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SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("BlackBeenie/nomic-embed-text-v2-moe-msmarco-bpr")
5# Run inference
6sentences = [
7 'what services are offered by adult day care',
8 'Consumer Guide to Long Term Care. Adult Day Care. Adult day care is a planned program offered in a group setting that provides services that improve or maintain health or functioning, and social activities for seniors and persons with disabilities.',
9 'The Met Life Market survey of 2008 on adult day services states the average cost for adult day care services is $64 per day. There has been an increase of 5% in these services in the past year.',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]sentence_0, sentence_1, and sentence_2| sentence_0 | sentence_1 | sentence_2 | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| sentence_0 | sentence_1 | sentence_2 |
|---|---|---|
what the history of bluetooth | When asked about the name Bluetooth, I explained that Bluetooth was borrowed from the 10th century, second King of Denmark, King Harald Bluetooth; who was famous for uniting Scandinavia just as we intended to unite the PC and cellular industries with a short-range wireless link. | Technology: 1 How secure is a Bluetooth network? 2 What is Frequency-Hopping Spread Spectrum (FHSS)? 3 Will other RF (Radio Frequency) devices interfere with Bluetooth Devices? 4 Will Bluetooth and Wireless LAN (WLAN) interfere with each other? 5 What is the data throughput speed of a Bluetooth connection? 6 What is the range of Bluetooth 7 ... What kind of ... |
how thin can a concrete slab be | Another issue that must be addressed is the added weight of the thin-slab. Poured gypsum thin-slabs typically add 13 to 15 pounds per square foot to the dead loading of a floor structure. Standard weight concrete thin slabs add about 18 pounds per square foot (at 1.5 thickness). | Find the Area in square feet: We will use a concrete slab pour for our example. Letâs say that we need to figure out the yardage for a slab that will be 15 feet long by 10 feet wide and 4 inches thick. First we find the area by multiplying the length times the width. 1 15 feet X 10 feet = 150 square feet. |
how long to cook eggs to hard boil | This method works best if the eggs are in a single layer, but you can double them up as well, you'll just need to add more time to the steaming time. 3 Set your timer for 6 minutes for soft boiled, 10 minutes for hard boiled with a still translucent and bright yolk, or 12-15 minutes for cooked-through hard boiled. | Hard-Steamed Eggs. Fill a pot that can comfortably hold your steamer with the lid on with 1 to 2 inches of water. Bring to a rolling boil, 212 degrees Fahrenheit. Place your eggs in a metal steamer, and lower the basket into the pot. The eggs should sit above the boiling water. Cover and cook for 12 minutes. Hard-steamed eggs, like hard-boiled eggs, are eggs that are cooked until the egg yolk is fully set and has turned to a chalky texture. |
beir.losses.bpr_loss.BPRLosseval_strategy: stepsper_device_train_batch_size: 32per_device_eval_batch_size: 32num_train_epochs: 5fp16: Truemulti_dataset_batch_sampler: round_robinoverwrite_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: 5max_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: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_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: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin| Epoch | Step | Training Loss |
|---|---|---|
| 0.0321 | 500 | 0.3396 |
| 0.0641 | 1000 | 0.2094 |
| 0.0962 | 1500 | 0.21 |
| 0.1283 | 2000 | 0.1955 |
| 0.1603 | 2500 | 0.1989 |
| 0.1924 | 3000 | 0.1851 |
| 0.2245 | 3500 | 0.1839 |
| 0.2565 | 4000 | 0.1859 |
| 0.2886 | 4500 | 0.1892 |
| 0.3207 | 5000 | 0.1865 |
| 0.3527 | 5500 | 0.1773 |
| 0.3848 | 6000 | 0.1796 |
| 0.4169 | 6500 | 0.1929 |
| 0.4489 | 7000 | 0.1829 |
| 0.4810 | 7500 | 0.172 |
| 0.5131 | 8000 | 0.1792 |
| 0.5451 | 8500 | 0.1747 |
| 0.5772 | 9000 | 0.1802 |
| 0.6092 | 9500 | 0.1856 |
| 0.6413 | 10000 | 0.1751 |
| 0.6734 | 10500 | 0.173 |
| 0.7054 | 11000 | 0.1774 |
| 0.7375 | 11500 | 0.1722 |
| 0.7696 | 12000 | 0.1825 |
| 0.8016 | 12500 | 0.1714 |
| 0.8337 | 13000 | 0.1732 |
| 0.8658 | 13500 | 0.167 |
| 0.8978 | 14000 | 0.1792 |
| 0.9299 | 14500 | 0.1697 |
| 0.9620 | 15000 | 0.1682 |
| 0.9940 | 15500 | 0.1764 |
| 1.0 | 15593 | - |
| 1.0261 | 16000 | 0.0875 |
| 1.0582 | 16500 | 0.0798 |
| 1.0902 | 17000 | 0.0764 |
| 1.1223 | 17500 | 0.0783 |
| 1.1544 | 18000 | 0.0759 |
| 1.1864 | 18500 | 0.0834 |
| 1.2185 | 19000 | 0.082 |
| 1.2506 | 19500 | 0.0827 |
| 1.2826 | 20000 | 0.0876 |
| 1.3147 | 20500 | 0.0819 |
| 1.3468 | 21000 | 0.0841 |
| 1.3788 | 21500 | 0.0815 |
| 1.4109 | 22000 | 0.0819 |
| 1.4430 | 22500 | 0.0883 |
| 1.4750 | 23000 | 0.0826 |
| 1.5071 | 23500 | 0.0837 |
| 1.5392 | 24000 | 0.086 |
| 1.5712 | 24500 | 0.0806 |
| 1.6033 | 25000 | 0.0918 |
| 1.6353 | 25500 | 0.0885 |
| 1.6674 | 26000 | 0.0885 |
| 1.6995 | 26500 | 0.088 |
| 1.7315 | 27000 | 0.0843 |
| 1.7636 | 27500 | 0.0915 |
| 1.7957 | 28000 | 0.0843 |
| 1.8277 | 28500 | 0.0868 |
| 1.8598 | 29000 | 0.0857 |
| 1.8919 | 29500 | 0.0931 |
| 1.9239 | 30000 | 0.0852 |
| 1.9560 | 30500 | 0.0913 |
| 1.9881 | 31000 | 0.0857 |
| 2.0 | 31186 | - |
| 2.0201 | 31500 | 0.0547 |
| 2.0522 | 32000 | 0.0459 |
| 2.0843 | 32500 | 0.0451 |
| 2.1163 | 33000 | 0.0407 |
| 2.1484 | 33500 | 0.0469 |
| 2.1805 | 34000 | 0.0459 |
| 2.2125 | 34500 | 0.0508 |
| 2.2446 | 35000 | 0.0508 |
| 2.2767 | 35500 | 0.0518 |
| 2.3087 | 36000 | 0.0552 |
| 2.3408 | 36500 | 0.0491 |
| 2.3729 | 37000 | 0.0575 |
| 2.4049 | 37500 | 0.0558 |
| 2.4370 | 38000 | 0.0475 |
| 2.4691 | 38500 | 0.0486 |
| 2.5011 | 39000 | 0.0536 |
| 2.5332 | 39500 | 0.0559 |
| 2.5653 | 40000 | 0.0524 |
| 2.5973 | 40500 | 0.0496 |
| 2.6294 | 41000 | 0.0486 |
| 2.6615 | 41500 | 0.0526 |
| 2.6935 | 42000 | 0.0443 |
| 2.7256 | 42500 | 0.058 |
| 2.7576 | 43000 | 0.0543 |
| 2.7897 | 43500 | 0.0527 |
| 2.8218 | 44000 | 0.0528 |
| 2.8538 | 44500 | 0.0573 |
| 2.8859 | 45000 | 0.0628 |
| 2.9180 | 45500 | 0.0443 |
| 2.9500 | 46000 | 0.0531 |
| 2.9821 | 46500 | 0.0554 |
| 3.0 | 46779 | - |
| 3.0142 | 47000 | 0.0346 |
| 3.0462 | 47500 | 0.0288 |
| 3.0783 | 48000 | 0.0219 |
| 3.1104 | 48500 | 0.0259 |
| 3.1424 | 49000 | 0.0237 |
| 3.1745 | 49500 | 0.0307 |
| 3.2066 | 50000 | 0.0234 |
| 3.2386 | 50500 | 0.0312 |
| 3.2707 | 51000 | 0.0297 |
| 3.3028 | 51500 | 0.0299 |
| 3.3348 | 52000 | 0.0326 |
| 3.3669 | 52500 | 0.0266 |
| 3.3990 | 53000 | 0.0296 |
| 3.4310 | 53500 | 0.0289 |
| 3.4631 | 54000 | 0.0216 |
| 3.4952 | 54500 | 0.0289 |
| 3.5272 | 55000 | 0.033 |
| 3.5593 | 55500 | 0.0248 |
| 3.5914 | 56000 | 0.0246 |
| 3.6234 | 56500 | 0.0287 |
| 3.6555 | 57000 | 0.0267 |
| 3.6876 | 57500 | 0.0285 |
| 3.7196 | 58000 | 0.0288 |
| 3.7517 | 58500 | 0.0283 |
| 3.7837 | 59000 | 0.0283 |
| 3.8158 | 59500 | 0.029 |
| 3.8479 | 60000 | 0.0327 |
| 3.8799 | 60500 | 0.0239 |
| 3.9120 | 61000 | 0.0356 |
| 3.9441 | 61500 | 0.0323 |
| 3.9761 | 62000 | 0.0213 |
| 4.0 | 62372 | - |
| 4.0082 | 62500 | 0.0275 |
| 4.0403 | 63000 | 0.0125 |
| 4.0723 | 63500 | 0.0183 |
| 4.1044 | 64000 | 0.0138 |
| 4.1365 | 64500 | 0.0174 |
| 4.1685 | 65000 | 0.0088 |
| 4.2006 | 65500 | 0.0126 |
| 4.2327 | 66000 | 0.0134 |
| 4.2647 | 66500 | 0.0099 |
| 4.2968 | 67000 | 0.0188 |
| 4.3289 | 67500 | 0.0112 |
| 4.3609 | 68000 | 0.0156 |
| 4.3930 | 68500 | 0.0175 |
| 4.4251 | 69000 | 0.0128 |
| 4.4571 | 69500 | 0.0154 |
| 4.4892 | 70000 | 0.0127 |
| 4.5213 | 70500 | 0.0131 |
| 4.5533 | 71000 | 0.017 |
| 4.5854 | 71500 | 0.0116 |
| 4.6175 | 72000 | 0.0137 |
| 4.6495 | 72500 | 0.0156 |
| 4.6816 | 73000 | 0.0155 |
| 4.7137 | 73500 | 0.0078 |
| 4.7457 | 74000 | 0.0152 |
| 4.7778 | 74500 | 0.0089 |
| 4.8099 | 75000 | 0.0116 |
| 4.8419 | 75500 | 0.0144 |
| 4.8740 | 76000 | 0.0112 |
| 4.9060 | 76500 | 0.0108 |
| 4.9381 | 77000 | 0.0188 |
| 4.9702 | 77500 | 0.0109 |
| 5.0 | 77965 | - |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}