This is a sentence-transformers model finetuned from thebajajra/RexBERT-large on the nomic-embed-unsupervised-data dataset. It maps sentences & paragraphs to a 1024-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: thebajajra/RexBERT-large
Maximum Sequence Length: 1024 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- nomic-embed-unsupervised-data
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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    "Corners are still lifting.",
]
documents = [
    'Hello, I got my ender 3 a little over a year ago and have gotten many successful prints off of my machine. \n\nI have always had a problem with the corners of my prints lifting. I originally used a glass plate. That by itself was horrible, but then I added hairspray, and that worked. The problem was that on long prints, corners still lifted.\n\nAfter doing this for around 5 months I switched to a PEI sheet.\n\nThis worked comparably as well as the glass/hairspray combo, except the corners STILL LIFT on long prints.\n\nNow I have a PEI sheet on boro glass with an EZABL attached and the corners of my prints are STILL LIFTING.\n\nI don\'t know what i could possibly be doing wrong. The bed must be level. I get beautiful first layers, which I have tried to "smudge" around during printing and I can confirm that the plastic is being layed down solidly.\n\nIf anyone could enlighten me as to what is going on I would be thrilled.\n\nI do have my first layer printing at 30% speed with 150% layer width with the print cooling fan off as well. Printing PLA at 200C tool temp, 60C bed.',
    'These are awesome quart jars. They have a beautiful color, and I use them for storing soups, nuts and homemade nut milk. I would purchase them again.',
    'Great product. I purchased this item becuase my wrists would ache after triceps day at the gym. I would never be able to straighten my wrist and this helped in fixing that issue.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5739, 0.0316, 0.1463]])

Training Details

Training Dataset

nomic-embed-unsupervised-data

Dataset: nomic-embed-unsupervised-data at 917bae6
Size: 221,599,363 training samples
Columns: query and document
Approximate statistics based on the first 1000 samples:
query document
type string string
details
min: 6 tokens
mean: 34.17 tokens
max: 1024 tokens
min: 8 tokens
mean: 166.07 tokens
max: 1024 tokens

Samples:

query	document
`Effect of steam reforming on methane-fueled chemical looping combustion with Cu-based oxygen carrier`	Abstract The reduction characteristics of Cu-based oxygen carrier with H 2 , CO and CH 4 were investigated using a fixed bed reactor, TPR and TGA. Results showed that temperatures for the complete reduction of Cu-based oxygen carrier with H 2 and CO are 300 °C and 225 °C, respectively, while the corresponding temperature with CH 4 is 650 °C. The carbon deposition from CH 4 occurred at over 550 °C. CO-chemisorption experiments were also conducted on the oxygen carrier, and it was indicated that Cu-based oxygen carrier sinter seriously at 700 °C. In order to lower the required reduction temperature of oxygen carriers, a new chemical looping combustion (CLC) process with CH 4 steam reforming has been presented in this paper. The basic feasibility of the process was illustrated using CuO–SiO 2 . The new CLC process has the potential to replace the conventional gas-fired middle- and low-pressure steam and hot water boilers.
`who appointed onesicritus as chief pilot of the fleet`	by the king to hold a conference with the Indian philosophers or Gymnosophists, the details of which have been transmitted to us from his own account of the interview. It was Onesicritus, whom Alexander first sent to summon Dandamis to his court. When later Onesicritus returned empty-handed with the reply of Dandamis, the King went to forest to visit Dandamis. When Alexander constructed his fleet on the Hydaspes, he appointed Onesicritus to the important position of pilot of the king's ship, or chief pilot of the fleet (). Onesicritus held this position not only during the descent of the Indus,
`when did the madonna of foligno go to paris`	Madonna of Foligno hence the name. In 1799 it was carried to Paris, France by Napoleon. There, in 1802, the painting was transferred from panel to canvas by Hacquin and restored by Roser of Heidelberg. A note was made by the restorer: "Rapporto dei cittadini Guijon Vincent Tannay e Berthollet sul ristauro dei quadri di Raffaello conosciuto sotto il nome di Madonna di Foligno." In 1815, after the Battle of Waterloo, it was returned to Italy, where it was placed in the room with the Transfiguration in the Pinacoteca Vaticana of the Vatican Museum in the Vatican City. The painting is a "sacra

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

nomic-embed-unsupervised-data

Dataset: nomic-embed-unsupervised-data at 917bae6
Size: 1,113,579 evaluation samples
Columns: query and document
Approximate statistics based on the first 1000 samples:
query document
type string string
details
min: 5 tokens
mean: 31.98 tokens
max: 1024 tokens
min: 6 tokens
mean: 161.48 tokens
max: 1024 tokens

Samples:

query	document
`Concise methods for the synthesis of chiral polyoxazolines and their application in asymmetric hydrosilylation`	Seven polyoxazoline ligands were synthesized in high yield in a one-pot reaction by heating polycarboxylic acids or their esters and chiral β-amino alcohols under reflux with concomitant removal of water or the alcohol produced in the reaction. The method is much simpler and more efficient in comparison to those methods reported in the literature.The compounds were used as chiral ligands in the rhodium-catalyzed asymmetric hydrosilylation of aromatic ketones, and the effects of the linkers and the substituents present on the oxazoline rings on the yield and enantioselectivity investigated. Compound 2 was identified as the best ligand of this family for the hydrosilylation of aromatic ketones.
`On the road to a stronger public health workforce: visual tools to address complex challenges.`	`The Public Health Workforce Taxonomy: Revisions and Recommendations for Implementation`
`140mm Jetflo fan availability?`	`I recently purchased a Nepton 280L, and would like to install an additional pair of 140mm Jetflo fans. Unfortunately they don't seem to be currently available, will they be in the future?[object Object][object Object]Thank you so much![object Object][object Object]PS - I'm loving the cooling system!`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 192
per_device_eval_batch_size: 128
learning_rate: 1e-05
num_train_epochs: 4
warmup_steps: 1000
bf16: True
dataloader_num_workers: 20
dataloader_prefetch_factor: 4
ddp_find_unused_parameters: False

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 192
per_device_eval_batch_size: 128
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: 1e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 1000
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
bf16: True
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: True
dataloader_num_workers: 20
dataloader_prefetch_factor: 4
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: False
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0007	100	3.8319	-
0.0014	200	1.5773	-
0.0021	300	0.2044	-
0.0028	400	0.0729	-
0.0035	500	0.0504	-
0.0042	600	0.0386	-
0.0049	700	0.0314	-
0.0055	800	0.0265	-
0.0062	900	0.0235	-
0.0069	1000	0.0203	-
0.0076	1100	0.0186	-
0.0083	1200	0.017	-
0.0090	1300	0.0157	-
0.0097	1400	0.0149	-
0.0104	1500	0.0144	-
0.0111	1600	0.0139	-
0.0118	1700	0.013	-
0.0125	1800	0.0125	-
0.0132	1900	0.0126	-
0.0139	2000	0.0119	-
0.0146	2100	0.0116	-
0.0152	2200	0.011	-
0.0159	2300	0.011	-
0.0166	2400	0.0105	-
0.0173	2500	0.0104	-
0.0180	2600	0.0098	-
0.0187	2700	0.0101	-
0.0194	2800	0.0094	-
0.0201	2900	0.0092	-
0.0208	3000	0.0093	-
0.0215	3100	0.0089	-
0.0222	3200	0.0089	-
0.0229	3300	0.0088	-
0.0236	3400	0.0086	-
0.0243	3500	0.0085	-
0.0250	3600	0.0084	-
0.0256	3700	0.0081	-
0.0263	3800	0.0082	-
0.0270	3900	0.0078	-
0.0277	4000	0.0079	-
0.0284	4100	0.0077	-
0.0291	4200	0.0077	-
0.0298	4300	0.0076	-
0.0305	4400	0.007	-
0.0312	4500	0.0072	-
0.0319	4600	0.0074	-
0.0326	4700	0.0068	-
0.0333	4800	0.0072	-
0.0340	4900	0.0069	-
0.0347	5000	0.007	-
0.0354	5100	0.0067	-
0.0360	5200	0.0069	-
0.0367	5300	0.0069	-
0.0374	5400	0.0068	-
0.0381	5500	0.0065	-
0.0388	5600	0.0063	-
0.0395	5700	0.0062	-
0.0402	5800	0.0065	-
0.0409	5900	0.0062	-
0.0416	6000	0.0062	-
0.0423	6100	0.0061	-
0.0430	6200	0.0062	-
0.0437	6300	0.006	-
0.0444	6400	0.0061	-
0.0451	6500	0.0061	-
0.0457	6600	0.0061	-
0.0464	6700	0.006	-
0.0471	6800	0.006	-
0.0478	6900	0.0058	-
0.0485	7000	0.0056	-
0.0492	7100	0.0056	-
0.0499	7200	0.0057	-
0.0506	7300	0.0057	-
0.0513	7400	0.0055	-
0.0520	7500	0.0054	-
0.0527	7600	0.0055	-
0.0534	7700	0.0055	-
0.0541	7800	0.0054	-
0.0548	7900	0.0055	-
0.0555	8000	0.0055	-
0.0561	8100	0.0055	-
0.0568	8200	0.0053	-
0.0575	8300	0.0054	-
0.0582	8400	0.0053	-
0.0589	8500	0.005	-
0.0596	8600	0.0051	-
0.0603	8700	0.0061	-
0.0610	8800	0.0058	-
0.0617	8900	0.0052	-
0.0624	9000	0.0049	-
0.0631	9100	0.0051	-
0.0638	9200	0.0051	-
0.0645	9300	0.005	-
0.0652	9400	0.0049	-
0.0658	9500	0.0048	-
0.0665	9600	0.0048	-
0.0672	9700	0.0045	-
0.0679	9800	0.0049	-
0.0686	9900	0.0049	-
0.0693	10000	0.0048	-
0.0700	10100	0.0052	-
0.0707	10200	0.0051	-
0.0714	10300	0.005	-
0.0721	10400	0.0048	-
0.0728	10500	0.0047	-
0.0735	10600	0.0046	-
0.0742	10700	0.0046	-
0.0749	10800	0.0046	-
0.0756	10900	0.0045	-
0.0762	11000	0.0045	-
0.0769	11100	0.0046	-
0.0776	11200	0.0046	-
0.0783	11300	0.0045	-
0.0790	11400	0.0046	-
0.0797	11500	0.0045	-
0.0804	11600	0.0044	-
0.0811	11700	0.0045	-
0.0818	11800	0.0044	-
0.0825	11900	0.0045	-
0.0832	12000	0.0044	-
0.0839	12100	0.0042	-
0.0846	12200	0.0042	-
0.0853	12300	0.0042	-
0.0859	12400	0.0042	-
0.0866	12500	0.0042	-
0.0873	12600	0.0045	-
0.0880	12700	0.0042	-
0.0887	12800	0.0043	-
0.0894	12900	0.0043	-
0.0901	13000	0.0042	-
0.0908	13100	0.0043	-
0.0915	13200	0.0043	-
0.0922	13300	0.0041	-
0.0929	13400	0.0041	-
0.0936	13500	0.0042	-
0.0943	13600	0.004	-
0.0950	13700	0.0043	-
0.0957	13800	0.004	-
0.0963	13900	0.0049	-
0.0970	14000	0.0048	-
0.0977	14100	0.0044	-
0.0984	14200	0.0045	-
0.0991	14300	0.0044	-
0.0998	14400	0.0042	-
0.1005	14500	0.004	-
0.1012	14600	0.0039	-
0.1019	14700	0.0039	-
0.1026	14800	0.0042	-
0.1033	14900	0.0039	-
0.1040	15000	0.004	-
0.1047	15100	0.0038	-
0.1054	15200	0.0039	-
0.1061	15300	0.0038	-
0.1067	15400	0.0039	-
0.1074	15500	0.0039	-
0.1081	15600	0.0039	-
0.1088	15700	0.004	-
0.1095	15800	0.0039	-
0.1102	15900	0.0039	-
0.1109	16000	0.0039	-
0.1116	16100	0.0038	-
0.1123	16200	0.0039	-
0.1130	16300	0.0037	-
0.1137	16400	0.0037	-
0.1144	16500	0.0038	-
0.1151	16600	0.0038	-
0.1158	16700	0.0039	-
0.1164	16800	0.0037	-
0.1171	16900	0.0038	-
0.1178	17000	0.0037	-
0.1185	17100	0.0037	-
0.1192	17200	0.0038	-
0.1199	17300	0.0035	-
0.1206	17400	0.0038	-
0.1213	17500	0.0036	-
0.1220	17600	0.0036	-
0.1227	17700	0.0038	-
0.1234	17800	0.0036	-
0.1241	17900	0.0037	-
0.1248	18000	0.0037	-
0.1255	18100	0.0038	-
0.1262	18200	0.0037	-
0.1268	18300	0.0036	-
0.1275	18400	0.0038	-
0.1282	18500	0.0035	-
0.1289	18600	0.0037	-
0.1296	18700	0.0036	-
0.1303	18800	0.0037	-
0.1310	18900	0.0036	-
0.1317	19000	0.0036	-
0.1324	19100	0.0034	-
0.1331	19200	0.0035	-
0.1338	19300	0.0037	-
0.1345	19400	0.0036	-
0.1352	19500	0.0035	-
0.1359	19600	0.0036	-
0.1365	19700	0.0036	-
0.1372	19800	0.0035	-
0.1379	19900	0.0035	-
0.1386	20000	0.0034	-
0.1393	20100	0.0034	-
0.1400	20200	0.0034	-
0.1407	20300	0.0036	-
0.1414	20400	0.0034	-
0.1421	20500	0.0034	-
0.1428	20600	0.0035	-
0.1435	20700	0.0034	-
0.1442	20800	0.0032	-
0.1449	20900	0.0032	-
0.1456	21000	0.0034	-
0.1463	21100	0.0034	-
0.1469	21200	0.0033	-
0.1476	21300	0.0034	-
0.1483	21400	0.0032	-
0.1490	21500	0.0032	-
0.1497	21600	0.0034	-
0.1504	21700	0.0034	-
0.1511	21800	0.0033	-
0.1518	21900	0.0034	-
0.1525	22000	0.0034	-
0.1532	22100	0.0034	-
0.1539	22200	0.0033	-
0.1546	22300	0.0034	-
0.1553	22400	0.0033	-
0.1560	22500	0.0031	-
0.1567	22600	0.0034	-
0.1573	22700	0.0035	-
0.1580	22800	0.0033	-
0.1587	22900	0.0032	-
0.1594	23000	0.0033	-
0.1601	23100	0.0032	-
0.1608	23200	0.0032	-
0.1615	23300	0.0033	-
0.1622	23400	0.0032	-
0.1629	23500	0.0031	-
0.1636	23600	0.003	-
0.1643	23700	0.0032	-
0.1650	23800	0.0033	-
0.1657	23900	0.0032	-
0.1664	24000	0.0031	-
0.1670	24100	0.0032	-
0.1677	24200	0.0032	-
0.1684	24300	0.0033	-
0.1691	24400	0.0032	-
0.1698	24500	0.0033	-
0.1705	24600	0.0032	-
0.1712	24700	0.0032	-
0.1719	24800	0.0033	-
0.1726	24900	0.0031	-
0.1733	25000	0.0032	-
0.1740	25100	0.003	-
0.1747	25200	0.0031	-
0.1754	25300	0.003	-
0.1761	25400	0.003	-
0.1768	25500	0.0032	-
0.1774	25600	0.003	-
0.1781	25700	0.0031	-
0.1788	25800	0.0031	-
0.1795	25900	0.003	-
0.1802	26000	0.003	-
0.1809	26100	0.003	-
0.1816	26200	0.0031	-
0.1823	26300	0.0031	-
0.1830	26400	0.0031	-
0.1837	26500	0.0032	-
0.1844	26600	0.0029	-
0.1851	26700	0.0031	-
0.1858	26800	0.0031	-
0.1865	26900	0.003	-
0.1871	27000	0.003	-
0.1878	27100	0.003	-
0.1885	27200	0.0029	-
0.1892	27300	0.003	-
0.1899	27400	0.003	-
0.1906	27500	0.0029	-
0.1913	27600	0.0029	-
0.1920	27700	0.0029	-
0.1927	27800	0.003	-
0.1934	27900	0.003	-
0.1941	28000	0.0029	-
0.1948	28100	0.003	-
0.1955	28200	0.0029	-
0.1962	28300	0.0029	-
0.1969	28400	0.0029	-
0.1975	28500	0.0029	-
0.1982	28600	0.0028	-
0.1989	28700	0.0029	-
0.1996	28800	0.003	-
0.2	28854	-	0.0020
0.2003	28900	0.003	-
0.2010	29000	0.0031	-
0.2017	29100	0.003	-
0.2024	29200	0.003	-
0.2031	29300	0.0031	-
0.2038	29400	0.0031	-
0.2045	29500	0.003	-
0.2052	29600	0.003	-
0.2059	29700	0.0031	-
0.2066	29800	0.0029	-
0.2073	29900	0.003	-
0.2079	30000	0.0029	-
0.2086	30100	0.0029	-
0.2093	30200	0.0031	-
0.2100	30300	0.0029	-
0.2107	30400	0.0027	-
0.2114	30500	0.0029	-
0.2121	30600	0.003	-
0.2128	30700	0.0029	-
0.2135	30800	0.0029	-
0.2142	30900	0.003	-
0.2149	31000	0.0028	-
0.2156	31100	0.0028	-
0.2163	31200	0.0029	-
0.2170	31300	0.0027	-
0.2176	31400	0.0028	-
0.2183	31500	0.0027	-
0.2190	31600	0.0027	-
0.2197	31700	0.0028	-
0.2204	31800	0.0027	-
0.2211	31900	0.0029	-
0.2218	32000	0.0027	-
0.2225	32100	0.0028	-
0.2232	32200	0.0028	-
0.2239	32300	0.0029	-
0.2246	32400	0.0027	-
0.2253	32500	0.0028	-
0.2260	32600	0.0026	-
0.2267	32700	0.0028	-
0.2274	32800	0.0027	-
0.2280	32900	0.0028	-
0.2287	33000	0.0026	-
0.2294	33100	0.0026	-
0.2301	33200	0.0026	-
0.2308	33300	0.0028	-
0.2315	33400	0.0027	-
0.2322	33500	0.0028	-
0.2329	33600	0.0028	-
0.2336	33700	0.0026	-
0.2343	33800	0.0028	-
0.2350	33900	0.0027	-
0.2357	34000	0.0027	-
0.2364	34100	0.0028	-
0.2371	34200	0.0029	-
0.2377	34300	0.0027	-
0.2384	34400	0.0028	-
0.2391	34500	0.0027	-
0.2398	34600	0.0026	-
0.2405	34700	0.0026	-
0.2412	34800	0.0026	-
0.2419	34900	0.0028	-
0.2426	35000	0.0026	-
0.2433	35100	0.0026	-
0.2440	35200	0.0027	-
0.2447	35300	0.0026	-
0.2454	35400	0.0026	-
0.2461	35500	0.0026	-
0.2468	35600	0.0027	-
0.2475	35700	0.0028	-
0.2481	35800	0.0026	-
0.2488	35900	0.0027	-
0.2495	36000	0.0027	-
0.2502	36100	0.0027	-
0.2509	36200	0.0027	-
0.2516	36300	0.0026	-
0.2523	36400	0.0025	-
0.2530	36500	0.0027	-
0.2537	36600	0.0028	-
0.2544	36700	0.0026	-
0.2551	36800	0.0027	-
0.2558	36900	0.0026	-
0.2565	37000	0.0026	-
0.2572	37100	0.0025	-
0.2578	37200	0.0026	-
0.2585	37300	0.0027	-
0.2592	37400	0.0026	-
0.2599	37500	0.0026	-
0.2606	37600	0.0026	-
0.2613	37700	0.0026	-
0.2620	37800	0.0025	-
0.2627	37900	0.0026	-
0.2634	38000	0.0025	-
0.2641	38100	0.0025	-
0.2648	38200	0.0026	-
0.2655	38300	0.0025	-
0.2662	38400	0.0025	-
0.2669	38500	0.0025	-
0.2676	38600	0.0026	-
0.2682	38700	0.0026	-
0.2689	38800	0.0025	-
0.2696	38900	0.0026	-
0.2703	39000	0.0026	-
0.2710	39100	0.0026	-
0.2717	39200	0.0025	-
0.2724	39300	0.0026	-
0.2731	39400	0.0026	-
0.2738	39500	0.0027	-
0.2745	39600	0.0025	-
0.2752	39700	0.0026	-
0.2759	39800	0.0025	-
0.2766	39900	0.0026	-
0.2773	40000	0.0027	-
0.2780	40100	0.0025	-
0.2786	40200	0.0026	-
0.2793	40300	0.0026	-
0.2800	40400	0.0026	-
0.2807	40500	0.0024	-
0.2814	40600	0.0025	-
0.2821	40700	0.0027	-
0.2828	40800	0.0024	-
0.2835	40900	0.0025	-
0.2842	41000	0.0025	-
0.2849	41100	0.0024	-
0.2856	41200	0.0025	-
0.2863	41300	0.0025	-
0.2870	41400	0.0024	-
0.2877	41500	0.0026	-
0.2883	41600	0.0024	-
0.2890	41700	0.0026	-
0.2897	41800	0.0027	-
0.2904	41900	0.0025	-
0.2911	42000	0.0024	-
0.2918	42100	0.0026	-
0.2925	42200	0.0025	-
0.2932	42300	0.0026	-
0.2939	42400	0.0025	-
0.2946	42500	0.0025	-
0.2953	42600	0.0025	-
0.2960	42700	0.0023	-
0.2967	42800	0.0025	-
0.2974	42900	0.0023	-
0.2981	43000	0.0024	-
0.2987	43100	0.0025	-
0.2994	43200	0.0024	-
0.3001	43300	0.0024	-
0.3008	43400	0.0025	-
0.3015	43500	0.0024	-
0.3022	43600	0.0024	-
0.3029	43700	0.0024	-
0.3036	43800	0.0024	-
0.3043	43900	0.0023	-
0.3050	44000	0.0025	-
0.3057	44100	0.0025	-
0.3064	44200	0.0024	-
0.3071	44300	0.0024	-
0.3078	44400	0.0023	-
0.3084	44500	0.0025	-
0.3091	44600	0.0024	-
0.3098	44700	0.0025	-
0.3105	44800	0.0025	-
0.3112	44900	0.0026	-
0.3119	45000	0.0025	-
0.3126	45100	0.0025	-
0.3133	45200	0.0025	-
0.3140	45300	0.0024	-
0.3147	45400	0.0023	-
0.3154	45500	0.0024	-
0.3161	45600	0.0024	-
0.3168	45700	0.0024	-
0.3175	45800	0.0023	-
0.3182	45900	0.0024	-
0.3188	46000	0.0024	-
0.3195	46100	0.0023	-
0.3202	46200	0.0025	-
0.3209	46300	0.0024	-
0.3216	46400	0.0025	-
0.3223	46500	0.0023	-
0.3230	46600	0.0024	-
0.3237	46700	0.0023	-
0.3244	46800	0.0024	-
0.3251	46900	0.0023	-
0.3258	47000	0.0023	-
0.3265	47100	0.0024	-
0.3272	47200	0.0023	-
0.3279	47300	0.0023	-
0.3286	47400	0.0024	-
0.3292	47500	0.0023	-
0.3299	47600	0.0024	-
0.3306	47700	0.0024	-
0.3313	47800	0.0023	-
0.3320	47900	0.0024	-
0.3327	48000	0.0023	-
0.3334	48100	0.0023	-
0.3341	48200	0.0023	-
0.3348	48300	0.0024	-
0.3355	48400	0.0023	-
0.3362	48500	0.0023	-
0.3369	48600	0.0023	-
0.3376	48700	0.0022	-
0.3383	48800	0.0023	-
0.3389	48900	0.0023	-
0.3396	49000	0.0023	-
0.3403	49100	0.0023	-
0.3410	49200	0.0022	-
0.3417	49300	0.0022	-
0.3424	49400	0.0024	-
0.3431	49500	0.0022	-
0.3438	49600	0.0024	-
0.3445	49700	0.0024	-
0.3452	49800	0.0022	-
0.3459	49900	0.0023	-
0.3466	50000	0.0023	-
0.3473	50100	0.0024	-
0.3480	50200	0.0023	-
0.3487	50300	0.0022	-
0.3493	50400	0.0023	-
0.3500	50500	0.0022	-
0.3507	50600	0.0023	-
0.3514	50700	0.0023	-
0.3521	50800	0.0023	-
0.3528	50900	0.0023	-
0.3535	51000	0.0023	-
0.3542	51100	0.0022	-
0.3549	51200	0.0023	-
0.3556	51300	0.0022	-
0.3563	51400	0.0024	-
0.3570	51500	0.0022	-
0.3577	51600	0.0022	-
0.3584	51700	0.0023	-
0.3590	51800	0.0023	-
0.3597	51900	0.0023	-
0.3604	52000	0.0022	-
0.3611	52100	0.0023	-
0.3618	52200	0.0022	-
0.3625	52300	0.0021	-
0.3632	52400	0.0022	-
0.3639	52500	0.0022	-
0.3646	52600	0.0023	-
0.3653	52700	0.0021	-
0.3660	52800	0.0023	-
0.3667	52900	0.0022	-
0.3674	53000	0.0023	-
0.3681	53100	0.0022	-
0.3688	53200	0.0023	-
0.3694	53300	0.0024	-
0.3701	53400	0.0023	-
0.3708	53500	0.0022	-
0.3715	53600	0.0022	-
0.3722	53700	0.0023	-
0.3729	53800	0.0023	-
0.3736	53900	0.0023	-
0.3743	54000	0.0022	-
0.3750	54100	0.0023	-
0.3757	54200	0.0022	-
0.3764	54300	0.0023	-
0.3771	54400	0.0023	-
0.3778	54500	0.0022	-
0.3785	54600	0.0022	-
0.3792	54700	0.0022	-
0.3798	54800	0.0021	-
0.3805	54900	0.0023	-
0.3812	55000	0.0022	-
0.3819	55100	0.0022	-
0.3826	55200	0.0023	-
0.3833	55300	0.0021	-
0.3840	55400	0.0021	-
0.3847	55500	0.0022	-
0.3854	55600	0.0022	-
0.3861	55700	0.0022	-
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0.3875	55900	0.0022	-
0.3882	56000	0.0022	-
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0.3895	56200	0.0021	-
0.3902	56300	0.0022	-
0.3909	56400	0.0021	-
0.3916	56500	0.0022	-
0.3923	56600	0.0022	-
0.3930	56700	0.0024	-
0.3937	56800	0.0021	-
0.3944	56900	0.0021	-
0.3951	57000	0.0022	-
0.3958	57100	0.0021	-
0.3965	57200	0.0022	-
0.3972	57300	0.0022	-
0.3979	57400	0.0023	-
0.3986	57500	0.0022	-
0.3993	57600	0.0022	-
0.3999	57700	0.0022	-
0.4	57708	-	0.0015
0.4006	57800	0.0022	-
0.4013	57900	0.0021	-
0.4020	58000	0.0022	-
0.4027	58100	0.0021	-
0.4034	58200	0.0022	-
0.4041	58300	0.0022	-
0.4048	58400	0.002	-
0.4055	58500	0.0023	-
0.4062	58600	0.0021	-
0.4069	58700	0.0022	-
0.4076	58800	0.0021	-
0.4083	58900	0.0021	-
0.4090	59000	0.0022	-
0.4096	59100	0.002	-
0.4103	59200	0.0021	-
0.4110	59300	0.0022	-
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0.4124	59500	0.0022	-
0.4131	59600	0.0021	-
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0.4152	59900	0.0022	-
0.4159	60000	0.0022	-
0.4166	60100	0.0023	-
0.4173	60200	0.0022	-
0.4180	60300	0.0021	-
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0.4200	60600	0.0022	-
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0.4214	60800	0.0022	-
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0.4228	61000	0.0022	-
0.4235	61100	0.002	-
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0.4339	62600	0.0021	-
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0.4353	62800	0.0021	-
0.4360	62900	0.002	-
0.4367	63000	0.0019	-
0.4374	63100	0.0021	-
0.4381	63200	0.0021	-
0.4388	63300	0.0021	-
0.4395	63400	0.0023	-
0.4401	63500	0.0021	-
0.4408	63600	0.002	-
0.4415	63700	0.0021	-
0.4422	63800	0.0022	-
0.4429	63900	0.0022	-
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0.4478	64600	0.002	-
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0.4492	64800	0.0021	-
0.4499	64900	0.0022	-
0.4505	65000	0.0021	-
0.4512	65100	0.0021	-
0.4519	65200	0.0021	-
0.4526	65300	0.002	-
0.4533	65400	0.0021	-
0.4540	65500	0.002	-
0.4547	65600	0.002	-
0.4554	65700	0.0021	-
0.4561	65800	0.002	-
0.4568	65900	0.002	-
0.4575	66000	0.0021	-
0.4582	66100	0.002	-
0.4589	66200	0.0021	-
0.4596	66300	0.0019	-
0.4602	66400	0.002	-
0.4609	66500	0.002	-
0.4616	66600	0.002	-
0.4623	66700	0.002	-
0.4630	66800	0.002	-
0.4637	66900	0.002	-
0.4644	67000	0.002	-
0.4651	67100	0.0021	-
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0.4665	67300	0.0021	-
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0.4679	67500	0.0021	-
0.4686	67600	0.0021	-
0.4693	67700	0.0022	-
0.4700	67800	0.002	-
0.4706	67900	0.0021	-
0.4713	68000	0.002	-
0.4720	68100	0.0021	-
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0.4734	68300	0.002	-
0.4741	68400	0.0019	-
0.4748	68500	0.002	-
0.4755	68600	0.002	-
0.4762	68700	0.002	-
0.4769	68800	0.002	-
0.4776	68900	0.0021	-
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0.4803	69300	0.002	-
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0.4824	69600	0.0021	-
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0.4845	69900	0.0019	-
0.4852	70000	0.0021	-
0.4859	70100	0.002	-
0.4866	70200	0.0021	-
0.4873	70300	0.0019	-
0.4880	70400	0.002	-
0.4887	70500	0.0021	-
0.4894	70600	0.0018	-
0.4901	70700	0.002	-
0.4907	70800	0.0021	-
0.4914	70900	0.0021	-
0.4921	71000	0.002	-
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0.4935	71200	0.002	-
0.4942	71300	0.002	-
0.4949	71400	0.002	-
0.4956	71500	0.0019	-
0.4963	71600	0.002	-
0.4970	71700	0.002	-
0.4977	71800	0.0021	-
0.4984	71900	0.0019	-
0.4991	72000	0.002	-
0.4998	72100	0.0021	-
0.5005	72200	0.0019	-
0.5011	72300	0.002	-
0.5018	72400	0.0019	-
0.5025	72500	0.002	-
0.5032	72600	0.002	-
0.5039	72700	0.0021	-
0.5046	72800	0.002	-
0.5053	72900	0.0019	-
0.5060	73000	0.002	-
0.5067	73100	0.002	-
0.5074	73200	0.002	-
0.5081	73300	0.0019	-
0.5088	73400	0.002	-
0.5095	73500	0.002	-
0.5102	73600	0.002	-
0.5108	73700	0.002	-
0.5115	73800	0.0021	-
0.5122	73900	0.002	-
0.5129	74000	0.002	-
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0.5143	74200	0.002	-
0.5150	74300	0.0019	-
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0.5164	74500	0.002	-
0.5171	74600	0.002	-
0.5178	74700	0.002	-
0.5185	74800	0.002	-
0.5192	74900	0.0018	-
0.5199	75000	0.0019	-
0.5206	75100	0.0019	-
0.5212	75200	0.0019	-
0.5219	75300	0.002	-
0.5226	75400	0.0019	-
0.5233	75500	0.0018	-
0.5240	75600	0.0018	-
0.5247	75700	0.0019	-
0.5254	75800	0.002	-
0.5261	75900	0.0019	-
0.5268	76000	0.002	-
0.5275	76100	0.0018	-
0.5282	76200	0.0019	-
0.5289	76300	0.002	-
0.5296	76400	0.0019	-
0.5303	76500	0.0019	-
0.5309	76600	0.002	-
0.5316	76700	0.0019	-
0.5323	76800	0.0019	-
0.5330	76900	0.0019	-
0.5337	77000	0.002	-
0.5344	77100	0.0018	-
0.5351	77200	0.002	-
0.5358	77300	0.0019	-
0.5365	77400	0.0019	-
0.5372	77500	0.0018	-
0.5379	77600	0.0019	-
0.5386	77700	0.0019	-
0.5393	77800	0.0018	-
0.5400	77900	0.002	-
0.5407	78000	0.002	-
0.5413	78100	0.0018	-
0.5420	78200	0.0019	-
0.5427	78300	0.0018	-
0.5434	78400	0.0019	-
0.5441	78500	0.0019	-
0.5448	78600	0.0018	-
0.5455	78700	0.002	-
0.5462	78800	0.0019	-
0.5469	78900	0.0019	-
0.5476	79000	0.0019	-
0.5483	79100	0.0018	-
0.5490	79200	0.0019	-
0.5497	79300	0.0019	-
0.5504	79400	0.0019	-
0.5511	79500	0.0018	-
0.5517	79600	0.0019	-
0.5524	79700	0.0019	-
0.5531	79800	0.0019	-
0.5538	79900	0.0019	-
0.5545	80000	0.0019	-
0.5552	80100	0.0018	-
0.5559	80200	0.0019	-
0.5566	80300	0.0019	-
0.5573	80400	0.0018	-
0.5580	80500	0.0018	-
0.5587	80600	0.0018	-
0.5594	80700	0.0018	-
0.5601	80800	0.0019	-
0.5608	80900	0.0019	-
0.5614	81000	0.0019	-
0.5621	81100	0.0019	-
0.5628	81200	0.0019	-
0.5635	81300	0.002	-
0.5642	81400	0.0019	-
0.5649	81500	0.0019	-
0.5656	81600	0.0019	-
0.5663	81700	0.0018	-
0.5670	81800	0.0019	-
0.5677	81900	0.0018	-
0.5684	82000	0.0019	-
0.5691	82100	0.0021	-
0.5698	82200	0.0019	-
0.5705	82300	0.0019	-
0.5712	82400	0.0017	-
0.5718	82500	0.0019	-
0.5725	82600	0.002	-
0.5732	82700	0.0019	-
0.5739	82800	0.0019	-
0.5746	82900	0.0019	-
0.5753	83000	0.0018	-
0.5760	83100	0.0019	-
0.5767	83200	0.0019	-
0.5774	83300	0.0019	-
0.5781	83400	0.0018	-
0.5788	83500	0.0019	-
0.5795	83600	0.0019	-
0.5802	83700	0.0018	-
0.5809	83800	0.0019	-
0.5815	83900	0.0019	-
0.5822	84000	0.0019	-
0.5829	84100	0.0018	-
0.5836	84200	0.0018	-
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0.9461	136500	0.0015	-
0.9468	136600	0.0015	-
0.9475	136700	0.0015	-
0.9482	136800	0.0015	-
0.9489	136900	0.0015	-
0.9496	137000	0.0015	-
0.9503	137100	0.0016	-
0.9510	137200	0.0015	-
0.9517	137300	0.0015	-
0.9524	137400	0.0015	-
0.9531	137500	0.0016	-
0.9538	137600	0.0015	-
0.9545	137700	0.0015	-
0.9552	137800	0.0016	-
0.9558	137900	0.0016	-
0.9565	138000	0.0016	-
0.9572	138100	0.0015	-
0.9579	138200	0.0015	-
0.9586	138300	0.0015	-
0.9593	138400	0.0015	-
0.9600	138500	0.0015	-
0.9607	138600	0.0015	-
0.9614	138700	0.0015	-
0.9621	138800	0.0015	-
0.9628	138900	0.0014	-
0.9635	139000	0.0016	-
0.9642	139100	0.0015	-
0.9649	139200	0.0014	-
0.9656	139300	0.0015	-
0.9662	139400	0.0015	-
0.9669	139500	0.0015	-
0.9676	139600	0.0015	-
0.9683	139700	0.0015	-
0.9690	139800	0.0015	-
0.9697	139900	0.0015	-
0.9704	140000	0.0014	-
0.9711	140100	0.0014	-
0.9718	140200	0.0015	-
0.9725	140300	0.0015	-
0.9732	140400	0.0015	-
0.9739	140500	0.0015	-
0.9746	140600	0.0016	-
0.9753	140700	0.0015	-
0.9759	140800	0.0015	-
0.9766	140900	0.0015	-
0.9773	141000	0.0016	-
0.9780	141100	0.0015	-
0.9787	141200	0.0015	-
0.9794	141300	0.0016	-
0.9801	141400	0.0015	-
0.9808	141500	0.0014	-
0.9815	141600	0.0014	-
0.9822	141700	0.0015	-
0.9829	141800	0.0016	-
0.9836	141900	0.0015	-
0.9843	142000	0.0015	-
0.9850	142100	0.0015	-
0.9857	142200	0.0015	-
0.9863	142300	0.0014	-
0.9870	142400	0.0016	-
0.9877	142500	0.0016	-
0.9884	142600	0.0015	-
0.9891	142700	0.0015	-
0.9898	142800	0.0014	-
0.9905	142900	0.0015	-
0.9912	143000	0.0015	-
0.9919	143100	0.0014	-
0.9926	143200	0.0014	-
0.9933	143300	0.0015	-
0.9940	143400	0.0015	-
0.9947	143500	0.0015	-
0.9954	143600	0.0015	-
0.9960	143700	0.0015	-
0.9967	143800	0.0015	-
0.9974	143900	0.0015	-
0.9981	144000	0.0015	-
0.9988	144100	0.0015	-
0.9995	144200	0.0015	-
1.0	144270	-	0.0010

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu129
Accelerate: 1.11.0
Datasets: 4.3.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}

	query	document
type	string	string
details	min: 6 tokens mean: 34.17 tokens max: 1024 tokens	min: 8 tokens mean: 166.07 tokens max: 1024 tokens

thebajajra/RexBERT-large-embed-pf-v0.2

SentenceTransformer based on thebajajra/RexBERT-large

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

nomic-embed-unsupervised-data

Evaluation Dataset

nomic-embed-unsupervised-data

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

thebajajra/
RexBERT-large-embed-pf-v0.2