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",
}
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