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SQAI
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streetlight_sql_embedding

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:2464
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/temporary_embedding_2. 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/temporary_embedding_2
  • 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/streetlight_sql_embedding")
# Run inference
sentences = [
    'Power output is lower than expected for the streetlight (failure)',
    '"What are instances of potential faults indicated by streetlight power usage exceeding the maximum level, and can you also highlight cases where power consumption was unusually low due to hardware issues? Could you provide the timestamp for these occurrences?"',
    '"What is the correlation between the power usage of the streetlights and their failure occurrences?"',
]
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 0.0036
cosine_accuracy@3 0.0146
cosine_accuracy@5 0.0182
cosine_accuracy@10 0.0328
cosine_precision@1 0.0036
cosine_precision@3 0.0049
cosine_precision@5 0.0036
cosine_precision@10 0.0033
cosine_recall@1 0.0036
cosine_recall@3 0.0146
cosine_recall@5 0.0182
cosine_recall@10 0.0328
cosine_ndcg@10 0.0167
cosine_mrr@10 0.0118
cosine_map@100 0.016

Information Retrieval

Metric Value
cosine_accuracy@1 0.0036
cosine_accuracy@3 0.0146
cosine_accuracy@5 0.0182
cosine_accuracy@10 0.0328
cosine_precision@1 0.0036
cosine_precision@3 0.0049
cosine_precision@5 0.0036
cosine_precision@10 0.0033
cosine_recall@1 0.0036
cosine_recall@3 0.0146
cosine_recall@5 0.0182
cosine_recall@10 0.0328
cosine_ndcg@10 0.0167
cosine_mrr@10 0.0118
cosine_map@100 0.016

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0073
cosine_accuracy@5 0.0109
cosine_accuracy@10 0.0292
cosine_precision@1 0.0
cosine_precision@3 0.0024
cosine_precision@5 0.0022
cosine_precision@10 0.0029
cosine_recall@1 0.0
cosine_recall@3 0.0073
cosine_recall@5 0.0109
cosine_recall@10 0.0292
cosine_ndcg@10 0.0119
cosine_mrr@10 0.0068
cosine_map@100 0.0111

Information Retrieval

Metric Value
cosine_accuracy@1 0.0073
cosine_accuracy@3 0.0182
cosine_accuracy@5 0.0182
cosine_accuracy@10 0.0255
cosine_precision@1 0.0073
cosine_precision@3 0.0061
cosine_precision@5 0.0036
cosine_precision@10 0.0026
cosine_recall@1 0.0073
cosine_recall@3 0.0182
cosine_recall@5 0.0182
cosine_recall@10 0.0255
cosine_ndcg@10 0.0159
cosine_mrr@10 0.013
cosine_map@100 0.0168

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0073
cosine_accuracy@5 0.0109
cosine_accuracy@10 0.0182
cosine_precision@1 0.0
cosine_precision@3 0.0024
cosine_precision@5 0.0022
cosine_precision@10 0.0018
cosine_recall@1 0.0
cosine_recall@3 0.0073
cosine_recall@5 0.0109
cosine_recall@10 0.0182
cosine_ndcg@10 0.0085
cosine_mrr@10 0.0054
cosine_map@100 0.0096

Training Details

Training Dataset

Unnamed Dataset

  • Size: 2,464 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 13.5 tokens
    • max: 20 tokens
    • min: 14 tokens
    • mean: 32.48 tokens
    • max: 81 tokens
  • Samples:
    positive anchor
    Timestamp of the latest data recorded or action performed by the streetlight "What is the latest action performed by the streetlight on 'specific street name' and when was it performed?"
    The relay responsible for turning the streetlight on and off is sticking (failure) "What is the count and timestamps of streetlighting errors due to malfunctioning relay in the system?"
    Default dimming level set for the streetlight "What is the default dimming level set for the city's streetlights?"
  • 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: 274 evaluation samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 13.36 tokens
    • max: 20 tokens
    • min: 14 tokens
    • mean: 34.05 tokens
    • max: 81 tokens
  • Samples:
    positive anchor
    Timestamp of the latest data recorded or action performed by the streetlight "When was the latest action performed by the streetlight, what is the status of its current draw, and where is it located?"
    Timestamp of the recorded streetlighting error (failure) "What are the permissions required to link multiple streetlights for synchronized control and when was the last recorded streetlighting error?"
    All failures listed "What is the latest timestamp data from the streetlight, the timestamp of the latest recorded streetlighting error, and if applicable, the subsequent error timestamps?"
  • 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: 1e-05
  • weight_decay: 0.03
  • num_train_epochs: 300
  • 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: 1e-05
  • weight_decay: 0.03
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 300
  • 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
0.2078 1 10.8799 - - - - - -
0.4156 2 11.9952 - - - - - -
0.6234 3 11.523 - - - - - -
0.8312 4 11.8355 - - - - - -
1.0390 5 8.7792 - - - - - -
1.2468 6 0.0 - - - - - -
1.4545 7 0.0 - - - - - -
1.6623 8 0.0 - - - - - -
1.8701 9 0.0 - - - - - -
2.0779 10 0.0 5.8123 0.0249 0.0315 0.0292 0.0250 0.0292
1.0260 11 1.4781 - - - - - -
1.2338 12 10.9683 - - - - - -
1.4416 13 11.657 - - - - - -
1.6494 14 11.4116 - - - - - -
1.8571 15 11.8774 - - - - - -
2.0649 16 6.9957 - - - - - -
2.2727 17 0.0 - - - - - -
2.4805 18 0.0 - - - - - -
2.6883 19 0.0 - - - - - -
2.8961 20 0.0 - - - - - -
3.1039 21 0.0 5.7413 0.0261 0.0298 0.0311 0.0208 0.0311
2.0519 22 2.5405 - - - - - -
2.2597 23 10.9358 - - - - - -
2.4675 24 11.4934 - - - - - -
2.6753 25 11.4221 - - - - - -
2.8831 26 11.8348 - - - - - -
3.0909 27 5.4544 - - - - - -
3.2987 28 0.0 - - - - - -
3.5065 29 0.0 - - - - - -
3.7143 30 0.0 - - - - - -
3.9221 31 0.0 - - - - - -
4.1299 32 0.0 5.6236 0.0285 0.0300 0.0309 0.0208 0.0309
3.0779 33 3.8704 - - - - - -
3.2857 34 11.127 - - - - - -
3.4935 35 11.2345 - - - - - -
3.7013 36 11.3097 - - - - - -
3.9091 37 11.5496 - - - - - -
4.1169 38 3.9187 - - - - - -
4.3247 39 0.0 - - - - - -
4.5325 40 0.0 - - - - - -
4.7403 41 0.0 - - - - - -
4.9481 42 0.0 - - - - - -
5.1558 43 0.0 5.4674 0.0313 0.0312 0.0299 0.0256 0.0299
4.1039 44 5.1029 - - - - - -
4.3117 45 10.8489 - - - - - -
4.5195 46 11.032 - - - - - -
4.7273 47 11.1298 - - - - - -
4.9351 48 10.7864 - - - - - -
5.1429 49 2.3991 - - - - - -
5.3506 50 0.0 - - - - - -
5.5584 51 0.0 - - - - - -
5.7662 52 0.0 - - - - - -
5.9740 53 0.0 - - - - - -
6.1818 54 0.0 5.2895 0.0299 0.0270 0.0303 0.0277 0.0303
5.1299 55 6.1795 - - - - - -
5.3377 56 10.4669 - - - - - -
5.5455 57 10.6607 - - - - - -
5.7532 58 10.7106 - - - - - -
5.9610 59 10.2785 - - - - - -
6.1688 60 1.3983 - - - - - -
6.3766 61 0.0 - - - - - -
6.5844 62 0.0 - - - - - -
6.7922 63 0.0 - - - - - -
7.0 64 0.0 - - - - - -
7.2078 65 0.0 5.1110 0.0240 0.0252 0.0294 0.0219 0.0294
6.1558 66 7.2055 - - - - - -
6.3636 67 10.5521 - - - - - -
6.5714 68 10.3721 - - - - - -
6.7792 69 10.5366 - - - - - -
6.9870 70 9.275 - - - - - -
7.1948 71 0.6043 - - - - - -
7.4026 72 0.0 - - - - - -
7.6104 73 0.0 - - - - - -
7.8182 74 0.0 - - - - - -
8.0260 75 0.0 - - - - - -
8.2338 76 0.0 4.9365 0.0257 0.0257 0.0328 0.0217 0.0328
7.1818 77 8.3679 - - - - - -
7.3896 78 10.4878 - - - - - -
7.5974 79 9.6964 - - - - - -
7.8052 80 10.2099 - - - - - -
8.0130 81 8.2936 - - - - - -
8.2208 82 0.1214 - - - - - -
8.4286 83 0.0 - - - - - -
8.6364 84 0.0 - - - - - -
8.8442 85 0.0 - - - - - -
9.0519 86 0.0 - - - - - -
9.2597 87 0.0 4.7852 0.0240 0.0272 0.0231 0.0227 0.0231
8.2078 88 9.1861 - - - - - -
8.4156 89 9.8309 - - - - - -
8.6234 90 9.7555 - - - - - -
8.8312 91 9.7722 - - - - - -
9.0390 92 6.9801 - - - - - -
9.2468 93 0.0 - - - - - -
9.4545 94 0.0 - - - - - -
9.6623 95 0.0 - - - - - -
9.8701 96 0.0 - - - - - -
10.0779 97 0.0 4.6765 0.0205 0.0241 0.0207 0.0170 0.0207
9.0260 98 1.1792 - - - - - -
9.2338 99 9.0915 - - - - - -
9.4416 100 9.4581 - - - - - -
9.6494 101 9.6098 - - - - - -
9.8571 102 9.581 - - - - - -
10.0649 103 5.335 - - - - - -
10.2727 104 0.0 - - - - - -
10.4805 105 0.0 - - - - - -
10.6883 106 0.0 - - - - - -
10.8961 107 0.0 - - - - - -
11.1039 108 0.0 4.5885 0.0216 0.0252 0.0215 0.0245 0.0215
10.0519 109 2.1487 - - - - - -
10.2597 110 9.1274 - - - - - -
10.4675 111 9.2582 - - - - - -
10.6753 112 9.4703 - - - - - -
10.8831 113 9.4621 - - - - - -
11.0909 114 4.035 - - - - - -
11.2987 115 0.0 - - - - - -
11.5065 116 0.0 - - - - - -
11.7143 117 0.0 - - - - - -
11.9221 118 0.0 - - - - - -
12.1299 119 0.0 4.5141 0.0188 0.0208 0.0189 0.0234 0.0189
11.0779 120 3.195 - - - - - -
11.2857 121 8.9396 - - - - - -
11.4935 122 9.0049 - - - - - -
11.7013 123 9.2817 - - - - - -
11.9091 124 9.0586 - - - - - -
12.1169 125 2.9238 - - - - - -
12.3247 126 0.0 - - - - - -
12.5325 127 0.0 - - - - - -
12.7403 128 0.0 - - - - - -
12.9481 129 0.0 - - - - - -
13.1558 130 0.0 4.4657 0.0189 0.0204 0.0189 0.0202 0.0189
12.1039 131 4.1912 - - - - - -
12.3117 132 8.6917 - - - - - -
12.5195 133 9.0598 - - - - - -
12.7273 134 9.1982 - - - - - -
12.9351 135 8.7155 - - - - - -
13.1429 136 1.766 - - - - - -
13.3506 137 0.0 - - - - - -
13.5584 138 0.0 - - - - - -
13.7662 139 0.0 - - - - - -
13.9740 140 0.0 - - - - - -
14.1818 141 0.0 4.4353 0.0195 0.0167 0.0175 0.0258 0.0175
13.1299 142 5.2156 - - - - - -
13.3377 143 8.6167 - - - - - -
13.5455 144 9.0144 - - - - - -
13.7532 145 9.0183 - - - - - -
13.9610 146 8.4695 - - - - - -
14.1688 147 0.8935 - - - - - -
14.3766 148 0.0 - - - - - -
14.5844 149 0.0 - - - - - -
14.7922 150 0.0 - - - - - -
15.0 151 0.0 - - - - - -
15.2078 152 0.0 4.4028 0.0195 0.0168 0.0187 0.0198 0.0187
14.1558 153 6.2434 - - - - - -
14.3636 154 8.6958 - - - - - -
14.5714 155 8.9179 - - - - - -
14.7792 156 8.8463 - - - - - -
14.9870 157 7.763 - - - - - -
15.1948 158 0.3196 - - - - - -
15.4026 159 0.0 - - - - - -
15.6104 160 0.0 - - - - - -
15.8182 161 0.0 - - - - - -
16.0260 162 0.0 - - - - - -
16.2338 163 0.0 4.3585 0.0245 0.0194 0.0179 0.0204 0.0179
15.1818 164 7.1152 - - - - - -
15.3896 165 8.7409 - - - - - -
15.5974 166 8.5694 - - - - - -
15.8052 167 8.8148 - - - - - -
16.0130 168 6.6922 - - - - - -
16.2208 169 0.0542 - - - - - -
16.4286 170 0.0 - - - - - -
16.6364 171 0.0 - - - - - -
16.8442 172 0.0 - - - - - -
17.0519 173 0.0 - - - - - -
17.2597 174 0.0 4.3435 0.0176 0.0205 0.0173 0.0177 0.0173
16.2078 175 8.0733 - - - - - -
16.4156 176 8.482 - - - - - -
16.6234 177 8.666 - - - - - -
16.8312 178 8.5444 - - - - - -
17.0390 179 5.7039 - - - - - -
17.2468 180 0.0 - - - - - -
17.4545 181 0.0 - - - - - -
17.6623 182 0.0 - - - - - -
17.8701 183 0.0 - - - - - -
18.0779 184 0.0 4.3313 0.0213 0.0167 0.0167 0.0158 0.0167
17.0260 185 1.0143 - - - - - -
17.2338 186 8.0912 - - - - - -
17.4416 187 8.3122 - - - - - -
17.6494 188 8.6962 - - - - - -
17.8571 189 8.5256 - - - - - -
18.0649 190 4.5109 - - - - - -
18.2727 191 0.0 - - - - - -
18.4805 192 0.0 - - - - - -
18.6883 193 0.0 - - - - - -
18.8961 194 0.0 - - - - - -
19.1039 195 0.0 4.3153 0.0235 0.0172 0.0172 0.0197 0.0172
18.0519 196 1.9381 - - - - - -
18.2597 197 8.1258 - - - - - -
18.4675 198 8.1344 - - - - - -
18.6753 199 8.4505 - - - - - -
18.8831 200 8.5247 - - - - - -
19.0909 201 3.3637 - - - - - -
19.2987 202 0.0 - - - - - -
19.5065 203 0.0 - - - - - -
19.7143 204 0.0 - - - - - -
19.9221 205 0.0 - - - - - -
20.1299 206 0.0 4.3008 0.0208 0.0182 0.0177 0.0154 0.0177
19.0779 207 2.922 - - - - - -
19.2857 208 8.1163 - - - - - -
19.4935 209 8.0596 - - - - - -
19.7013 210 8.5134 - - - - - -
19.9091 211 8.2545 - - - - - -
20.1169 212 2.2737 - - - - - -
20.3247 213 0.0 - - - - - -
20.5325 214 0.0 - - - - - -
20.7403 215 0.0 - - - - - -
20.9481 216 0.0 - - - - - -
21.1558 217 0.0 4.3059 0.0196 0.0169 0.0179 0.0161 0.0179
20.1039 218 3.9272 - - - - - -
20.3117 219 8.0318 - - - - - -
20.5195 220 8.3357 - - - - - -
20.7273 221 8.4001 - - - - - -
20.9351 222 8.0041 - - - - - -
21.1429 223 1.3759 - - - - - -
21.3506 224 0.0 - - - - - -
21.5584 225 0.0 - - - - - -
21.7662 226 0.0 - - - - - -
21.9740 227 0.0 - - - - - -
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102.1688 1104 0.2527 - - - - - -
102.3766 1105 0.0 - - - - - -
102.5844 1106 0.0 - - - - - -
102.7922 1107 0.0 - - - - - -
103.0 1108 0.0 - - - - - -
103.2078 1109 0.0 4.9040 0.0157 0.0129 0.0165 0.0119 0.0165
102.1558 1110 4.8664 - - - - - -
102.3636 1111 7.2066 - - - - - -
102.5714 1112 7.3654 - - - - - -
102.7792 1113 7.3292 - - - - - -
102.9870 1114 6.3064 - - - - - -
103.1948 1115 0.0578 - - - - - -
103.4026 1116 0.0 - - - - - -
103.6104 1117 0.0 - - - - - -
103.8182 1118 0.0 - - - - - -
104.0260 1119 0.0 - - - - - -
104.2338 1120 0.0 4.9041 0.0186 0.0100 0.0122 0.0102 0.0122
103.1818 1121 5.9061 - - - - - -
103.3896 1122 7.4147 - - - - - -
103.5974 1123 7.2213 - - - - - -
103.8052 1124 7.2518 - - - - - -
104.0130 1125 5.2947 - - - - - -
104.2208 1126 0.0086 - - - - - -
104.4286 1127 0.0 - - - - - -
104.6364 1128 0.0 - - - - - -
104.8442 1129 0.0 - - - - - -
105.0519 1130 0.0 - - - - - -
105.2597 1131 0.0 4.9044 0.0143 0.0114 0.0152 0.0107 0.0152
104.2078 1132 6.7405 - - - - - -
104.4156 1133 7.3468 - - - - - -
104.6234 1134 7.1923 - - - - - -
104.8312 1135 7.3162 - - - - - -
105.0390 1136 4.4502 - - - - - -
105.2468 1137 0.0 - - - - - -
105.4545 1138 0.0 - - - - - -
105.6623 1139 0.0 - - - - - -
105.8701 1140 0.0 - - - - - -
106.0779 1141 0.0 4.9045 0.0171 0.0099 0.0125 0.0103 0.0125
105.0260 1142 0.7863 - - - - - -
105.2338 1143 6.9397 - - - - - -
105.4416 1144 7.0708 - - - - - -
105.6494 1145 7.3346 - - - - - -
105.8571 1146 7.286 - - - - - -
106.0649 1147 3.4373 - - - - - -
106.2727 1148 0.0 - - - - - -
106.4805 1149 0.0 - - - - - -
106.6883 1150 0.0 - - - - - -
106.8961 1151 0.0 - - - - - -
107.1039 1152 0.0 4.9037 0.0139 0.0101 0.0138 0.0102 0.0138
106.0519 1153 1.5296 - - - - - -
106.2597 1154 7.1443 - - - - - -
106.4675 1155 7.2283 - - - - - -
106.6753 1156 7.3585 - - - - - -
106.8831 1157 7.2205 - - - - - -
107.0909 1158 2.5759 - - - - - -
107.2987 1159 0.0 - - - - - -
107.5065 1160 0.0 - - - - - -
107.7143 1161 0.0 - - - - - -
107.9221 1162 0.0 - - - - - -
108.1299 1163 0.0 4.9056 0.0170 0.0100 0.0125 0.0108 0.0125
107.0779 1164 2.4286 - - - - - -
107.2857 1165 7.1156 - - - - - -
107.4935 1166 7.2695 - - - - - -
107.7013 1167 7.2653 - - - - - -
107.9091 1168 7.2675 - - - - - -
108.1169 1169 1.7527 - - - - - -
108.3247 1170 0.0 - - - - - -
108.5325 1171 0.0 - - - - - -
108.7403 1172 0.0 - - - - - -
108.9481 1173 0.0 - - - - - -
109.1558 1174 0.0 4.9052 0.0137 0.0101 0.0129 0.0111 0.0129
108.1039 1175 3.2648 - - - - - -
108.3117 1176 7.1925 - - - - - -
108.5195 1177 7.5201 - - - - - -
108.7273 1178 7.4072 - - - - - -
108.9351 1179 7.0738 - - - - - -
109.1429 1180 0.9527 - - - - - -
109.3506 1181 0.0 - - - - - -
109.5584 1182 0.0 - - - - - -
109.7662 1183 0.0 - - - - - -
109.9740 1184 0.0 - - - - - -
110.1818 1185 0.0 4.9039 0.0168 0.0099 0.0139 0.0104 0.0139
109.1299 1186 4.0418 - - - - - -
109.3377 1187 7.1313 - - - - - -
109.5455 1188 7.4737 - - - - - -
109.7532 1189 7.3314 - - - - - -
109.9610 1190 6.8946 - - - - - -
110.1688 1191 0.1954 - - - - - -
110.3766 1192 0.0 - - - - - -
110.5844 1193 0.0 - - - - - -
110.7922 1194 0.0 - - - - - -
111.0 1195 0.0 - - - - - -
111.2078 1196 0.0 4.9053 0.0168 0.0099 0.0160 0.0096 0.0160
110.1558 1197 4.9106 - - - - - -
110.3636 1198 7.3166 - - - - - -
110.5714 1199 7.3645 - - - - - -
110.7792 1200 7.3088 4.9040 0.0168 0.0111 0.0160 0.0096 0.0160
  • 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: 0.32.0
  • 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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