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bge-embedding-model2

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1865
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
text-embeddings-inference
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BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from SQAI/bge-embedding-model. It maps sentences & paragraphs to a 384-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: SQAI/bge-embedding-model
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 384 tokens
  • 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("SQAI/bge-embedding-model2")
# Run inference
sentences = [
    'errors.controllerFault.lowLoadCurrent',
    '"Can you provide me with the current status of the streetlight on \'street name\', specifically in relation to its voltage under load, whether it\'s lower than expected and how that might be indicating potential electrical issues? Could you also give me insight into the current drawn by the streetlight, whether or not the relay is currently on or off, and if there are any faults in the lux module that may affect light level sensing and control? Moreover, could you tell me the type of dimming schedule applied, the ambient light level detected in lux, the total energy consumed so far recorded in kilowatt-hours, and the lower voltage threshold for this streetlight\'s efficient operation?"',
    '"Can you show me the current status of the relay in the streetlights located at the X-coordinate grid, highlighting any faults in the lux module that might be affecting light level sensing and control? Also, could you provide information on the current dimming level of these streetlights in operation, the type of dimming schedule applied, and whether the voltage is within the upper limit considered safe and efficient for their operation?"',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0144
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.0014
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0144
cosine_ndcg@10 0.0043
cosine_mrr@10 0.0015
cosine_map@100 0.0059

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0144
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.0014
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0144
cosine_ndcg@10 0.0043
cosine_mrr@10 0.0015
cosine_map@100 0.0059

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0144
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.0014
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0144
cosine_ndcg@10 0.0044
cosine_mrr@10 0.0016
cosine_map@100 0.0057

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0096
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.001
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0096
cosine_ndcg@10 0.003
cosine_mrr@10 0.0012
cosine_map@100 0.0052

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0192
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.0019
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0192
cosine_ndcg@10 0.006
cosine_mrr@10 0.0023
cosine_map@100 0.0052

Training Details

Training Dataset

Unnamed Dataset

  • Size: 1,865 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 7.68 tokens
    • max: 14 tokens
    • min: 17 tokens
    • mean: 89.79 tokens
    • max: 187 tokens
  • Samples:
    positive anchor
    threshold.lowLoadVoltage "What is the maximum current level above which it is considered unsafe for a specific streetlight in my area, what is the minimum longitude of the geographic area this streetlight covers, is this streetlight's control mode automated or manually controlled, also, can you provide the delta or width of the grid area occupied by this group of streetlights, what is the level of AC voltage supply to this streetlight, what's the lower voltage threshold below which this streetlight may not operate efficiently, how many times has this streetlight been switched on, what is the minimum operational voltage under load conditions, and finally, what is the latitude of this streetlight?"
    asset.id "Could you please tell me the scheduled dimming settings for the string stored streetlights, troubleshoot why these streetlights remain on during daylight hours, and confirm if this could be due to sensor faults? Also, I'd like to know the identifier for the parent group to which this group of streetlights belongs, and the IMEI number of the streetlight device."
    errors.controllerFault.highPower "Can you provide an analysis of the efficiency of power usage by examining the power factor of the streetlights, especially in areas of the grid with high Y-coordinates, highlight instances where power consumption is significantly higher than expected which may indicate faults, identify situations where voltage under load is above safe levels, and assess if there are any problems with our central control system's ability to manage streetlight groups?"
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Evaluation Dataset

Unnamed Dataset

  • Size: 208 evaluation samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 7.55 tokens
    • max: 14 tokens
    • min: 19 tokens
    • mean: 90.69 tokens
    • max: 187 tokens
  • Samples:
    positive anchor
    log.controlModeSwitch "Can you provide the control mode switch identifier used for changing the default dimming level set for a specific group of streetlights, identified by their unique identifier, considering the time taken for the streetlight to activate or light up from the command, and possibly troubleshoot why the power consumption is lower than expected which could be due to hardware issues, quite possibly due to the relay responsible for turning the streetlight on and off sticking?"
    errors.controllerFault.luxModuleFault "Can you provide the timestamp of the last update to the threshold settings, and detail any faults in the lux module related to light level sensing and control for the streetlight on this specific street name? I also want to know the longitude of the streetlight. And also, can you tell me what type of dimming schedule is applied to the streetlight, the type of port used for its dimming controls, and the total energy it has consumed, recorded in kilowatt-hours. Lastly, could you also provide the timestamp of the recorded streetlighting error, and confirm the status of the relay responsible for turning this streetlight on and off, as I am suspecting it might be sticking?"
    threshold.lowLoadCurrent "What is the maximum safe voltage under load conditions for the city's streetlights, and do we possess the necessary rights to link these streetlights for synchronized control? Could you provide me with the timestamp of the latest data or action performed by our streetlights, and tell me the lower lux level threshold at which we would need to consider additional lighting? How often does each streetlight send a data report in normal operation, and what is the minimum load current level where we might start seeing suboptimal functioning? Have we been experiencing any problems with managing groups of streetlights via the central control system? Also, has there been any instances where the current under load was excessively high, indicating possible overloads, or situations where the operation temperature was belo normal limits due to environmental conditions? Lastly, have there been any noted communication issues between the streetlight's driver and the control system?"
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-06
  • weight_decay: 0.03
  • num_train_epochs: 200
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.2
  • 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: 32
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 16
  • eval_accumulation_steps: None
  • learning_rate: 2e-06
  • weight_decay: 0.03
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 200
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.2
  • 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}
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss loss dim_128_cosine_map@100 dim_256_cosine_map@100 dim_512_cosine_map@100 dim_64_cosine_map@100 dim_768_cosine_map@100
0.2712 1 13.2713 - - - - - -
0.5424 2 13.2895 - - - - - -
0.8136 3 9.9139 - - - - - -
1.0847 4 5.6117 - - - - - -
1.3559 5 4.7571 - - - - - -
1.6271 6 5.5215 - - - - - -
1.8983 7 5.7945 - - - - - -
2.1695 8 5.7064 - - - - - -
2.4407 9 5.6794 - - - - - -
2.7119 10 5.7384 - - - - - -
2.9831 11 5.6081 - - - - - -
3.2542 12 5.5278 - - - - - -
3.5254 13 5.149 - - - - - -
3.7966 14 5.5904 5.6043 0.0081 0.0072 0.0079 0.0055 0.0079
1.0169 15 3.9458 - - - - - -
1.2881 16 13.3653 - - - - - -
1.5593 17 13.4413 - - - - - -
1.8305 18 9.4188 - - - - - -
2.1017 19 5.717 - - - - - -
2.3729 20 5.2455 - - - - - -
2.6441 21 5.2117 - - - - - -
2.9153 22 5.5217 - - - - - -
3.1864 23 5.6725 - - - - - -
3.4576 24 5.786 - - - - - -
3.7288 25 5.6507 - - - - - -
4.0 26 5.7215 - - - - - -
4.2712 27 5.3999 - - - - - -
4.5424 28 5.4275 - - - - - -
4.8136 29 5.7143 5.5718 0.0082 0.0071 0.0077 0.0052 0.0077
2.0339 30 4.478 - - - - - -
2.3051 31 13.1821 - - - - - -
2.5763 32 13.2473 - - - - - -
2.8475 33 8.8654 - - - - - -
3.1186 34 5.3181 - - - - - -
3.3898 35 5.2091 - - - - - -
3.6610 36 5.6027 - - - - - -
3.9322 37 5.6839 - - - - - -
4.2034 38 5.5955 - - - - - -
4.4746 39 5.5786 - - - - - -
4.7458 40 5.4509 - - - - - -
5.0169 41 5.3361 - - - - - -
5.2881 42 5.1608 - - - - - -
5.5593 43 5.4896 - - - - - -
5.8305 44 5.6466 5.5241 0.0062 0.0070 0.0076 0.0095 0.0076
3.0508 45 4.5617 - - - - - -
3.3220 46 13.0665 - - - - - -
3.5932 47 13.1848 - - - - - -
3.8644 48 8.4053 - - - - - -
4.1356 49 5.2706 - - - - - -
4.4068 50 5.4269 - - - - - -
4.6780 51 5.3645 - - - - - -
4.9492 52 5.3587 - - - - - -
5.2203 53 5.1047 - - - - - -
5.4915 54 5.743 - - - - - -
5.7627 55 5.3754 - - - - - -
6.0339 56 5.3021 - - - - - -
6.3051 57 5.6983 - - - - - -
6.5763 58 5.302 - - - - - -
6.8475 59 5.4545 5.4638 0.0060 0.0070 0.0077 0.0094 0.0077
4.0678 60 5.2213 - - - - - -
4.3390 61 12.9854 - - - - - -
4.6102 62 13.207 - - - - - -
4.8814 63 7.7493 - - - - - -
5.1525 64 5.3787 - - - - - -
5.4237 65 4.9406 - - - - - -
5.6949 66 5.3963 - - - - - -
5.9661 67 5.3429 - - - - - -
6.2373 68 5.292 - - - - - -
6.5085 69 5.6738 - - - - - -
6.7797 70 5.5927 - - - - - -
7.0508 71 5.5245 - - - - - -
7.3220 72 4.8334 - - - - - -
7.5932 73 5.2015 - - - - - -
7.8644 74 5.5393 5.3954 0.0060 0.0071 0.0078 0.0094 0.0078
5.0847 75 5.6168 - - - - - -
5.3559 76 12.8678 - - - - - -
5.6271 77 13.2377 - - - - - -
5.8983 78 7.1882 - - - - - -
6.1695 79 5.1293 - - - - - -
6.4407 80 4.9413 - - - - - -
6.7119 81 5.1763 - - - - - -
6.9831 82 4.9512 - - - - - -
7.2542 83 5.2744 - - - - - -
7.5254 84 5.0573 - - - - - -
7.7966 85 5.1938 - - - - - -
8.0678 86 5.1514 - - - - - -
8.3390 87 4.9808 - - - - - -
8.6102 88 4.9983 - - - - - -
8.8814 89 5.3211 5.3268 0.0062 0.0067 0.0075 0.0095 0.0075
6.1017 90 6.1513 - - - - - -
6.3729 91 12.7972 - - - - - -
6.6441 92 13.0051 - - - - - -
6.9153 93 6.551 - - - - - -
7.1864 94 4.6644 - - - - - -
7.4576 95 4.8619 - - - - - -
7.7288 96 5.0812 - - - - - -
8.0 97 4.758 - - - - - -
8.2712 98 5.1362 - - - - - -
8.5424 99 5.5405 - - - - - -
8.8136 100 5.228 - - - - - -
9.0847 101 5.1084 - - - - - -
9.3559 102 5.1574 - - - - - -
9.6271 103 5.3326 - - - - - -
9.8983 104 5.34 5.2658 0.0060 0.0066 0.0076 0.0052 0.0076
7.1186 105 6.5789 - - - - - -
7.3898 106 12.7557 - - - - - -
7.6610 107 13.0203 - - - - - -
7.9322 108 5.7148 - - - - - -
8.2034 109 4.7945 - - - - - -
8.4746 110 4.5926 - - - - - -
8.7458 111 4.6727 - - - - - -
9.0169 112 5.0886 - - - - - -
9.2881 113 5.0562 - - - - - -
9.5593 114 5.2167 - - - - - -
9.8305 115 5.048 - - - - - -
10.1017 116 4.7765 - - - - - -
10.3729 117 4.9875 - - - - - -
10.6441 118 4.9501 - - - - - -
10.9153 119 4.756 5.2124 0.0057 0.0065 0.0075 0.0054 0.0075
8.1356 120 6.9381 - - - - - -
8.4068 121 12.7916 - - - - - -
8.6780 122 12.8517 - - - - - -
8.9492 123 5.51 - - - - - -
9.2203 124 4.686 - - - - - -
9.4915 125 4.6611 - - - - - -
9.7627 126 5.2767 - - - - - -
10.0339 127 4.6103 - - - - - -
10.3051 128 4.957 - - - - - -
10.5763 129 5.0236 - - - - - -
10.8475 130 5.0894 - - - - - -
11.1186 131 4.7025 - - - - - -
11.3898 132 5.0765 - - - - - -
11.6610 133 4.6601 - - - - - -
11.9322 134 4.9064 5.1731 0.0056 0.0060 0.0070 0.0054 0.0070
9.1525 135 7.5884 - - - - - -
9.4237 136 12.679 - - - - - -
9.6949 137 12.417 - - - - - -
9.9661 138 5.1632 - - - - - -
10.2373 139 4.9486 - - - - - -
10.5085 140 4.6341 - - - - - -
10.7797 141 4.9664 - - - - - -
11.0508 142 4.9567 - - - - - -
11.3220 143 4.7532 - - - - - -
11.5932 144 5.2556 - - - - - -
11.8644 145 4.9652 - - - - - -
12.1356 146 4.8118 - - - - - -
12.4068 147 4.704 - - - - - -
12.6780 148 4.8922 - - - - - -
12.9492 149 4.6571 5.1441 0.0061 0.0055 0.0064 0.0053 0.0064
10.1695 150 8.1284 - - - - - -
10.4407 151 12.5703 - - - - - -
10.7119 152 11.8696 - - - - - -
10.9831 153 4.8543 - - - - - -
11.2542 154 4.8099 - - - - - -
11.5254 155 4.7009 - - - - - -
11.7966 156 4.7986 - - - - - -
12.0678 157 4.7973 - - - - - -
12.3390 158 4.5529 - - - - - -
12.6102 159 5.0275 - - - - - -
12.8814 160 4.6675 - - - - - -
13.1525 161 4.6538 - - - - - -
13.4237 162 4.8355 - - - - - -
13.6949 163 4.6304 - - - - - -
13.9661 164 4.7047 5.1242 0.0064 0.0054 0.0064 0.0095 0.0064
11.1864 165 8.6549 - - - - - -
11.4576 166 12.4788 - - - - - -
11.7288 167 11.6425 - - - - - -
12.0 168 4.5654 - - - - - -
12.2712 169 4.7016 - - - - - -
12.5424 170 4.3306 - - - - - -
12.8136 171 4.9692 - - - - - -
13.0847 172 4.7557 - - - - - -
13.3559 173 4.8665 - - - - - -
13.6271 174 4.8338 - - - - - -
13.8983 175 4.9221 - - - - - -
14.1695 176 4.4968 - - - - - -
14.4407 177 4.6104 - - - - - -
14.7119 178 4.8449 - - - - - -
14.9831 179 4.2392 5.1123 0.0059 0.0055 0.0065 0.0094 0.0065
12.2034 180 9.4893 - - - - - -
12.4746 181 12.4241 - - - - - -
12.7458 182 11.0389 - - - - - -
13.0169 183 4.7595 - - - - - -
13.2881 184 4.5408 - - - - - -
13.5593 185 4.6108 - - - - - -
13.8305 186 4.5832 - - - - - -
14.1017 187 4.6741 - - - - - -
14.3729 188 4.9353 - - - - - -
14.6441 189 5.0511 - - - - - -
14.9153 190 4.6575 - - - - - -
15.1864 191 4.648 - - - - - -
15.4576 192 4.6224 - - - - - -
15.7288 193 4.9292 - - - - - -
16.0 194 3.7805 5.1058 0.0063 0.0057 0.0062 0.0094 0.0062
13.2203 195 10.2695 - - - - - -
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40.3390 565 4.4605 - - - - - -
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41.3559 580 4.5718 - - - - - -
41.6271 581 4.5955 - - - - - -
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39.1186 583 6.359 - - - - - -
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40.2034 587 4.4783 - - - - - -
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40.7458 589 4.4847 - - - - - -
41.0169 590 4.4116 - - - - - -
41.2881 591 4.3979 - - - - - -
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42.1017 594 4.5555 - - - - - -
42.3729 595 4.4966 - - - - - -
42.6441 596 4.6267 - - - - - -
42.9153 597 4.5834 5.1446 0.0051 0.0057 0.0058 0.0052 0.0058
40.1356 598 6.7009 - - - - - -
40.4068 599 12.2755 - - - - - -
40.6780 600 12.4465 5.1447 0.0052 0.0057 0.0059 0.0052 0.0059
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.1.2+cu121
  • Accelerate: 0.31.0
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

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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