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([[0.4861, 0.0704, 0.1127]])

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	4.1347	-
0.0014	200	3.9719	-
0.0021	300	3.2117	-
0.0028	400	1.0292	-
0.0035	500	0.2228	-
0.0042	600	0.1093	-
0.0049	700	0.0794	-
0.0055	800	0.0633	-
0.0062	900	0.0543	-
0.0069	1000	0.0468	-
0.0076	1100	0.0427	-
0.0083	1200	0.0391	-
0.0090	1300	0.0366	-
0.0097	1400	0.035	-
0.0104	1500	0.0331	-
0.0111	1600	0.0323	-
0.0118	1700	0.0306	-
0.0125	1800	0.0302	-
0.0132	1900	0.0301	-
0.0139	2000	0.0291	-
0.0146	2100	0.0287	-
0.0152	2200	0.0277	-
0.0159	2300	0.0273	-
0.0166	2400	0.0269	-
0.0173	2500	0.0263	-
0.0180	2600	0.0259	-
0.0187	2700	0.026	-
0.0194	2800	0.0253	-
0.0201	2900	0.0247	-
0.0208	3000	0.0246	-
0.0215	3100	0.0241	-
0.0222	3200	0.0238	-
0.0229	3300	0.0238	-
0.0236	3400	0.0237	-
0.0243	3500	0.0234	-
0.0250	3600	0.023	-
0.0256	3700	0.0227	-
0.0263	3800	0.0229	-
0.0270	3900	0.0228	-
0.0277	4000	0.0228	-
0.0284	4100	0.0224	-
0.0291	4200	0.0223	-
0.0298	4300	0.0217	-
0.0305	4400	0.0212	-
0.0312	4500	0.0219	-
0.0319	4600	0.0223	-
0.0326	4700	0.0209	-
0.0333	4800	0.0217	-
0.0340	4900	0.0212	-
0.0347	5000	0.0207	-
0.0354	5100	0.0208	-
0.0360	5200	0.0209	-
0.0367	5300	0.0209	-
0.0374	5400	0.021	-
0.0381	5500	0.0207	-
0.0388	5600	0.0207	-
0.0395	5700	0.0204	-
0.0402	5800	0.0204	-
0.0409	5900	0.0199	-
0.0416	6000	0.0199	-
0.0423	6100	0.0206	-
0.0430	6200	0.0203	-
0.0437	6300	0.02	-
0.0444	6400	0.0204	-
0.0451	6500	0.0201	-
0.0457	6600	0.0198	-
0.0464	6700	0.02	-
0.0471	6800	0.0199	-
0.0478	6900	0.0195	-
0.0485	7000	0.0199	-
0.0492	7100	0.0193	-
0.0499	7200	0.0193	-
0.0506	7300	0.02	-
0.0513	7400	0.019	-
0.0520	7500	0.0189	-
0.0527	7600	0.0194	-
0.0534	7700	0.0193	-
0.0541	7800	0.0194	-
0.0548	7900	0.0194	-
0.0555	8000	0.0191	-
0.0561	8100	0.0193	-
0.0568	8200	0.019	-
0.0575	8300	0.0193	-
0.0582	8400	0.0194	-
0.0589	8500	0.0188	-
0.0596	8600	0.0189	-
0.0603	8700	0.0191	-
0.0610	8800	0.0191	-
0.0617	8900	0.0182	-
0.0624	9000	0.018	-
0.0631	9100	0.0186	-
0.0638	9200	0.0187	-
0.0645	9300	0.0189	-
0.0652	9400	0.0186	-
0.0658	9500	0.0188	-
0.0665	9600	0.018	-
0.0672	9700	0.0181	-
0.0679	9800	0.0185	-
0.0686	9900	0.0187	-
0.0693	10000	0.0183	-
0.0700	10100	0.0189	-
0.0707	10200	0.0181	-
0.0714	10300	0.0185	-
0.0721	10400	0.019	-
0.0728	10500	0.0181	-
0.0735	10600	0.0179	-
0.0742	10700	0.0181	-
0.0749	10800	0.0188	-
0.0756	10900	0.0178	-
0.0762	11000	0.018	-
0.0769	11100	0.0181	-
0.0776	11200	0.0184	-
0.0783	11300	0.018	-
0.0790	11400	0.0183	-
0.0797	11500	0.0181	-
0.0804	11600	0.0177	-
0.0811	11700	0.0181	-
0.0818	11800	0.0174	-
0.0825	11900	0.0182	-
0.0832	12000	0.0182	-
0.0839	12100	0.0174	-
0.0846	12200	0.0175	-
0.0853	12300	0.0179	-
0.0859	12400	0.0176	-
0.0866	12500	0.0175	-
0.0873	12600	0.0179	-
0.0880	12700	0.0177	-
0.0887	12800	0.0176	-
0.0894	12900	0.0179	-
0.0901	13000	0.0171	-
0.0908	13100	0.0177	-
0.0915	13200	0.0176	-
0.0922	13300	0.0177	-
0.0929	13400	0.0175	-
0.0936	13500	0.0176	-
0.0943	13600	0.017	-
0.0950	13700	0.018	-
0.0957	13800	0.0176	-
0.0963	13900	0.0177	-
0.0970	14000	0.0178	-
0.0977	14100	0.0175	-
0.0984	14200	0.0178	-
0.0991	14300	0.0177	-
0.0998	14400	0.0178	-
0.1005	14500	0.0174	-
0.1012	14600	0.0175	-
0.1019	14700	0.0175	-
0.1026	14800	0.0175	-
0.1033	14900	0.0174	-
0.1040	15000	0.0175	-
0.1047	15100	0.0174	-
0.1054	15200	0.0173	-
0.1061	15300	0.0174	-
0.1067	15400	0.0173	-
0.1074	15500	0.0176	-
0.1081	15600	0.0174	-
0.1088	15700	0.0173	-
0.1095	15800	0.0175	-
0.1102	15900	0.0172	-
0.1109	16000	0.0171	-
0.1116	16100	0.0173	-
0.1123	16200	0.0177	-
0.1130	16300	0.017	-
0.1137	16400	0.0174	-
0.1144	16500	0.0174	-
0.1151	16600	0.0174	-
0.1158	16700	0.0174	-
0.1164	16800	0.0172	-
0.1171	16900	0.0175	-
0.1178	17000	0.0173	-
0.1185	17100	0.017	-
0.1192	17200	0.017	-
0.1199	17300	0.0169	-
0.1206	17400	0.0175	-
0.1213	17500	0.0167	-
0.1220	17600	0.0169	-
0.1227	17700	0.0178	-
0.1234	17800	0.0168	-
0.1241	17900	0.0173	-
0.1248	18000	0.0172	-
0.1255	18100	0.0174	-
0.1262	18200	0.0173	-
0.1268	18300	0.0167	-
0.1275	18400	0.0175	-
0.1282	18500	0.0169	-
0.1289	18600	0.0171	-
0.1296	18700	0.0168	-
0.1303	18800	0.0174	-
0.1310	18900	0.017	-
0.1317	19000	0.0174	-
0.1324	19100	0.0169	-
0.1331	19200	0.0169	-
0.1338	19300	0.0175	-
0.1345	19400	0.0168	-
0.1352	19500	0.0173	-
0.1359	19600	0.0169	-
0.1365	19700	0.0175	-
0.1372	19800	0.0171	-
0.1379	19900	0.0169	-
0.1386	20000	0.0165	-
0.1393	20100	0.017	-
0.1400	20200	0.0168	-
0.1407	20300	0.017	-
0.1414	20400	0.0166	-
0.1421	20500	0.017	-
0.1428	20600	0.0169	-
0.1435	20700	0.0166	-
0.1442	20800	0.0165	-
0.1449	20900	0.0166	-
0.1456	21000	0.017	-
0.1463	21100	0.0171	-
0.1469	21200	0.0165	-
0.1476	21300	0.0169	-
0.1483	21400	0.0168	-
0.1490	21500	0.0167	-
0.1497	21600	0.0171	-
0.1504	21700	0.0168	-
0.1511	21800	0.0167	-
0.1518	21900	0.0172	-
0.1525	22000	0.0166	-
0.1532	22100	0.017	-
0.1539	22200	0.0165	-
0.1546	22300	0.0168	-
0.1553	22400	0.017	-
0.1560	22500	0.0167	-
0.1567	22600	0.0169	-
0.1573	22700	0.0174	-
0.1580	22800	0.0169	-
0.1587	22900	0.0168	-
0.1594	23000	0.0169	-
0.1601	23100	0.0168	-
0.1608	23200	0.0167	-
0.1615	23300	0.0167	-
0.1622	23400	0.017	-
0.1629	23500	0.0166	-
0.1636	23600	0.0164	-
0.1643	23700	0.0168	-
0.1650	23800	0.0169	-
0.1657	23900	0.0164	-
0.1664	24000	0.0164	-
0.1670	24100	0.0167	-
0.1677	24200	0.0164	-
0.1684	24300	0.0165	-
0.1691	24400	0.0167	-
0.1698	24500	0.0167	-
0.1705	24600	0.0167	-
0.1712	24700	0.0168	-
0.1719	24800	0.0168	-
0.1726	24900	0.016	-
0.1733	25000	0.0168	-
0.1740	25100	0.0167	-
0.1747	25200	0.0168	-
0.1754	25300	0.0163	-
0.1761	25400	0.0165	-
0.1768	25500	0.0164	-
0.1774	25600	0.0165	-
0.1781	25700	0.0164	-
0.1788	25800	0.0167	-
0.1795	25900	0.0162	-
0.1802	26000	0.0162	-
0.1809	26100	0.0171	-
0.1816	26200	0.0168	-
0.1823	26300	0.0167	-
0.1830	26400	0.0169	-
0.1837	26500	0.0171	-
0.1844	26600	0.0163	-
0.1851	26700	0.0164	-
0.1858	26800	0.0164	-
0.1865	26900	0.0165	-
0.1871	27000	0.0165	-
0.1878	27100	0.0163	-
0.1885	27200	0.016	-
0.1892	27300	0.0166	-
0.1899	27400	0.0162	-
0.1906	27500	0.0162	-
0.1913	27600	0.0162	-
0.1920	27700	0.016	-
0.1927	27800	0.0162	-
0.1934	27900	0.0162	-
0.1941	28000	0.0165	-
0.1948	28100	0.0167	-
0.1955	28200	0.0163	-
0.1962	28300	0.016	-
0.1969	28400	0.0163	-
0.1975	28500	0.0167	-
0.1982	28600	0.0164	-
0.1989	28700	0.0163	-
0.1996	28800	0.0167	-
0.2	28854	-	0.0116
0.2003	28900	0.0169	-
0.2010	29000	0.0165	-
0.2017	29100	0.0165	-
0.2024	29200	0.0161	-
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0.9011	130000	0.0155	-
0.9018	130100	0.0159	-
0.9025	130200	0.0153	-
0.9032	130300	0.0156	-
0.9039	130400	0.016	-
0.9046	130500	0.0161	-
0.9052	130600	0.0154	-
0.9059	130700	0.0159	-
0.9066	130800	0.0157	-
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0.9080	131000	0.0156	-
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0.9094	131200	0.0158	-
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0.9115	131500	0.0163	-
0.9122	131600	0.0158	-
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0.9150	132000	0.0159	-
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0.9163	132200	0.0158	-
0.9170	132300	0.016	-
0.9177	132400	0.0153	-
0.9184	132500	0.0154	-
0.9191	132600	0.0157	-
0.9198	132700	0.016	-
0.9205	132800	0.0159	-
0.9212	132900	0.0159	-
0.9219	133000	0.0159	-
0.9226	133100	0.0155	-
0.9233	133200	0.0158	-
0.9240	133300	0.0161	-
0.9247	133400	0.0155	-
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0.9260	133600	0.0157	-
0.9267	133700	0.0158	-
0.9274	133800	0.0156	-
0.9281	133900	0.0161	-
0.9288	134000	0.0157	-
0.9295	134100	0.0159	-
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0.9309	134300	0.0153	-
0.9316	134400	0.0153	-
0.9323	134500	0.0156	-
0.9330	134600	0.0156	-
0.9337	134700	0.0159	-
0.9344	134800	0.0157	-
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0.9364	135100	0.0156	-
0.9371	135200	0.016	-
0.9378	135300	0.0157	-
0.9385	135400	0.016	-
0.9392	135500	0.0157	-
0.9399	135600	0.0155	-
0.9406	135700	0.0154	-
0.9413	135800	0.0157	-
0.9420	135900	0.016	-
0.9427	136000	0.0157	-
0.9434	136100	0.0158	-
0.9441	136200	0.0161	-
0.9448	136300	0.016	-
0.9454	136400	0.0155	-
0.9461	136500	0.0157	-
0.9468	136600	0.0155	-
0.9475	136700	0.0157	-
0.9482	136800	0.0154	-
0.9489	136900	0.0158	-
0.9496	137000	0.0158	-
0.9503	137100	0.0157	-
0.9510	137200	0.0156	-
0.9517	137300	0.0154	-
0.9524	137400	0.016	-
0.9531	137500	0.0159	-
0.9538	137600	0.0162	-
0.9545	137700	0.016	-
0.9552	137800	0.016	-
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0.9565	138000	0.0156	-
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0.9579	138200	0.0154	-
0.9586	138300	0.0159	-
0.9593	138400	0.0158	-
0.9600	138500	0.0154	-
0.9607	138600	0.0159	-
0.9614	138700	0.0157	-
0.9621	138800	0.0157	-
0.9628	138900	0.0154	-
0.9635	139000	0.0159	-
0.9642	139100	0.0157	-
0.9649	139200	0.0155	-
0.9656	139300	0.0154	-
0.9662	139400	0.0163	-
0.9669	139500	0.0156	-
0.9676	139600	0.0155	-
0.9683	139700	0.0156	-
0.9690	139800	0.0158	-
0.9697	139900	0.0157	-
0.9704	140000	0.0154	-
0.9711	140100	0.0156	-
0.9718	140200	0.0154	-
0.9725	140300	0.0155	-
0.9732	140400	0.0153	-
0.9739	140500	0.0156	-
0.9746	140600	0.0153	-
0.9753	140700	0.0157	-
0.9759	140800	0.0155	-
0.9766	140900	0.0156	-
0.9773	141000	0.0161	-
0.9780	141100	0.0158	-
0.9787	141200	0.0162	-
0.9794	141300	0.0166	-
0.9801	141400	0.016	-
0.9808	141500	0.0156	-
0.9815	141600	0.0153	-
0.9822	141700	0.0156	-
0.9829	141800	0.0161	-
0.9836	141900	0.0157	-
0.9843	142000	0.0157	-
0.9850	142100	0.0156	-
0.9857	142200	0.0159	-
0.9863	142300	0.0157	-
0.9870	142400	0.0164	-
0.9877	142500	0.0161	-
0.9884	142600	0.0157	-
0.9891	142700	0.0158	-
0.9898	142800	0.0155	-
0.9905	142900	0.0155	-
0.9912	143000	0.0155	-
0.9919	143100	0.0157	-
0.9926	143200	0.0153	-
0.9933	143300	0.0154	-
0.9940	143400	0.0154	-
0.9947	143500	0.0157	-
0.9954	143600	0.0159	-
0.9960	143700	0.0159	-
0.9967	143800	0.0157	-
0.9974	143900	0.0159	-
0.9981	144000	0.0159	-
0.9988	144100	0.0159	-
0.9995	144200	0.0158	-
1.0	144270	-	0.0110

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-v0.1

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-v0.1