NohTow/ModernBERT-base-DPR-fullneg-gte-0.0002

SentenceTransformer

This is a sentence-transformers model trained on the bge-full-data dataset. 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • bge-full-data

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': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("NohTow/ModernBERT-base-DPR-fullneg-gte-0.0002")
# Run inference
sentences = [
    'Who won 23 World Rally Championships, two in particular with the Lancia Delta Group A rally car?',
    "Lancia Delta Group A The Lancia Delta Group A is a Group A rally car built for the Martini Lancia by Lancia to compete in the World Rally Championship. It is based upon the Lancia Delta road car and replaced the Lancia Delta S4. The car was introduced for the 1987 World Rally Championship season and dominated the World Rally Championship, scoring 46 WRC victories overall and winning the constructors' championship a record six times in a row from 1987 to 1992, in addition to drivers' championship titles for Juha Kankkunen (1987 and 1991) and Miki Biasion (1988 and 1989), making Lancia the most successful marque in the history of the WRC and the Delta the most successful car.",
    'Luis Moya Luis Rodríguez Moya, better known as Luis Moya (born 23 September 1960 in La Coruña, Spain) is a now-retired rally co-driver, synonymous with driver Carlos Sainz. He is the third most successful co-driver in the history of the World Rally Championship (WRC), after Daniel Elena and Timo Rautiainen',
]
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]

Evaluation

Metrics

Information Retrieval

• Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
• Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.22	0.7	0.88	0.46	0.82	0.34	0.36	0.46	0.94	0.48	0.26	0.34	0.4898
cosine_accuracy@3	0.52	0.74	0.96	0.62	0.94	0.6	0.5	0.66	0.98	0.66	0.64	0.48	0.8367
cosine_accuracy@5	0.6	0.8	1.0	0.68	0.94	0.72	0.56	0.7	0.98	0.74	0.8	0.54	0.898
cosine_accuracy@10	0.64	0.86	1.0	0.74	0.96	0.82	0.62	0.78	1.0	0.86	0.9	0.6	0.9796
cosine_precision@1	0.22	0.7	0.88	0.46	0.82	0.34	0.36	0.46	0.94	0.48	0.26	0.34	0.4898
cosine_precision@3	0.2067	0.48	0.3333	0.2867	0.3867	0.2	0.2933	0.22	0.4067	0.3333	0.2133	0.18	0.5034
cosine_precision@5	0.144	0.432	0.208	0.224	0.248	0.144	0.296	0.144	0.252	0.276	0.16	0.128	0.4653
cosine_precision@10	0.084	0.376	0.108	0.134	0.132	0.082	0.22	0.084	0.136	0.202	0.09	0.07	0.3612
cosine_recall@1	0.0883	0.0726	0.8267	0.2445	0.41	0.34	0.0156	0.45	0.8173	0.1017	0.26	0.305	0.0355
cosine_recall@3	0.2667	0.1134	0.9233	0.4038	0.58	0.6	0.0349	0.61	0.9453	0.2067	0.64	0.47	0.1075
cosine_recall@5	0.3083	0.1586	0.9533	0.489	0.62	0.72	0.0641	0.66	0.956	0.2847	0.8	0.54	0.1652
cosine_recall@10	0.3567	0.2345	0.9733	0.5964	0.66	0.82	0.0797	0.75	0.9933	0.4157	0.9	0.6	0.243
cosine_ndcg@10	0.284	0.4733	0.9203	0.4901	0.67	0.5747	0.2547	0.6061	0.9594	0.3972	0.5856	0.4572	0.418
cosine_mrr@10	0.3747	0.7389	0.9267	0.5513	0.8795	0.4967	0.4444	0.5691	0.9625	0.5928	0.4839	0.4177	0.6742
cosine_map@100	0.2232	0.3348	0.8908	0.4201	0.5984	0.505	0.0923	0.5645	0.9423	0.3043	0.4893	0.4156	0.308

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.5192
cosine_accuracy@3	0.7028
cosine_accuracy@5	0.766
cosine_accuracy@10	0.8277
cosine_precision@1	0.5192
cosine_precision@3	0.311
cosine_precision@5	0.2401
cosine_precision@10	0.1599
cosine_recall@1	0.3052
cosine_recall@3	0.454
cosine_recall@5	0.5169
cosine_recall@10	0.5864
cosine_ndcg@10	0.5454
cosine_mrr@10	0.624
cosine_map@100	0.4684

Training Details

Training Dataset

bge-full-data

• Dataset: bge-full-data at 78f5c99
• Size: 1,770,649 training samples
• Columns: anchor, positive, negative_0, negative_1, negative_2, negative_3, and negative_4
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative_0	negative_1	negative_2	negative_3	negative_4
  type	string	string	string	string	string	string	string
  details	min: 4 tokens mean: 20.15 tokens max: 512 tokens	min: 3 tokens mean: 173.18 tokens max: 512 tokens	min: 5 tokens mean: 170.06 tokens max: 512 tokens	min: 4 tokens mean: 167.88 tokens max: 512 tokens	min: 6 tokens mean: 167.95 tokens max: 512 tokens	min: 6 tokens mean: 166.32 tokens max: 512 tokens	min: 5 tokens mean: 167.63 tokens max: 512 tokens

• Samples:

  anchor	positive	negative_0	negative_1	negative_2	negative_3	negative_4
  What happens if you eat raw chicken?	What are the dangers of eating raw chicken?	Does all raw chicken have salmonella?	How safe is to eat chicken during pregnancy?	What meats are safe to eat raw?	What are some natural obligations of chicken?	Is it safe to eat raw egg?
  how long does it take for a wren egg to hatch	How often does a mother Wren sit on her nest? I don't know for sure about how long Wrens usually spend on the nest at one sitting.. (Sorry couldn't resist the joke) However, the eggs usually hatch in 13-18 days, so if there were no hatchlings when that time elapsed, then you'd know for sure that she hadn't been behaving normally.	- When you are trying to hatch Tennessee red quail eggs, it will take approximately 23 days. You should perform lock down on the egg at 20 days. This is a period of time whe … n there should be no disturbances because hatching is likely to begin.urkey eggs usually take 21 to 28 days to hatch depending on what they are incubated in like an incubator or by a hen.	How long does it take an egg to hatch? For an average Eagle it would have a time for about 32-36 days, but the average time for an Eagle egg to hatch is about 35 days. 28 people found this useful.	- When you are trying to hatch Tennessee red quail eggs, it will take approximately 23 days. You should perform lock down on the egg at 20 days. This is a period of time whe … n there should be no disturbances because hatching is likely to begin.urkey eggs usually take 21 to 28 days to hatch depending on what they are incubated in like an incubator or by a hen. It also depends on how fertile it is and how it is cared … for.	- Actually this may vary depending on the kind of bird finch, the eggs hatch in between 12 - 16 days or 3 weeks.The nestlings fledge in 18 - 19 days.ctually this may vary depending on the kind of bird finch, the eggs hatch in between 12 - 16 days or 3 weeks.	- Welcome, and thanks for visiting the virtual home of the Whitestown Fire Department. Whether you’re stopping by to obtain information on our department, place a comment, track our progress and events, or just looking at the great pictures of our top notch personnel in action, we hope that you find what you’re after. Please feel free to provide feedback or contact us for any questions you may have.
  can you have schizophrenia and bipolar	Can you have both bipolar disorder and schizophrenia? Health Mental Health Can you have both bipolar disorder and schizophrenia? I'm 19 and was diagnosed with Bipolar Disorder almost 2 years ago. I also have some symptoms of schizophrenia such as auditory hallucinations and occasional visual ones as well and occasional paranoia. Ok the paranoia is pretty frequent. So yea, Can you have both of them? I know some of the symptoms can be... show more Follow 6 answers Answers Relevance Rating Newest Oldest Best Answer: yes you can, but some people with bipolar disorder have hallucinations and delusions from the bipolar disorder. only a psychiatrist could diagnose you i guess. Source (s):er nurse Zach · 9 years ago0 0 Comment Asker's rating Yes, one can have both bipolar disorder and schizophrenia, as the cause is one and the same - a spirit (ghost). Not only are the mood swings imparted by the associated spirit, but the alleged hallucinations are as well. The voices that those diagnosed as h...	Dual Diagnosis: Understanding Sex Addiction With Bipolar Disorder Dual Diagnosis: Understanding Sex Addiction With Bipolar Disorder February 5, 2015 Dual Diagnosis Bipolar disorder manifests itself in one college student’s “need” to sexually expose himself on campus. Marty was diagnosed with bipolar 1 disorder in the spring of his junior year in college. The symptoms had emerged during adolescence, but it wasn’t until a particularly startling manic episode that Marty’s doctor knew his depression was more than unipolar (i.e., clinical depression by itself). The gifted art student had painted his naked body in elaborate geometric patterns and shown up at the fountain in front of his university’s grand administrative building during the middle of a sunny afternoon. He proceeded to dramatically quote Michel Foucault’s Madness and Civilization, even as he was carried away by campus security. The combination of SSRIs and mood stabilizers prescribed to Marty for the treatment of bipolar disor...	Understanding Schizoaffective Disorder Medication Understanding Schizoaffective Disorder Medication Because schizoaffective disorder has symptoms of both psychosis and a mood disorder, ✱ doctors often prescribe different medicines to treat different symptoms of the condition. For example, they may prescribe: An antipsychotic, which helps symptoms like delusions and hallucinations A mood-stabilizing medicine, which can help level out “highs” and “lows”An antidepressant, which can help feelings of sadness, hopelessness, and difficulty with sleep and concentration One medicine for schizoaffective disorder's symptoms INVEGA SUSTENNA ® treats the symptoms of schizoaffective disorder (psychosis and mood), so it may be possible for you to manage symptoms with one medicine if your doctor feels it’s right for you. And that means one less pill to think about every day. Approved for the treatment of schizophrenia and schizoaffective disorder.✱ Please discuss your symptoms with your healthcare pro...	Paranoia and schizophrenia: What you need to know Newsletter MNT - Hourly Medical News Since 2003Search Log in Newsletter MNT - Hourly Medical News Since 2003Search Login Paranoia and schizophrenia: What you need to know Last updated Thu 25 May 2017By Yvette Brazier Reviewed by Timothy J. Legg, Ph D, CRNPOverview Symptoms Causes Diagnosis Treatment Complications A person who has a condition on the schizophrenia spectrum may experience delusions and what is commonly known as paranoia. These delusions may give rise to fears that others are plotting against the individual. Everyone can have a paranoid thought from time to time. On a rough day, we may find ourselves saying "Oh boy, the whole world is out to get me!" But we recognize that this is not the case. People with paranoia often have an extensive network of paranoid thoughts and ideas. This can result in a disproportionate amount of time spent thinking up ways for the individual to protect themselves from their perceived persecutors...	Same Genes Suspected in Both Depression and Bipolar Illness Same Genes Suspected in Both Depression and Bipolar Illness Increased Risk May Stem From Variation in Gene On/Off Switch January 28, 2010 • Science Update Protein produced by PBRM1 gene Researchers, for the first time, have pinpointed a genetic hotspot that confers risk for both bipolar disorder and depression. People with either of these mood disorders were significantly more likely to have risk versions of genes at this site than healthy controls. One of the genes, which codes for part of a cell's machinery that tells genes when to turn on and off, was also found to be over-expressed in the executive hub of bipolar patients' brains, making it a prime suspect. The results add to mounting evidence that major mental disorders overlap at the molecular level. "People who carry the risk versions may differ in some dimension of brain development that may increase risk for mood disorders later in life," explained Francis Mc Mahon, M...	Schizophrenia Definition and Characteristics Schizophrenia Schizophrenia Definition and Characteristics Symptoms, Treatments and Risk Factors By Marcia Purse

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 2048
• per_device_eval_batch_size: 2048
• learning_rate: 0.0002
• num_train_epochs: 2
• warmup_ratio: 0.05
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 2048
• per_device_eval_batch_size: 2048
• 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: 0.0002
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 5
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0185	2	8.9197	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0370	4	8.4814	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0556	6	6.6919	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0741	8	5.2493	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0926	10	4.2792	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1111	12	3.4554	0.2385	0.3867	0.7209	0.3194	0.5207	0.4438	0.1702	0.3732	0.8791	0.2758	0.4377	0.4026	0.4623	0.4331
0.1296	14	3.0437	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1481	16	2.6133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1667	18	2.3395	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1852	20	2.1826	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2037	22	2.0498	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2222	24	1.9743	0.2706	0.4493	0.8104	0.4201	0.6036	0.5542	0.2249	0.5859	0.9221	0.3091	0.5671	0.5562	0.4864	0.5200
0.2407	26	1.9111	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2593	28	1.8534	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2778	30	1.8137	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2963	32	1.7587	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3148	34	1.7124	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3333	36	1.6841	0.2945	0.4652	0.8333	0.4352	0.6189	0.5619	0.2512	0.5977	0.9403	0.3322	0.5502	0.5778	0.4596	0.5321
0.3519	38	1.6765	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3704	40	1.6314	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3889	42	1.5989	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4074	44	1.592	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4259	46	1.572	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4444	48	1.5525	0.3045	0.4626	0.8526	0.4507	0.6275	0.5617	0.2575	0.5676	0.9406	0.3661	0.5666	0.5693	0.4231	0.5346
0.4630	50	1.51	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4815	52	1.5156	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5	54	1.5076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5185	56	1.4781	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5370	58	1.4833	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5556	60	1.4576	0.3042	0.4727	0.8456	0.4578	0.6338	0.5599	0.2513	0.5883	0.9370	0.3792	0.5656	0.5229	0.4431	0.5355
0.5741	62	1.4402	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5926	64	1.438	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6111	66	1.4504	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6296	68	1.4142	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6481	70	1.4141	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6667	72	1.3917	0.3225	0.4697	0.8632	0.4529	0.6474	0.5575	0.2341	0.5942	0.9464	0.3846	0.5467	0.4924	0.4124	0.5326
0.6852	74	1.4108	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7037	76	1.4	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7222	78	1.385	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7407	80	1.3946	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7593	82	1.3762	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7778	84	1.3606	0.3325	0.4747	0.8730	0.4891	0.6511	0.5941	0.2530	0.5835	0.9452	0.3776	0.5490	0.4680	0.4447	0.5412
0.7963	86	1.3615	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8148	88	1.3811	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8333	90	1.3462	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8519	92	1.3617	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8704	94	1.3345	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8889	96	1.3291	0.3249	0.4780	0.8791	0.4925	0.6518	0.6018	0.2678	0.5981	0.9451	0.3799	0.5474	0.4423	0.4340	0.5418
0.9074	98	1.3253	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9259	100	1.3375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9444	102	1.3177	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9630	104	1.3318	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9815	106	1.297	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0093	108	1.3128	0.3211	0.4761	0.8869	0.4904	0.6531	0.5906	0.2660	0.6035	0.9473	0.3810	0.5749	0.4420	0.4286	0.5432
1.0278	110	1.3088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0463	112	1.3071	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0648	114	1.2936	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0833	116	1.2839	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1019	118	1.2693	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1204	120	1.291	0.3022	0.4793	0.8822	0.5117	0.6691	0.5708	0.2637	0.6140	0.9521	0.3913	0.5773	0.4487	0.4281	0.5454
1.1389	122	1.2636	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1574	124	1.2427	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1759	126	1.2167	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1944	128	1.202	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2130	130	1.1931	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2315	132	1.178	0.2842	0.4731	0.8755	0.5114	0.6814	0.5611	0.2731	0.6122	0.9477	0.3926	0.5723	0.4647	0.4441	0.5457
1.25	134	1.1955	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2685	136	1.18	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2870	138	1.1771	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3056	140	1.173	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3241	142	1.141	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3426	144	1.1531	0.2816	0.4822	0.9067	0.5164	0.6609	0.5758	0.2713	0.6295	0.9596	0.4018	0.5862	0.4615	0.4309	0.5511
1.3611	146	1.1608	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3796	148	1.1489	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3981	150	1.1531	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4167	152	1.1391	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4352	154	1.1405	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4537	156	1.1336	0.3180	0.4810	0.8891	0.5077	0.6655	0.5609	0.2797	0.5979	0.9557	0.3988	0.6011	0.5093	0.4176	0.5525
1.4722	158	1.1165	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4907	160	1.1316	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5093	162	1.1328	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5278	164	1.1229	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5463	166	1.1312	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5648	168	1.1112	0.2801	0.4865	0.9104	0.5040	0.6631	0.5666	0.2847	0.6059	0.9599	0.4003	0.5906	0.4927	0.4312	0.5520
1.5833	170	1.1304	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6019	172	1.1257	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6204	174	1.139	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6389	176	1.1116	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6574	178	1.1161	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6759	180	1.1024	0.2991	0.4822	0.9009	0.4886	0.6652	0.5659	0.2577	0.6147	0.9597	0.4051	0.5747	0.4585	0.4207	0.5456
1.6944	182	1.1239	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7130	184	1.1266	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7315	186	1.1154	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.75	188	1.1382	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7685	190	1.102	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7870	192	1.1046	0.3107	0.4764	0.9040	0.4828	0.6680	0.5747	0.2625	0.5969	0.9567	0.3948	0.5801	0.4641	0.4313	0.5464
1.8056	194	1.1241	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8241	196	1.1266	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8426	198	1.1257	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8611	200	1.1148	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8796	202	1.1133	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8981	204	1.1149	0.2840	0.4733	0.9203	0.4901	0.6700	0.5747	0.2547	0.6061	0.9594	0.3972	0.5856	0.4572	0.4180	0.5454
1.9167	206	1.1122	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9352	208	1.1259	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9537	210	1.1215	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9722	212	1.1047	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9907	214	1.1166	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0.dev0
• PyTorch: 2.6.0.dev20241112+cu121
• Accelerate: 1.2.1
• Datasets: 2.21.0
• 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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}