metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:498970
  - loss:BPRLoss
base_model: nomic-ai/nomic-embed-text-v2-moe
widget:
  - source_sentence: what was the start treaty 2010
    sentences:
      - "Strategic Offensive Reductions: The Treaty between the United States of America and the Russian Federation on Measures for the Further Reduction and Limitation of Strategic Offensive Arms, also known as the New START Treaty, entered into force on February 5, 2011.nder the Treaty, the United States and Russia must meet the Treatyâ\x80\x99s central limits on strategic arms by February 5, 2018; seven years from the date the Treaty entered into force. Each Party has the flexibility to determine for itself the structure of its strategic forces within the aggregate limits of the Treaty."
      - >-
        Nuclear pharmacy practice: hour-for-hour credit in a licensed nuclear
        pharmacy or health care facility approved by state or federal agencies
        to handle radioactive materials, to a maximum of 4,000 hours.
      - >-
        Signed: 18 June 1979. Entered into Force: Never entered into force;
        superseded by the START I Treaty in 1991. Duration: Until 31 December
        1985; unless the Treaty is replaced earlier by an agreement further
        limiting strategic offensive arms. Parties: Soviet Union and United
        States.
  - source_sentence: is pez a word
    sentences:
      - >-
        From dispensers to candy, there's a PEZ for anyone and everyone. Look
        for these PEZ products at your local retailer.rom dispensers to candy,
        there's a PEZ for anyone and everyone. Look for these PEZ products at
        your local retailer.
      - >-
        PEZ was first introduced in 1927 in Vienna, Austria as a breath mint for
        adults! The word PEZ was created using the first, middle and last letter
        in the German word for peppermint P feff E rmin Z.
      - >-
        Boonville is a city in Boon Township, Warrick County, Indiana, United
        States. The population was 6,246 at the 2010 census.The city is the
        county seat of Warrick County.oonville was founded in 1818 and named for
        Jesse Boon, father of Ratliff Boon. A post office has been in operation
        at Boonville since 1820. Boonville was incorporated in 1858.
  - source_sentence: us budget deficit by president
    sentences:
      - "By 2022, the government will once again be running trillion-dollar deficits, the report said. â\x80\x9CWe still have a lot of work to do,â\x80\x9D said House Budget Committee Chairman Paul Ryan. Lawmakers can take some credit for the short-term improvement in the budget outlook, the report showed, though the strengthening economy helps as well."
      - >-
        However, when they are 3 to 4 months old, they become susceptible to the
        disease, so all calves should be vaccinated for blackleg by 4 months of
        age. A revaccination 3 to 6 weeks later according to product label
        directions is necessary to provide the best protec-tion.lackleg seldom
        affects cattle older than 2 years of age, most likely due to immunity
        induced by vaccines or natural exposure. However, sporadic cases do
        occur in cattle older than 2 years and are often associated with the
        reuse of needles for multiple injections.
      - >-
        According to this method, Barack Obama's budget is projected to run a
        deficit of $7.3 trillion over his eight years, making him the president
        with the largest budget deficit. George W. Bush is second, with a
        deficit of $3.29 trillion over his eight years.
  - source_sentence: what is a sixth sense
    sentences:
      - >-
        1 Extrasensory perception (ESP), commonly called the sixth sense. 2 
        Equilibrioception (sense of balance) and proprioception (sense of body
        position), commonly accepted physiological senses in addition to the
        usually considered five senses.
      - >-
        Glaze or glazing may refer to: 1  Glaze (metallurgy), a layer of
        compacted sintered oxide formed on some metals. 2  Glaze (cooking
        technique), a coating of a glossy, often sweet, mixture applied to
        food.  Glaze (painting technique), a layer of paint, thinned with a
        medium, so as to become somewhat transparent.
      - >-
        Definition of Proprioception. The term proprioception is used to
        describe the sensory information that contributes to the sense of
        position of self and movement. Sir Charles Bell named the sixth sense as
        the sense of the positions and actions of the limbs (McCloskey
        1978).eceptors of Proprioception. It is well recognized that joint
        movements activate receptors in the joint, skin and muscle. In turn, any
        of these receptors may play a role in the perception and control of limb
        movement and joint angle.
  - source_sentence: what services are offered by adult day care
    sentences:
      - >-
        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.
      - >-
        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.
      - >-
        As nouns the difference between tackle and guard is that tackle is
        (nautical) a system of ropes and blocks used to increase the force
        applied to the free end of the rope while guard is a person who, or
        thing that, protects or watches over something. As verbs the difference
        between tackle and guard
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on nomic-ai/nomic-embed-text-v2-moe

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v2-moe. 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: nomic-ai/nomic-embed-text-v2-moe
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 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})
)

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/nomic-embed-text-v2-moe-msmarco-bpr")
# Run inference
sentences = [
    'what services are offered by adult day care',
    '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.',
    '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.',
]
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, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 4 tokens mean: 9.75 tokens max: 24 tokens	min: 24 tokens mean: 89.23 tokens max: 241 tokens	min: 20 tokens mean: 86.66 tokens max: 280 tokens

Samples:

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.`

Loss: beir.losses.bpr_loss.BPRLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 5
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 5
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: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

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	-

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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",
}