2349 | 144000 | 0.1385 |
9.2670 | 144500 | 0.1309 |
9.2990 | 145000 | 0.1324 |
9.3311 | 145500 | 0.1354 |
9.3632 | 146000 | 0.1283 |
9.3952 | 146500 | 0.1239 |
9.4273 | 147000 | 0.126 |
9.4594 | 147500 | 0.1232 |
9.4914 | 148000 | 0.1269 |
9.5235 | 148500 | 0.1269 |
9.5556 | 149000 | 0.1299 |
9.5876 | 149500 | 0.1367 |
9.6197 | 150000 | 0.1354 |
9.6518 | 150500 | 0.1239 |
9.6838 | 151000 | 0.1311 |
9.7159 | 151500 | 0.1235 |
9.7480 | 152000 | 0.129 |
9.7800 | 152500 | 0.1244 |
9.8121 | 153000 | 0.1201 |
9.8442 | 153500 | 0.1332 |
9.8762 | 154000 | 0.1189 |
9.9083 | 154500 | 0.1221 |
9.9404 | 155000 | 0.1228 |
9.9724 | 155500 | 0.1173 |
10.0 | 155930 | - |
10.0045 | 156000 | 0.1347 |
10.0366 | 156500 | 0.1384 |
10.0686 | 157000 | 0.1402 |
10.1007 | 157500 | 0.1161 |
10.1328 | 158000 | 0.1141 |
10.1648 | 158500 | 0.1199 |
10.1969 | 159000 | 0.1328 |
10.2289 | 159500 | 0.1263 |
10.2610 | 160000 | 0.1143 |
10.2931 | 160500 | 0.1207 |
10.3251 | 161000 | 0.1119 |
10.3572 | 161500 | 0.114 |
10.3893 | 162000 | 0.114 |
10.4213 | 162500 | 0.1118 |
10.4534 | 163000 | 0.1228 |
10.4855 | 163500 | 0.1209 |
10.5175 | 164000 | 0.1153 |
10.5496 | 164500 | 0.118 |
10.5817 | 165000 | 0.1118 |
10.6137 | 165500 | 0.1206 |
10.6458 | 166000 | 0.1108 |
10.6779 | 166500 | 0.1084 |
10.7099 | 167000 | 0.1127 |
10.7420 | 167500 | 0.1001 |
10.7741 | 168000 | 0.1073 |
10.8061 | 168500 | 0.1174 |
10.8382 | 169000 | 0.1143 |
10.8703 | 169500 | 0.1158 |
10.9023 | 170000 | 0.1099 |
10.9344 | 170500 | 0.0998 |
10.9665 | 171000 | 0.1009 |
10.9985 | 171500 | 0.1167 |
11.0 | 171523 | - |
11.0306 | 172000 | 0.1161 |
11.0627 | 172500 | 0.1126 |
11.0947 | 173000 | 0.1046 |
11.1268 | 173500 | 0.1054 |
11.1589 | 174000 | 0.1063 |
11.1909 | 174500 | 0.1136 |
11.2230 | 175000 | 0.108 |
11.2551 | 175500 | 0.1014 |
11.2871 | 176000 | 0.1036 |
11.3192 | 176500 | 0.1043 |
11.3512 | 177000 | 0.0973 |
11.3833 | 177500 | 0.0934 |
11.4154 | 178000 | 0.095 |
11.4474 | 178500 | 0.1032 |
11.4795 | 179000 | 0.1089 |
11.5116 | 179500 | 0.098 |
11.5436 | 180000 | 0.099 |
11.5757 | 180500 | 0.1007 |
11.6078 | 181000 | 0.096 |
11.6398 | 181500 | 0.0986 |
11.6719 | 182000 | 0.1033 |
11.7040 | 182500 | 0.0899 |
11.7360 | 183000 | 0.0946 |
11.7681 | 183500 | 0.0943 |
11.8002 | 184000 | 0.0954 |
11.8322 | 184500 | 0.0955 |
11.8643 | 185000 | 0.0924 |
11.8964 | 185500 | 0.0847 |
11.9284 | 186000 | 0.0914 |
11.9605 | 186500 | 0.0918 |
11.9926 | 187000 | 0.099 |
12.0 | 187116 | - |
12.0246 | 187500 | 0.1029 |
12.0567 | 188000 | 0.1032 |
12.0888 | 188500 | 0.0864 |
12.1208 | 189000 | 0.0921 |
12.1529 | 189500 | 0.0959 |
12.1850 | 190000 | 0.0846 |
12.2170 | 190500 | 0.0924 |
12.2491 | 191000 | 0.0897 |
12.2812 | 191500 | 0.0858 |
12.3132 | 192000 | 0.0851 |
12.3453 | 192500 | 0.0925 |
12.3773 | 193000 | 0.0963 |
12.4094 | 193500 | 0.0867 |
12.4415 | 194000 | 0.0929 |
12.4735 | 194500 | 0.0904 |
12.5056 | 195000 | 0.0854 |
12.5377 | 195500 | 0.0876 |
12.5697 | 196000 | 0.0899 |
12.6018 | 196500 | 0.09 |
12.6339 | 197000 | 0.0921 |
12.6659 | 197500 | 0.0829 |
12.6980 | 198000 | 0.0952 |
12.7301 | 198500 | 0.087 |
12.7621 | 199000 | 0.086 |
12.7942 | 199500 | 0.0836 |
12.8263 | 200000 | 0.0845 |
12.8583 | 200500 | 0.0808 |
12.8904 | 201000 | 0.0771 |
12.9225 | 201500 | 0.0815 |
12.9545 | 202000 | 0.0901 |
12.9866 | 202500 | 0.0871 |
13.0 | 202709 | - |
13.0187 | 203000 | 0.088 |
13.0507 | 203500 | 0.089 |
13.0828 | 204000 | 0.081 |
13.1149 | 204500 | 0.0739 |
13.1469 | 205000 | 0.0825 |
13.1790 | 205500 | 0.0855 |
13.2111 | 206000 | 0.0788 |
13.2431 | 206500 | 0.0769 |
13.2752 | 207000 | 0.0706 |
13.3073 | 207500 | 0.0821 |
13.3393 | 208000 | 0.0752 |
13.3714 | 208500 | 0.0746 |
13.4035 | 209000 | 0.066 |
13.4355 | 209500 | 0.0779 |
13.4676 | 210000 | 0.0755 |
13.4996 | 210500 | 0.0829 |
13.5317 | 211000 | 0.0731 |
13.5638 | 211500 | 0.086 |
13.5958 | 212000 | 0.078 |
13.6279 | 212500 | 0.0724 |
13.6600 | 213000 | 0.0696 |
13.6920 | 213500 | 0.0789 |
13.7241 | 214000 | 0.0657 |
13.7562 | 214500 | 0.0767 |
13.7882 | 215000 | 0.0728 |
13.8203 | 215500 | 0.071 |
13.8524 | 216000 | 0.0733 |
13.8844 | 216500 | 0.0621 |
13.9165 | 217000 | 0.0677 |
13.9486 | 217500 | 0.0761 |
13.9806 | 218000 | 0.0669 |
14.0 | 218302 | - |
14.0127 | 218500 | 0.0848 |
14.0448 | 219000 | 0.0647 |
14.0768 | 219500 | 0.0717 |
14.1089 | 220000 | 0.0653 |
14.1410 | 220500 | 0.0615 |
14.1730 | 221000 | 0.0711 |
14.2051 | 221500 | 0.0674 |
14.2372 | 222000 | 0.0674 |
14.2692 | 222500 | 0.0657 |
14.3013 | 223000 | 0.0727 |
14.3334 | 223500 | 0.0709 |
14.3654 | 224000 | 0.061 |
14.3975 | 224500 | 0.0638 |
14.4296 | 225000 | 0.0704 |
14.4616 | 225500 | 0.0623 |
14.4937 | 226000 | 0.065 |
14.5257 | 226500 | 0.0657 |
14.5578 | 227000 | 0.0634 |
14.5899 | 227500 | 0.0555 |
14.6219 | 228000 | 0.0647 |
14.6540 | 228500 | 0.0616 |
14.6861 | 229000 | 0.0645 |
14.7181 | 229500 | 0.0649 |
14.7502 | 230000 | 0.0612 |
14.7823 | 230500 | 0.0646 |
14.8143 | 231000 | 0.0571 |
14.8464 | 231500 | 0.0561 |
14.8785 | 232000 | 0.0598 |
14.9105 | 232500 | 0.0634 |
14.9426 | 233000 | 0.0657 |
14.9747 | 233500 | 0.0644 |
15.0 | 233895 | - |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1
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",
}