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legal-bert-base-uncased-ft-new

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1627
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community

legal_bert_base_uncased Finetuned on Data

This is a sentence-transformers model finetuned from nlpaueb/legal-bert-base-uncased. 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: nlpaueb/legal-bert-base-uncased
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
  (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 = [
    "In what context does the operation of the democratic system in a Member State require political parties to compile personal data on people's political opinions?",
    "Where in the course of electoral activities, the operation of the democratic system in a Member State requires that political parties compile personal data on people's political opinions, the processing of such data may be permitted for reasons of public interest, provided that appropriate safeguards are established.",
    '1.The data subject shall have the right to obtain from the controller confirmation as to whether or not personal data concerning him or her are being processed, and, where that is the case, access to the personal data and the following information: (a)  the purposes of the processing; (b)  the categories of personal data concerned; (c)  the recipients or categories of recipient to whom the personal data have been or will be disclosed, in particular recipients in third countries or international organisations; (d)  where possible, the envisaged period for which the personal data will be stored, or, if not possible, the criteria used to determine that period; (e)  the existence of the right to request from the controller rectification or erasure of personal data or restriction of processing of personal data concerning the data subject or to object to such processing; (f)  the right to lodge a complaint with a supervisory authority; (g)  where the personal data are not collected from the data subject, any available information as to their source; (h)  the existence of automated decision-making, including profiling, referred to in Article 22(1) and (4) and, at least in those cases, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.\n2.Where personal data are transferred to a third country or to an international organisation, the data subject shall have the right to be informed of the appropriate safeguards pursuant to Article 46 relating to the transfer.\n3.The controller shall provide a copy of the personal data undergoing processing. For any further copies requested by the data subject, the controller may charge a reasonable fee based on administrative costs. Where the data subject makes the request by electronic means, and unless otherwise requested by the data subject, the information shall be provided in a commonly used electronic form.\n4.The right to obtain a copy referred to in paragraph 3 shall not adversely affect the rights and freedoms of others. Section 3 Rectification and erasure',
]
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, 0.8307, 0.1443],
#         [0.8307, 1.0000, 0.1468],
#         [0.1443, 0.1468, 1.0000]])

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.4079
cosine_accuracy@3 0.4521
cosine_accuracy@5 0.4988
cosine_accuracy@10 0.5627
cosine_precision@1 0.4079
cosine_precision@3 0.407
cosine_precision@5 0.398
cosine_precision@10 0.3681
cosine_recall@1 0.0629
cosine_recall@3 0.1767
cosine_recall@5 0.2581
cosine_recall@10 0.397
cosine_ndcg@10 0.4802
cosine_mrr@10 0.4422
cosine_map@100 0.5349

Information Retrieval

Metric Value
cosine_accuracy@1 0.4201
cosine_accuracy@3 0.4595
cosine_accuracy@5 0.5037
cosine_accuracy@10 0.5725
cosine_precision@1 0.4201
cosine_precision@3 0.4185
cosine_precision@5 0.4079
cosine_precision@10 0.3737
cosine_recall@1 0.0639
cosine_recall@3 0.178
cosine_recall@5 0.2597
cosine_recall@10 0.3948
cosine_ndcg@10 0.4862
cosine_mrr@10 0.4528
cosine_map@100 0.5415

Information Retrieval

Metric Value
cosine_accuracy@1 0.4029
cosine_accuracy@3 0.4423
cosine_accuracy@5 0.4717
cosine_accuracy@10 0.5479
cosine_precision@1 0.4029
cosine_precision@3 0.4013
cosine_precision@5 0.3877
cosine_precision@10 0.3587
cosine_recall@1 0.0619
cosine_recall@3 0.1719
cosine_recall@5 0.2476
cosine_recall@10 0.3811
cosine_ndcg@10 0.4679
cosine_mrr@10 0.434
cosine_map@100 0.5272

Information Retrieval

Metric Value
cosine_accuracy@1 0.4054
cosine_accuracy@3 0.4496
cosine_accuracy@5 0.4742
cosine_accuracy@10 0.5258
cosine_precision@1 0.4054
cosine_precision@3 0.3997
cosine_precision@5 0.3838
cosine_precision@10 0.3511
cosine_recall@1 0.0673
cosine_recall@3 0.1777
cosine_recall@5 0.2456
cosine_recall@10 0.3598
cosine_ndcg@10 0.4621
cosine_mrr@10 0.4332
cosine_map@100 0.5189

Information Retrieval

Metric Value
cosine_accuracy@1 0.3833
cosine_accuracy@3 0.4251
cosine_accuracy@5 0.4521
cosine_accuracy@10 0.5135
cosine_precision@1 0.3833
cosine_precision@3 0.3833
cosine_precision@5 0.373
cosine_precision@10 0.343
cosine_recall@1 0.0568
cosine_recall@3 0.1575
cosine_recall@5 0.2312
cosine_recall@10 0.3402
cosine_ndcg@10 0.4408
cosine_mrr@10 0.4121
cosine_map@100 0.5014

Training Details

Training Dataset

Unnamed Dataset

  • Size: 1,627 training samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchor positive
    type string string
    details
    • min: 7 tokens
    • mean: 15.21 tokens
    • max: 36 tokens
    • min: 25 tokens
    • mean: 369.39 tokens
    • max: 512 tokens
  • Samples:
    anchor positive
    What is the purpose of the publicly accessible electronic register maintained by the authority? 1.The Board shall ensure the consistent application of this Regulation. To that end, the Board shall, on its own initiative or, where relevant, at the request of the Commission, in particular: (a) monitor and ensure the correct application of this Regulation in the cases provided for in Articles 64 and 65 without prejudice to the tasks of national supervisory authorities; 4.5.2016 L 119/76 (b) advise the Commission on any issue related to the protection of personal data in the Union, including on any proposed amendment of this Regulation; (c) advise the Commission on the format and procedures for the exchange of information between controllers, processors and supervisory authorities for binding corporate rules; (d) issue guidelines, recommendations, and best practices on procedures for erasing links, copies or replications of personal data from publicly available communication services as referred to in Article 17(2); (e) examine, on its own initiative, on request of one of its ...
    How should the request for consent be presented? 1.Where processing is based on consent, the controller shall be able to demonstrate that the data subject has consented to processing of his or her personal data.
    2.If the data subject's consent is given in the context of a written declaration which also concerns other matters, the request for consent shall be presented in a manner which is clearly distinguishable from the other matters, in an intelligible and easily accessible form, using clear and plain language. Any part of such a declaration which constitutes an infringement of this Regulation shall not be binding.
    3.The data subject shall have the right to withdraw his or her consent at any time. The withdrawal of consent shall not affect the lawfulness of processing based on consent before its withdrawal. Prior to giving consent, the data subject shall be informed thereof. It shall be as easy to withdraw as to give consent.
    4.When assessing whether consent is freely given, utmost account shall be taken of whether, inter alia, the...
    What type of clauses is the European Data Protection Committee encouraged to adopt under point (j)? 1.Without prejudice to other tasks set out under this Regulation, each supervisory authority shall on its territory: (a) monitor and enforce the application of this Regulation; (b) promote public awareness and understanding of the risks, rules, safeguards and rights in relation to processing. Activities addressed specifically to children shall receive specific attention; (c) advise, in accordance with Member State law, the national parliament, the government, and other institutions and bodies on legislative and administrative measures relating to the protection of natural persons' rights and freedoms with regard to processing; (d) promote the awareness of controllers and processors of their obligations under this Regulation; (e) upon request, provide information to any data subject concerning the exercise of their rights under this Regulation and, if appropriate, cooperate with the supervisory authorities in other Member States to that end; (f) handle complaints lodged by a data ...
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • gradient_accumulation_steps: 2
  • learning_rate: 2e-05
  • num_train_epochs: 10
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • bf16: True
  • tf32: True
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 2
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-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: 10
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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: True
  • 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: True
  • 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}
  • tp_size: 0
  • 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_fused
  • 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
  • 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
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss dim_768_cosine_ndcg@10 dim_512_cosine_ndcg@10 dim_256_cosine_ndcg@10 dim_128_cosine_ndcg@10 dim_64_cosine_ndcg@10
0.0098 1 16.142 - - - - -
0.0196 2 13.6979 - - - - -
0.0294 3 17.5356 - - - - -
0.0392 4 14.932 - - - - -
0.0490 5 19.2064 - - - - -
0.0588 6 16.981 - - - - -
0.0686 7 17.1026 - - - - -
0.0784 8 15.6905 - - - - -
0.0882 9 18.223 - - - - -
0.0980 10 15.6634 - - - - -
0.1078 11 21.4638 - - - - -
0.1176 12 18.1468 - - - - -
0.1275 13 17.1178 - - - - -
0.1373 14 15.877 - - - - -
0.1471 15 15.1227 - - - - -
0.1569 16 14.0964 - - - - -
0.1667 17 17.4772 - - - - -
0.1765 18 19.1766 - - - - -
0.1863 19 14.6675 - - - - -
0.1961 20 13.2606 - - - - -
0.2059 21 18.2742 - - - - -
0.2157 22 18.0096 - - - - -
0.2255 23 12.3101 - - - - -
0.2353 24 14.0233 - - - - -
0.2451 25 12.9841 - - - - -
0.2549 26 13.7307 - - - - -
0.2647 27 16.5663 - - - - -
0.2745 28 13.9413 - - - - -
0.2843 29 15.4937 - - - - -
0.2941 30 12.2452 - - - - -
0.3039 31 11.8533 - - - - -
0.3137 32 12.9059 - - - - -
0.3235 33 13.6171 - - - - -
0.3333 34 15.7267 - - - - -
0.3431 35 11.0184 - - - - -
0.3529 36 13.0709 - - - - -
0.3627 37 13.0111 - - - - -
0.3725 38 12.1237 - - - - -
0.3824 39 12.1466 - - - - -
0.3922 40 13.1446 - - - - -
0.4020 41 14.607 - - - - -
0.4118 42 13.0565 - - - - -
0.4216 43 11.0872 - - - - -
0.4314 44 13.9679 - - - - -
0.4412 45 11.4318 - - - - -
0.4510 46 13.5983 - - - - -
0.4608 47 13.3464 - - - - -
0.4706 48 10.2001 - - - - -
0.4804 49 12.6368 - - - - -
0.4902 50 14.6145 - - - - -
0.5 51 10.4465 - - - - -
0.5098 52 11.4625 - - - - -
0.5196 53 13.0207 - - - - -
0.5294 54 14.5234 - - - - -
0.5392 55 13.5372 - - - - -
0.5490 56 9.3016 - - - - -
0.5588 57 10.8238 - - - - -
0.5686 58 13.8616 - - - - -
0.5784 59 11.3745 - - - - -
0.5882 60 9.5075 - - - - -
0.5980 61 10.5469 - - - - -
0.6078 62 10.9505 - - - - -
0.6176 63 9.7053 - - - - -
0.6275 64 13.7708 - - - - -
0.6373 65 8.9201 - - - - -
0.6471 66 7.1201 - - - - -
0.6569 67 10.5854 - - - - -
0.6667 68 10.8858 - - - - -
0.6765 69 5.099 - - - - -
0.6863 70 7.2317 - - - - -
0.6961 71 12.0098 - - - - -
0.7059 72 11.1372 - - - - -
0.7157 73 13.2915 - - - - -
0.7255 74 7.666 - - - - -
0.7353 75 8.9583 - - - - -
0.7451 76 14.4897 - - - - -
0.7549 77 5.2462 - - - - -
0.7647 78 8.6225 - - - - -
0.7745 79 11.8394 - - - - -
0.7843 80 13.6184 - - - - -
0.7941 81 8.3545 - - - - -
0.8039 82 10.7644 - - - - -
0.8137 83 11.0633 - - - - -
0.8235 84 12.6646 - - - - -
0.8333 85 9.8459 - - - - -
0.8431 86 9.6683 - - - - -
0.8529 87 8.6824 - - - - -
0.8627 88 11.2598 - - - - -
0.8725 89 7.7519 - - - - -
0.8824 90 6.2339 - - - - -
0.8922 91 12.3876 - - - - -
0.9020 92 9.0571 - - - - -
0.9118 93 11.2924 - - - - -
0.9216 94 9.8345 - - - - -
0.9314 95 11.6696 - - - - -
0.9412 96 4.8431 - - - - -
0.9510 97 6.9415 - - - - -
0.9608 98 6.5089 - - - - -
0.9706 99 7.2339 - - - - -
0.9804 100 6.9042 - - - - -
0.9902 101 7.3693 - - - - -
1.0 102 7.3341 0.3214 0.3273 0.3465 0.3125 0.2972
1.0098 103 2.4598 - - - - -
1.0196 104 6.2536 - - - - -
1.0294 105 3.4257 - - - - -
1.0392 106 3.8891 - - - - -
1.0490 107 4.9762 - - - - -
1.0588 108 7.4206 - - - - -
1.0686 109 7.4156 - - - - -
1.0784 110 7.7509 - - - - -
1.0882 111 7.8635 - - - - -
1.0980 112 5.5645 - - - - -
1.1078 113 4.4094 - - - - -
1.1176 114 7.3678 - - - - -
1.1275 115 5.9872 - - - - -
1.1373 116 10.9788 - - - - -
1.1471 117 10.184 - - - - -
1.1569 118 9.0405 - - - - -
1.1667 119 4.9606 - - - - -
1.1765 120 5.9992 - - - - -
1.1863 121 4.846 - - - - -
1.1961 122 9.0015 - - - - -
1.2059 123 3.7348 - - - - -
1.2157 124 5.6321 - - - - -
1.2255 125 5.4282 - - - - -
1.2353 126 4.7605 - - - - -
1.2451 127 6.4746 - - - - -
1.2549 128 5.2789 - - - - -
1.2647 129 8.5016 - - - - -
1.2745 130 5.6314 - - - - -
1.2843 131 5.0657 - - - - -
1.2941 132 5.4924 - - - - -
1.3039 133 4.5245 - - - - -
1.3137 134 3.4378 - - - - -
1.3235 135 9.0247 - - - - -
1.3333 136 8.8494 - - - - -
1.3431 137 5.8472 - - - - -
1.3529 138 9.1909 - - - - -
1.3627 139 7.2911 - - - - -
1.3725 140 4.8004 - - - - -
1.3824 141 5.212 - - - - -
1.3922 142 6.5621 - - - - -
1.4020 143 4.5448 - - - - -
1.4118 144 6.5068 - - - - -
1.4216 145 3.3916 - - - - -
1.4314 146 4.8277 - - - - -
1.4412 147 7.9691 - - - - -
1.4510 148 5.8588 - - - - -
1.4608 149 4.7892 - - - - -
1.4706 150 6.8099 - - - - -
1.4804 151 8.754 - - - - -
1.4902 152 7.0441 - - - - -
1.5 153 9.6257 - - - - -
1.5098 154 2.521 - - - - -
1.5196 155 5.5603 - - - - -
1.5294 156 1.9541 - - - - -
1.5392 157 5.8526 - - - - -
1.5490 158 7.6458 - - - - -
1.5588 159 6.384 - - - - -
1.5686 160 5.6423 - - - - -
1.5784 161 5.5204 - - - - -
1.5882 162 7.6642 - - - - -
1.5980 163 3.7946 - - - - -
1.6078 164 4.2481 - - - - -
1.6176 165 4.4644 - - - - -
1.6275 166 4.674 - - - - -
1.6373 167 3.3481 - - - - -
1.6471 168 10.6918 - - - - -
1.6569 169 5.1483 - - - - -
1.6667 170 5.0832 - - - - -
1.6765 171 4.7421 - - - - -
1.6863 172 4.2187 - - - - -
1.6961 173 6.6807 - - - - -
1.7059 174 2.2695 - - - - -
1.7157 175 2.53 - - - - -
1.7255 176 6.0218 - - - - -
1.7353 177 8.7176 - - - - -
1.7451 178 6.9877 - - - - -
1.7549 179 7.2235 - - - - -
1.7647 180 2.7433 - - - - -
1.7745 181 4.1857 - - - - -
1.7843 182 8.8419 - - - - -
1.7941 183 4.2731 - - - - -
1.8039 184 5.9068 - - - - -
1.8137 185 3.8098 - - - - -
1.8235 186 3.6064 - - - - -
1.8333 187 7.5581 - - - - -
1.8431 188 5.1475 - - - - -
1.8529 189 0.9157 - - - - -
1.8627 190 5.8079 - - - - -
1.8725 191 3.7 - - - - -
1.8824 192 5.0955 - - - - -
1.8922 193 4.1939 - - - - -
1.9020 194 9.5075 - - - - -
1.9118 195 8.3701 - - - - -
1.9216 196 4.8127 - - - - -
1.9314 197 10.9187 - - - - -
1.9412 198 3.5791 - - - - -
1.9510 199 11.3306 - - - - -
1.9608 200 6.3793 - - - - -
1.9706 201 4.1617 - - - - -
1.9804 202 13.2734 - - - - -
1.9902 203 7.1173 - - - - -
2.0 204 2.266 0.3906 0.3760 0.3709 0.3645 0.3147
2.0098 205 3.1302 - - - - -
2.0196 206 1.3766 - - - - -
2.0294 207 3.3127 - - - - -
2.0392 208 2.5981 - - - - -
2.0490 209 3.4364 - - - - -
2.0588 210 2.6902 - - - - -
2.0686 211 2.2741 - - - - -
2.0784 212 3.3271 - - - - -
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7.0392 718 0.1334 - - - - -
7.0490 719 0.4343 - - - - -
7.0588 720 3.0699 - - - - -
7.0686 721 0.9501 - - - - -
7.0784 722 0.1778 - - - - -
7.0882 723 0.4164 - - - - -
7.0980 724 0.2963 - - - - -
7.1078 725 1.3947 - - - - -
7.1176 726 0.505 - - - - -
7.1275 727 0.5072 - - - - -
7.1373 728 0.1854 - - - - -
7.1471 729 0.1622 - - - - -
7.1569 730 0.6847 - - - - -
7.1667 731 1.3038 - - - - -
7.1765 732 0.9911 - - - - -
7.1863 733 2.1003 - - - - -
7.1961 734 0.3802 - - - - -
7.2059 735 1.134 - - - - -
7.2157 736 0.3427 - - - - -
7.2255 737 0.8341 - - - - -
7.2353 738 1.1072 - - - - -
7.2451 739 0.3203 - - - - -
7.2549 740 0.3252 - - - - -
7.2647 741 1.0124 - - - - -
7.2745 742 0.1467 - - - - -
7.2843 743 0.1729 - - - - -
7.2941 744 0.167 - - - - -
7.3039 745 0.1038 - - - - -
7.3137 746 2.6918 - - - - -
7.3235 747 0.3386 - - - - -
7.3333 748 0.1204 - - - - -
7.3431 749 1.5078 - - - - -
7.3529 750 0.0813 - - - - -
7.3627 751 1.3693 - - - - -
7.3725 752 0.7416 - - - - -
7.3824 753 0.5061 - - - - -
7.3922 754 1.0782 - - - - -
7.4020 755 0.1061 - - - - -
7.4118 756 0.4726 - - - - -
7.4216 757 0.3794 - - - - -
7.4314 758 1.0286 - - - - -
7.4412 759 0.3516 - - - - -
7.4510 760 0.2665 - - - - -
7.4608 761 0.4261 - - - - -
7.4706 762 0.7815 - - - - -
7.4804 763 0.0516 - - - - -
7.4902 764 0.4681 - - - - -
7.5 765 0.1083 - - - - -
7.5098 766 0.1157 - - - - -
7.5196 767 0.0383 - - - - -
7.5294 768 0.1496 - - - - -
7.5392 769 1.5602 - - - - -
7.5490 770 1.2548 - - - - -
7.5588 771 0.5062 - - - - -
7.5686 772 1.3416 - - - - -
7.5784 773 0.0605 - - - - -
7.5882 774 0.4715 - - - - -
7.5980 775 0.0467 - - - - -
7.6078 776 0.9769 - - - - -
7.6176 777 0.0936 - - - - -
7.6275 778 0.3977 - - - - -
7.6373 779 0.0266 - - - - -
7.6471 780 0.2169 - - - - -
7.6569 781 0.4521 - - - - -
7.6667 782 0.5292 - - - - -
7.6765 783 0.9527 - - - - -
7.6863 784 0.2699 - - - - -
7.6961 785 0.0181 - - - - -
7.7059 786 1.7187 - - - - -
7.7157 787 1.8481 - - - - -
7.7255 788 0.1036 - - - - -
7.7353 789 0.2311 - - - - -
7.7451 790 0.221 - - - - -
7.7549 791 0.1773 - - - - -
7.7647 792 0.2201 - - - - -
7.7745 793 0.3505 - - - - -
7.7843 794 0.6739 - - - - -
7.7941 795 0.3774 - - - - -
7.8039 796 0.609 - - - - -
7.8137 797 0.106 - - - - -
7.8235 798 0.4456 - - - - -
7.8333 799 1.3334 - - - - -
7.8431 800 0.0451 - - - - -
7.8529 801 1.024 - - - - -
7.8627 802 0.1337 - - - - -
7.8725 803 1.0237 - - - - -
7.8824 804 0.7726 - - - - -
7.8922 805 0.5858 - - - - -
7.9020 806 0.0826 - - - - -
7.9118 807 1.5779 - - - - -
7.9216 808 0.5999 - - - - -
7.9314 809 0.1622 - - - - -
7.9412 810 0.8974 - - - - -
7.9510 811 0.1727 - - - - -
7.9608 812 0.5008 - - - - -
7.9706 813 0.1356 - - - - -
7.9804 814 1.4574 - - - - -
7.9902 815 0.7763 - - - - -
8.0 816 0.0359 0.4802 0.4862 0.4679 0.4621 0.4408
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 5.1.2
  • Transformers: 4.51.3
  • PyTorch: 2.8.0+cu126
  • Accelerate: 1.11.0
  • Datasets: 4.0.0
  • 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",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss 

@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}
}
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