VTorr/pchem-nomic_v1.5

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. 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-v1.5
• Maximum Sequence Length: 512 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': 512, 'do_lower_case': False, 'architecture': 'NomicBertModel'})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'AKOS034228228',
    'z358268078',
    'schembl4017317',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.7901,  0.0481],
#         [ 0.7901,  1.0000, -0.0067],
#         [ 0.0481, -0.0067,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 53,296,992 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 3 tokens mean: 17.0 tokens max: 99 tokens	min: 4 tokens mean: 22.4 tokens max: 175 tokens

• Samples:

  sentence_0	sentence_1
  1352219-33-0	1-(2,3-dimethylphenyl)-2,2,3,3,3-pentafluoro-propan-1-one
  104504-45-2	schembl44097
  Oprea1_769337	(2,5-dichlorophenyl)-n-[2-(2-chloro-4-fluorophenyl)benzoxazol-5-yl]carboxamide

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 2048,
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 96
• per_device_eval_batch_size: 96
• num_train_epochs: 10
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 96
• per_device_eval_batch_size: 96
• 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: 10
• 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
• 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: False
• 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: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
6.6753	3706000	0.6616
6.6762	3706500	0.6588
6.6771	3707000	0.6588
6.6781	3707500	0.6611
6.6790	3708000	0.6586
6.6799	3708500	0.6637
6.6808	3709000	0.6518
6.6817	3709500	0.6631
6.6826	3710000	0.6629
6.6835	3710500	0.6726
6.6844	3711000	0.6635
6.6853	3711500	0.663
6.6862	3712000	0.6518
6.6871	3712500	0.6582
6.6880	3713000	0.6753
6.6889	3713500	0.6627
6.6898	3714000	0.6632
6.6907	3714500	0.6577
6.6916	3715000	0.6633
6.6925	3715500	0.6519
6.6934	3716000	0.662
6.6943	3716500	0.674
6.6952	3717000	0.6674
6.6961	3717500	0.6537
6.6970	3718000	0.6648
6.6979	3718500	0.6598
6.6988	3719000	0.6664
6.6997	3719500	0.6589
6.7006	3720000	0.6709
6.7015	3720500	0.6592
6.7024	3721000	0.659
6.7033	3721500	0.6694
6.7042	3722000	0.6599
6.7051	3722500	0.6577
6.7060	3723000	0.6697
6.7069	3723500	0.6712
6.7078	3724000	0.6726
6.7087	3724500	0.6626
6.7096	3725000	0.6733
6.7105	3725500	0.6643
6.7114	3726000	0.6669
6.7123	3726500	0.6691
6.7132	3727000	0.6633
6.7141	3727500	0.6681
6.7150	3728000	0.6686
6.7159	3728500	0.672
6.7168	3729000	0.6708
6.7177	3729500	0.6588
6.7186	3730000	0.6734
6.7195	3730500	0.6722
6.7204	3731000	0.6742
6.7213	3731500	0.6729
6.7222	3732000	0.6744
6.7231	3732500	0.6722
6.7240	3733000	0.6704
6.7249	3733500	0.6706
6.7258	3734000	0.6668
6.7267	3734500	0.6567
6.7276	3735000	0.6664
6.7285	3735500	0.6758
6.7294	3736000	0.6729
6.7303	3736500	0.6641
6.7312	3737000	0.6729
6.7321	3737500	0.6708
6.7330	3738000	0.6736
6.7339	3738500	0.6664
6.7348	3739000	0.6581
6.7357	3739500	0.6603
6.7366	3740000	0.6589
6.7375	3740500	0.661
6.7384	3741000	0.6717
6.7393	3741500	0.6693
6.7402	3742000	0.6623
6.7411	3742500	0.6735
6.7420	3743000	0.6691
6.7429	3743500	0.6686
6.7438	3744000	0.675
6.7447	3744500	0.6693
6.7456	3745000	0.6792
6.7465	3745500	0.6812
6.7474	3746000	0.6665
6.7483	3746500	0.666
6.7492	3747000	0.6746
6.7501	3747500	0.6769
6.7510	3748000	0.6675
6.7519	3748500	0.6762
6.7528	3749000	0.6722
6.7537	3749500	0.6689
6.7546	3750000	0.6621
6.7555	3750500	0.6636
6.7564	3751000	0.6639
6.7573	3751500	0.6755
6.7582	3752000	0.6729
6.7591	3752500	0.6701
6.7600	3753000	0.6689
6.7609	3753500	0.6746
6.7618	3754000	0.6759
6.7627	3754500	0.682
6.7636	3755000	0.6696
6.7645	3755500	0.6824
6.7654	3756000	0.6727
6.7663	3756500	0.6735
6.7672	3757000	0.6763
6.7681	3757500	0.6758
6.7690	3758000	0.6741
6.7699	3758500	0.6689
6.7708	3759000	0.6758
6.7717	3759500	0.6836
6.7726	3760000	0.6807
6.7735	3760500	0.6745
6.7744	3761000	0.6818
6.7753	3761500	0.6769
6.7762	3762000	0.6632
6.7771	3762500	0.6779
6.7780	3763000	0.6792
6.7789	3763500	0.678
6.7798	3764000	0.6771
6.7807	3764500	0.6747
6.7816	3765000	0.6716
6.7825	3765500	0.6761
6.7834	3766000	0.6823
6.7843	3766500	0.682
6.7852	3767000	0.6784
6.7861	3767500	0.6809
6.7870	3768000	0.671
6.7879	3768500	0.677
6.7888	3769000	0.6837
6.7897	3769500	0.6839
6.7906	3770000	0.6773
6.7915	3770500	0.68
6.7924	3771000	0.6678
6.7933	3771500	0.6729
6.7942	3772000	0.6792
6.7951	3772500	0.6779
6.7960	3773000	0.6759
6.7969	3773500	0.663
6.7978	3774000	0.6833
6.7987	3774500	0.671
6.7996	3775000	0.6754
6.8005	3775500	0.6708
6.8014	3776000	0.6698
6.8023	3776500	0.6656
6.8032	3777000	0.6713
6.8041	3777500	0.6868
6.8050	3778000	0.6755
6.8059	3778500	0.6697
6.8068	3779000	0.6801
6.8077	3779500	0.6728
6.8086	3780000	0.6906
6.8095	3780500	0.6885
6.8104	3781000	0.6777
6.8113	3781500	0.6774
6.8122	3782000	0.6637
6.8131	3782500	0.6827
6.8140	3783000	0.6741
6.8149	3783500	0.6722
6.8158	3784000	0.6755
6.8167	3784500	0.6811
6.8176	3785000	0.6777
6.8185	3785500	0.6817
6.8194	3786000	0.6673
6.8203	3786500	0.6666
6.8212	3787000	0.6823
6.8221	3787500	0.6836
6.8230	3788000	0.6724
6.8239	3788500	0.6648
6.8249	3789000	0.6755
6.8258	3789500	0.6801
6.8267	3790000	0.6806
6.8276	3790500	0.6767
6.8285	3791000	0.6742
6.8294	3791500	0.6828
6.8303	3792000	0.6859
6.8312	3792500	0.6857
6.8321	3793000	0.685
6.8330	3793500	0.6821
6.8339	3794000	0.6838
6.8348	3794500	0.6728
6.8357	3795000	0.6789
6.8366	3795500	0.6819
6.8375	3796000	0.6831
6.8384	3796500	0.6809
6.8393	3797000	0.6841
6.8402	3797500	0.6736
6.8411	3798000	0.6821
6.8420	3798500	0.6685
6.8429	3799000	0.6765
6.8438	3799500	0.6861
6.8447	3800000	0.6694
6.8456	3800500	0.6837
6.8465	3801000	0.6816
6.8474	3801500	0.6735
6.8483	3802000	0.6821
6.8492	3802500	0.6737
6.8501	3803000	0.6822
6.8510	3803500	0.6879
6.8519	3804000	0.6771
6.8528	3804500	0.6713
6.8537	3805000	0.6779
6.8546	3805500	0.6831
6.8555	3806000	0.6878
6.8564	3806500	0.6862
6.8573	3807000	0.6823
6.8582	3807500	0.6733
6.8591	3808000	0.6788
6.8600	3808500	0.679
6.8609	3809000	0.6748
6.8618	3809500	0.6827
6.8627	3810000	0.6838
6.8636	3810500	0.6763
6.8645	3811000	0.679
6.8654	3811500	0.68
6.8663	3812000	0.6925
6.8672	3812500	0.6896
6.8681	3813000	0.697
6.8690	3813500	0.686
6.8699	3814000	0.6697
6.8708	3814500	0.6853
6.8717	3815000	0.6845
6.8726	3815500	0.6828
6.8735	3816000	0.6877
6.8744	3816500	0.6789
6.8753	3817000	0.6779
6.8762	3817500	0.6845
6.8771	3818000	0.6685
6.8780	3818500	0.675
6.8789	3819000	0.6725
6.8798	3819500	0.6722
6.8807	3820000	0.6749
6.8816	3820500	0.688
6.8825	3821000	0.6884
6.8834	3821500	0.6809
6.8843	3822000	0.679
6.8852	3822500	0.683
6.8861	3823000	0.6763
6.8870	3823500	0.6825
6.8879	3824000	0.6877
6.8888	3824500	0.6885
6.8897	3825000	0.6795
6.8906	3825500	0.6836
6.8915	3826000	0.6864
6.8924	3826500	0.6817
6.8933	3827000	0.6911
6.8942	3827500	0.6836
6.8951	3828000	0.6804
6.8960	3828500	0.6726
6.8969	3829000	0.6879
6.8978	3829500	0.6714
6.8987	3830000	0.6842
6.8996	3830500	0.6863
6.9005	3831000	0.6877
6.9014	3831500	0.6866
6.9023	3832000	0.6816
6.9032	3832500	0.6837
6.9041	3833000	0.6817
6.9050	3833500	0.6829
6.9059	3834000	0.6808
6.9068	3834500	0.6863
6.9077	3835000	0.6799
6.9086	3835500	0.6871
6.9095	3836000	0.6886
6.9104	3836500	0.6886
6.9113	3837000	0.6874
6.9122	3837500	0.6841
6.9131	3838000	0.6812
6.9140	3838500	0.6907
6.9149	3839000	0.6857
6.9158	3839500	0.6832
6.9167	3840000	0.6755
6.9176	3840500	0.6814
6.9185	3841000	0.6761
6.9194	3841500	0.6752
6.9203	3842000	0.6861
6.9212	3842500	0.6913
6.9221	3843000	0.6883
6.9230	3843500	0.6808
6.9239	3844000	0.6859
6.9248	3844500	0.6784
6.9257	3845000	0.6818
6.9266	3845500	0.6904
6.9275	3846000	0.6783
6.9284	3846500	0.6934
6.9293	3847000	0.6909
6.9302	3847500	0.6843
6.9311	3848000	0.6888
6.9320	3848500	0.6694
6.9329	3849000	0.6898
6.9338	3849500	0.6908
6.9347	3850000	0.6871
6.9356	3850500	0.69
6.9365	3851000	0.7011
6.9374	3851500	0.6906
6.9383	3852000	0.6905
6.9392	3852500	0.6923
6.9401	3853000	0.69
6.9410	3853500	0.684
6.9419	3854000	0.6822
6.9428	3854500	0.68
6.9437	3855000	0.7033
6.9446	3855500	0.6866
6.9455	3856000	0.6926
6.9464	3856500	0.6704
6.9473	3857000	0.6853
6.9482	3857500	0.6876
6.9491	3858000	0.6879
6.9500	3858500	0.6792
6.9509	3859000	0.6773
6.9518	3859500	0.6779
6.9527	3860000	0.6953
6.9536	3860500	0.6836
6.9545	3861000	0.6926
6.9554	3861500	0.6873
6.9563	3862000	0.6898
6.9572	3862500	0.6835
6.9581	3863000	0.6922
6.9590	3863500	0.6898
6.9599	3864000	0.6865
6.9608	3864500	0.6817
6.9617	3865000	0.6803
6.9626	3865500	0.6792
6.9635	3866000	0.6926
6.9644	3866500	0.6864
6.9653	3867000	0.682
6.9662	3867500	0.6971
6.9671	3868000	0.69
6.9680	3868500	0.6848
6.9689	3869000	0.6835
6.9698	3869500	0.6873
6.9707	3870000	0.6823
6.9717	3870500	0.6777
6.9726	3871000	0.6782
6.9735	3871500	0.6858
6.9744	3872000	0.6861
6.9753	3872500	0.6926
6.9762	3873000	0.6943
6.9771	3873500	0.686
6.9780	3874000	0.6865
6.9789	3874500	0.6925
6.9798	3875000	0.6901
6.9807	3875500	0.6843
6.9816	3876000	0.6891
6.9825	3876500	0.691
6.9834	3877000	0.6892
6.9843	3877500	0.6821
6.9852	3878000	0.6876
6.9861	3878500	0.6923
6.9870	3879000	0.6854
6.9879	3879500	0.6927
6.9888	3880000	0.6865
6.9897	3880500	0.6954
6.9906	3881000	0.6882
6.9915	3881500	0.682
6.9924	3882000	0.6755
6.9933	3882500	0.6991
6.9942	3883000	0.69
6.9951	3883500	0.6795
6.9960	3884000	0.682
6.9969	3884500	0.6896
6.9978	3885000	0.6878
6.9987	3885500	0.6855
6.9996	3886000	0.6855
7.0005	3886500	0.6647
7.0014	3887000	0.6399
7.0023	3887500	0.6376
7.0032	3888000	0.6421
7.0041	3888500	0.6404
7.0050	3889000	0.6367
7.0059	3889500	0.6392
7.0068	3890000	0.6449
7.0077	3890500	0.6526
7.0086	3891000	0.6452
7.0095	3891500	0.6419
7.0104	3892000	0.6492
7.0113	3892500	0.6575
7.0122	3893000	0.6381
7.0131	3893500	0.6379
7.0140	3894000	0.6481
7.0149	3894500	0.6372
7.0158	3895000	0.6454
7.0167	3895500	0.6464
7.0176	3896000	0.6434
7.0185	3896500	0.6406
7.0194	3897000	0.6435
7.0203	3897500	0.6448
7.0212	3898000	0.6492
7.0221	3898500	0.6528
7.0230	3899000	0.6464
7.0239	3899500	0.6481
7.0248	3900000	0.6378
7.0257	3900500	0.6401
7.0266	3901000	0.6537
7.0275	3901500	0.6503
7.0284	3902000	0.6439
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7.5273	4179000	0.6681
7.5282	4179500	0.6745
7.5291	4180000	0.6789
7.5300	4180500	0.6715
7.5309	4181000	0.6749
7.5318	4181500	0.6694
7.5327	4182000	0.6811
7.5336	4182500	0.6661
7.5345	4183000	0.6694
7.5354	4183500	0.6747
7.5363	4184000	0.6787
7.5372	4184500	0.6844
7.5381	4185000	0.6658
7.5390	4185500	0.6817
7.5399	4186000	0.6699
7.5408	4186500	0.6737
7.5417	4187000	0.6774
7.5426	4187500	0.6758
7.5435	4188000	0.6713
7.5444	4188500	0.6737
7.5453	4189000	0.6678
7.5462	4189500	0.6711
7.5471	4190000	0.6712
7.5480	4190500	0.6796
7.5489	4191000	0.6652
7.5498	4191500	0.6701
7.5507	4192000	0.6773
7.5516	4192500	0.669
7.5525	4193000	0.6698
7.5534	4193500	0.6695
7.5543	4194000	0.6716
7.5552	4194500	0.6668
7.5561	4195000	0.6864
7.5570	4195500	0.6782
7.5579	4196000	0.6769
7.5589	4196500	0.6723
7.5598	4197000	0.6878
7.5607	4197500	0.6663
7.5616	4198000	0.6751
7.5625	4198500	0.6691
7.5634	4199000	0.676
7.5643	4199500	0.6728
7.5652	4200000	0.6807
7.5661	4200500	0.6757
7.5670	4201000	0.6649
7.5679	4201500	0.6716
7.5688	4202000	0.6751
7.5697	4202500	0.67
7.5706	4203000	0.666
7.5715	4203500	0.6701
7.5724	4204000	0.6844
7.5733	4204500	0.6802
7.5742	4205000	0.673
7.5751	4205500	0.6747
7.5760	4206000	0.6775
7.5769	4206500	0.6604
7.5778	4207000	0.6736
7.5787	4207500	0.6763
7.5796	4208000	0.6743
7.5805	4208500	0.6714
7.5814	4209000	0.6858
7.5823	4209500	0.6764
7.5832	4210000	0.6739
7.5841	4210500	0.6702
7.5850	4211000	0.681
7.5859	4211500	0.671
7.5868	4212000	0.6636
7.5877	4212500	0.6647
7.5886	4213000	0.6706
7.5895	4213500	0.6691
7.5904	4214000	0.6708
7.5913	4214500	0.6681
7.5922	4215000	0.6762
7.5931	4215500	0.6779
7.5940	4216000	0.6613
7.5949	4216500	0.6764
7.5958	4217000	0.6746
7.5967	4217500	0.6672
7.5976	4218000	0.6709
7.5985	4218500	0.6717
7.5994	4219000	0.6742
7.6003	4219500	0.6771
7.6012	4220000	0.6715
7.6021	4220500	0.679
7.6030	4221000	0.6783
7.6039	4221500	0.6779
7.6048	4222000	0.6832
7.6057	4222500	0.6771
7.6066	4223000	0.6786
7.6075	4223500	0.6689
7.6084	4224000	0.6772
7.6093	4224500	0.6775
7.6102	4225000	0.677
7.6111	4225500	0.6726
7.6120	4226000	0.6757
7.6129	4226500	0.6639
7.6138	4227000	0.677
7.6147	4227500	0.6711
7.6156	4228000	0.6728
7.6165	4228500	0.6697
7.6174	4229000	0.676
7.6183	4229500	0.6759
7.6192	4230000	0.674
7.6201	4230500	0.6732
7.6210	4231000	0.6756
7.6219	4231500	0.6729
7.6228	4232000	0.6713
7.6237	4232500	0.6745
7.6246	4233000	0.6751
7.6255	4233500	0.6617
7.6264	4234000	0.6714
7.6273	4234500	0.6692
7.6282	4235000	0.6751
7.6291	4235500	0.6684
7.6300	4236000	0.6708
7.6309	4236500	0.6681
7.6318	4237000	0.6701
7.6327	4237500	0.6719
7.6336	4238000	0.6703
7.6345	4238500	0.6637
7.6354	4239000	0.6736
7.6363	4239500	0.6746
7.6372	4240000	0.6727
7.6381	4240500	0.6748
7.6390	4241000	0.6857
7.6399	4241500	0.6779
7.6408	4242000	0.6714
7.6417	4242500	0.6705
7.6426	4243000	0.6737
7.6435	4243500	0.6811
7.6444	4244000	0.6691
7.6453	4244500	0.6745
7.6462	4245000	0.6752
7.6471	4245500	0.6839
7.6480	4246000	0.6749
7.6489	4246500	0.6701
7.6498	4247000	0.6728
7.6507	4247500	0.6667
7.6516	4248000	0.6666
7.6525	4248500	0.6877
7.6534	4249000	0.6755
7.6543	4249500	0.6882
7.6552	4250000	0.6709
7.6561	4250500	0.6694
7.6570	4251000	0.6878
7.6579	4251500	0.685
7.6588	4252000	0.6714
7.6597	4252500	0.6736
7.6606	4253000	0.6729
7.6615	4253500	0.6791
7.6624	4254000	0.6711
7.6633	4254500	0.6726
7.6642	4255000	0.6672
7.6651	4255500	0.6673
7.6660	4256000	0.6758
7.6669	4256500	0.6691
7.6678	4257000	0.6683
7.6687	4257500	0.6722
7.6696	4258000	0.6758
7.6705	4258500	0.6828
7.6714	4259000	0.6732
7.6723	4259500	0.6626
7.6732	4260000	0.6745
7.6741	4260500	0.6704
7.6750	4261000	0.6699
7.6759	4261500	0.6698
7.6768	4262000	0.6682
7.6777	4262500	0.6722
7.6786	4263000	0.679
7.6795	4263500	0.6751
7.6804	4264000	0.6795
7.6813	4264500	0.6842
7.6822	4265000	0.6701
7.6831	4265500	0.6731
7.6840	4266000	0.6733
7.6849	4266500	0.6775
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7.6867	4267500	0.676
7.6876	4268000	0.6741
7.6885	4268500	0.6784
7.6894	4269000	0.6671
7.6903	4269500	0.6704
7.6912	4270000	0.6676
7.6921	4270500	0.6801
7.6930	4271000	0.6637
7.6939	4271500	0.6567
7.6948	4272000	0.6729
7.6957	4272500	0.6685
7.6966	4273000	0.6838
7.6975	4273500	0.6677
7.6984	4274000	0.6729
7.6993	4274500	0.6689
7.7002	4275000	0.6735
7.7011	4275500	0.6579
7.7020	4276000	0.6848
7.7029	4276500	0.6759
7.7038	4277000	0.6794
7.7048	4277500	0.6691
7.7057	4278000	0.6701
7.7066	4278500	0.6808
7.7075	4279000	0.6726
7.7084	4279500	0.6741
7.7093	4280000	0.6726
7.7102	4280500	0.6675
7.7111	4281000	0.6746
7.7120	4281500	0.6674
7.7129	4282000	0.6664
7.7138	4282500	0.6789
7.7147	4283000	0.6711
7.7156	4283500	0.6687
7.7165	4284000	0.6672
7.7174	4284500	0.6717
7.7183	4285000	0.6777
7.7192	4285500	0.6588
7.7201	4286000	0.6806
7.7210	4286500	0.6665
7.7219	4287000	0.6763
7.7228	4287500	0.6699
7.7237	4288000	0.6698
7.7246	4288500	0.667
7.7255	4289000	0.6771
7.7264	4289500	0.6755
7.7273	4290000	0.673
7.7282	4290500	0.6696
7.7291	4291000	0.6791
7.7300	4291500	0.6759
7.7309	4292000	0.6609
7.7318	4292500	0.6839
7.7327	4293000	0.6613
7.7336	4293500	0.6747
7.7345	4294000	0.667
7.7354	4294500	0.6784
7.7363	4295000	0.6654
7.7372	4295500	0.6783
7.7381	4296000	0.6714
7.7390	4296500	0.675
7.7399	4297000	0.67
7.7408	4297500	0.6643
7.7417	4298000	0.6732
7.7426	4298500	0.6688
7.7435	4299000	0.6795
7.7444	4299500	0.6734
7.7453	4300000	0.6777
7.7462	4300500	0.6747
7.7471	4301000	0.6777
7.7480	4301500	0.6729
7.7489	4302000	0.6739
7.7498	4302500	0.6654
7.7507	4303000	0.6636
7.7516	4303500	0.6817
7.7525	4304000	0.6804
7.7534	4304500	0.6796
7.7543	4305000	0.6692
7.7552	4305500	0.6701
7.7561	4306000	0.6737
7.7570	4306500	0.679
7.7579	4307000	0.6722
7.7588	4307500	0.6793
7.7597	4308000	0.681
7.7606	4308500	0.6735
7.7615	4309000	0.6793
7.7624	4309500	0.6679
7.7633	4310000	0.6639
7.7642	4310500	0.6557
7.7651	4311000	0.6786
7.7660	4311500	0.674
7.7669	4312000	0.6731
7.7678	4312500	0.6574
7.7687	4313000	0.6725
7.7696	4313500	0.6822
7.7705	4314000	0.6761
7.7714	4314500	0.675
7.7723	4315000	0.6718
7.7732	4315500	0.6704
7.7741	4316000	0.6793
7.7750	4316500	0.6746
7.7759	4317000	0.6819
7.7768	4317500	0.6788
7.7777	4318000	0.6717
7.7786	4318500	0.6752
7.7795	4319000	0.6707
7.7804	4319500	0.6883
7.7813	4320000	0.6719
7.7822	4320500	0.6666
7.7831	4321000	0.6796
7.7840	4321500	0.6749
7.7849	4322000	0.6779
7.7858	4322500	0.6747
7.7867	4323000	0.6765
7.7876	4323500	0.672
7.7885	4324000	0.6832
7.7894	4324500	0.6719
7.7903	4325000	0.6733
7.7912	4325500	0.6712
7.7921	4326000	0.6715
7.7930	4326500	0.6801
7.7939	4327000	0.6792
7.7948	4327500	0.6587
7.7957	4328000	0.6769
7.7966	4328500	0.6637
7.7975	4329000	0.6695
7.7984	4329500	0.6646
7.7993	4330000	0.677
7.8002	4330500	0.6712
7.8011	4331000	0.6763
7.8020	4331500	0.6658
7.8029	4332000	0.6684
7.8038	4332500	0.6836
7.8047	4333000	0.6803
7.8056	4333500	0.6848
7.8065	4334000	0.6721
7.8074	4334500	0.6756
7.8083	4335000	0.6658
7.8092	4335500	0.668
7.8101	4336000	0.6713
7.8110	4336500	0.6788
7.8119	4337000	0.6836
7.8128	4337500	0.6691
7.8137	4338000	0.6756
7.8146	4338500	0.6762
7.8155	4339000	0.6785
7.8164	4339500	0.6727
7.8173	4340000	0.6846
7.8182	4340500	0.6625
7.8191	4341000	0.6711
7.8200	4341500	0.6773
7.8209	4342000	0.6748
7.8218	4342500	0.6715
7.8227	4343000	0.6697
7.8236	4343500	0.6686
7.8245	4344000	0.6828

Framework Versions

• Python: 3.12.9
• Sentence Transformers: 5.1.2
• Transformers: 4.55.4
• PyTorch: 2.6.0+cu126
• Accelerate: 1.11.0
• Datasets: 4.4.1
• Tokenizers: 0.21.4

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}
}