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SQAI
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temporary_embedding_2

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1041
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 BAAI/bge-small-en-v1.5. 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: BAAI/bge-small-en-v1.5
  • 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/temporary_embedding_2")
# Run inference
sentences = [
    'All failures listed',
    "failure count for street = 'Birch Avenue', 'Maple Drive' for time = last 30 days in streetlighting in streetlighting",
    '"What is the frequency of streetlight activations, alongside instances of lower than expected power consumption or network disconnection, within the designated grid height over the last quarter?"',
]
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 0.0086
cosine_accuracy@5 0.0172
cosine_accuracy@10 0.1121
cosine_precision@1 0.0
cosine_precision@3 0.0029
cosine_precision@5 0.0034
cosine_precision@10 0.0112
cosine_recall@1 0.0
cosine_recall@3 0.0086
cosine_recall@5 0.0172
cosine_recall@10 0.1121
cosine_ndcg@10 0.0391
cosine_mrr@10 0.0181
cosine_map@100 0.0317

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0086
cosine_accuracy@5 0.0172
cosine_accuracy@10 0.1121
cosine_precision@1 0.0
cosine_precision@3 0.0029
cosine_precision@5 0.0034
cosine_precision@10 0.0112
cosine_recall@1 0.0
cosine_recall@3 0.0086
cosine_recall@5 0.0172
cosine_recall@10 0.1121
cosine_ndcg@10 0.0391
cosine_mrr@10 0.0181
cosine_map@100 0.0317

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0086
cosine_accuracy@5 0.0172
cosine_accuracy@10 0.1034
cosine_precision@1 0.0
cosine_precision@3 0.0029
cosine_precision@5 0.0034
cosine_precision@10 0.0103
cosine_recall@1 0.0
cosine_recall@3 0.0086
cosine_recall@5 0.0172
cosine_recall@10 0.1034
cosine_ndcg@10 0.0359
cosine_mrr@10 0.0165
cosine_map@100 0.0305

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0086
cosine_accuracy@5 0.0172
cosine_accuracy@10 0.0776
cosine_precision@1 0.0
cosine_precision@3 0.0029
cosine_precision@5 0.0034
cosine_precision@10 0.0078
cosine_recall@1 0.0
cosine_recall@3 0.0086
cosine_recall@5 0.0172
cosine_recall@10 0.0776
cosine_ndcg@10 0.0284
cosine_mrr@10 0.0142
cosine_map@100 0.0278

Information Retrieval

Metric Value
cosine_accuracy@1 0.0
cosine_accuracy@3 0.0
cosine_accuracy@5 0.0
cosine_accuracy@10 0.0603
cosine_precision@1 0.0
cosine_precision@3 0.0
cosine_precision@5 0.0
cosine_precision@10 0.006
cosine_recall@1 0.0
cosine_recall@3 0.0
cosine_recall@5 0.0
cosine_recall@10 0.0603
cosine_ndcg@10 0.0192
cosine_mrr@10 0.0078
cosine_map@100 0.02

Training Details

Training Dataset

Unnamed Dataset

  • Size: 1,041 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 11.39 tokens
    • max: 20 tokens
    • min: 9 tokens
    • mean: 46.15 tokens
    • max: 116 tokens
  • Samples:
    positive anchor
    Default dimming level set for the streetlight "What is the preset dimming level for the streetlight?"
    Internal temperature of the streetlight, indicating operating conditions "Could you provide an overview of streetlight performance issues including power load inefficiencies, temperature risk occurrences, and voltage abnormalities over the past three months?"
    unique streetlight identifier "What is the frequency of voltage overflow or unstable flashing incidents in the Lamplighter streetlight group over the last quarter, and could you list the identifiers for the streetlights involved along with the minimum longitude of the area? Also, how often did the lux level fall below supplementary lighting threshold?"
  • 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: 116 evaluation samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 10.83 tokens
    • max: 20 tokens
    • min: 12 tokens
    • mean: 44.14 tokens
    • max: 116 tokens
  • Samples:
    positive anchor
    Timestamp of the recorded streetlighting error (failure) "What has been the frequency and timing of operating temperature failures within the 'Main Street' cluster of streetlights, also noting any incidents of potential overloading? Additionally, please provide count details of how often the streetlights have been switched on, while specifying their unique identifiers for better tracking."
    All failures listed "What is the current malfunction rate for the streetlights on a specified street?"
    Interval at which the streetlight sends data reports "Can you provide an overview on the recent performance of the streetlight with IMEI number, including average power output, issues with control link mechanism, load overages, and abnormal operating temperature across the geographical area of defined latitude and longitude, keeping the latest actions performed into account for the data interval?"
  • 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: 700
  • 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: 700
  • 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
0.4848 1 11.7245 - - - - - -
0.9697 2 6.2069 - - - - - -
1.4545 3 0.0 - - - - - -
1.9394 4 0.0 - - - - - -
2.4242 5 0.0 - - - - - -
2.9091 6 0.0 - - - - - -
3.3939 7 0.0 - - - - - -
3.8788 8 0.0 - - - - - -
4.3636 9 0.0 - - - - - -
4.8485 10 0.0 - - - - - -
5.3333 11 0.0 - - - - - -
5.8182 12 0.0 - - - - - -
6.3030 13 0.0 - - - - - -
6.7879 14 0.0 - - - - - -
7.2727 15 0.0 - - - - - -
7.7576 16 0.0 - - - - - -
8.2424 17 0.0 - - - - - -
8.7273 18 0.0 0.9350 0.0160 0.0137 0.0164 0.0148 0.0164
1.2121 19 5.0896 - - - - - -
1.6970 20 11.2565 - - - - - -
2.1818 21 1.0568 - - - - - -
2.6667 22 0.0 - - - - - -
3.1515 23 0.0 - - - - - -
3.6364 24 0.0 - - - - - -
4.1212 25 0.0 - - - - - -
4.6061 26 0.0 - - - - - -
5.0909 27 0.0 - - - - - -
5.5758 28 0.0 - - - - - -
6.0606 29 0.0 - - - - - -
6.5455 30 0.0 - - - - - -
7.0303 31 0.0 - - - - - -
7.5152 32 0.0 - - - - - -
8.0 33 0.0 - - - - - -
8.4848 34 0.0 - - - - - -
8.9697 35 0.0 - - - - - -
9.4545 36 0.0 - - - - - -
9.9394 37 0.0 0.9363 0.0187 0.0159 0.0202 0.0183 0.0202
2.4242 38 10.0087 - - - - - -
2.9091 39 7.3492 - - - - - -
3.3939 40 0.0 - - - - - -
3.8788 41 0.0 - - - - - -
4.3636 42 0.0 - - - - - -
4.8485 43 0.0 - - - - - -
5.3333 44 0.0 - - - - - -
5.8182 45 0.0 - - - - - -
6.3030 46 0.0 - - - - - -
6.7879 47 0.0 - - - - - -
7.2727 48 0.0 - - - - - -
7.7576 49 0.0 - - - - - -
8.2424 50 0.0 - - - - - -
8.7273 51 0.0 - - - - - -
9.2121 52 0.0 - - - - - -
9.6970 53 0.0 - - - - - -
10.1818 54 0.0 - - - - - -
10.6667 55 0.0 0.9337 0.0189 0.0175 0.0201 0.0184 0.0201
3.1515 56 3.5405 - - - - - -
3.6364 57 11.2906 - - - - - -
4.1212 58 2.5817 - - - - - -
4.6061 59 0.0 - - - - - -
5.0909 60 0.0 - - - - - -
5.5758 61 0.0 - - - - - -
6.0606 62 0.0 - - - - - -
6.5455 63 0.0 - - - - - -
7.0303 64 0.0 - - - - - -
7.5152 65 0.0 - - - - - -
8.0 66 0.0 - - - - - -
8.4848 67 0.0 - - - - - -
8.9697 68 0.0 - - - - - -
9.4545 69 0.0 - - - - - -
9.9394 70 0.0 - - - - - -
10.4242 71 0.0 - - - - - -
10.9091 72 0.0 - - - - - -
11.3939 73 0.0 - - - - - -
11.8788 74 0.0 0.9316 0.0191 0.0175 0.0201 0.0147 0.0201
4.3636 75 8.5848 - - - - - -
4.8485 76 8.7169 - - - - - -
5.3333 77 0.0 - - - - - -
5.8182 78 0.0 - - - - - -
6.3030 79 0.0 - - - - - -
6.7879 80 0.0 - - - - - -
7.2727 81 0.0 - - - - - -
7.7576 82 0.0 - - - - - -
8.2424 83 0.0 - - - - - -
8.7273 84 0.0 - - - - - -
9.2121 85 0.0 - - - - - -
9.6970 86 0.0 - - - - - -
10.1818 87 0.0 - - - - - -
10.6667 88 0.0 - - - - - -
11.1515 89 0.0 - - - - - -
11.6364 90 0.0 - - - - - -
12.1212 91 0.0 - - - - - -
12.6061 92 0.0 0.9292 0.0163 0.0161 0.0174 0.0157 0.0174
5.0909 93 2.1174 - - - - - -
5.5758 94 11.5135 - - - - - -
6.0606 95 3.8687 - - - - - -
6.5455 96 0.0 - - - - - -
7.0303 97 0.0 - - - - - -
7.5152 98 0.0 - - - - - -
8.0 99 0.0 - - - - - -
8.4848 100 0.0 - - - - - -
8.9697 101 0.0 - - - - - -
9.4545 102 0.0 - - - - - -
9.9394 103 0.0 - - - - - -
10.4242 104 0.0 - - - - - -
10.9091 105 0.0 - - - - - -
11.3939 106 0.0 - - - - - -
11.8788 107 0.0 - - - - - -
12.3636 108 0.0 - - - - - -
12.8485 109 0.0 - - - - - -
13.3333 110 0.0 - - - - - -
13.8182 111 0.0 0.9275 0.0163 0.0161 0.0174 0.0177 0.0174
6.3030 112 7.1418 - - - - - -
6.7879 113 10.1515 - - - - - -
7.2727 114 0.0632 - - - - - -
7.7576 115 0.0 - - - - - -
8.2424 116 0.0 - - - - - -
8.7273 117 0.0 - - - - - -
9.2121 118 0.0 - - - - - -
9.6970 119 0.0 - - - - - -
10.1818 120 0.0 - - - - - -
10.6667 121 0.0 - - - - - -
11.1515 122 0.0 - - - - - -
11.6364 123 0.0 - - - - - -
12.1212 124 0.0 - - - - - -
12.6061 125 0.0 - - - - - -
13.0909 126 0.0 - - - - - -
13.5758 127 0.0 - - - - - -
14.0606 128 0.0 - - - - - -
14.5455 129 0.0 0.9221 0.0168 0.0235 0.0171 0.0153 0.0171
7.0303 130 0.6962 - - - - - -
7.5152 131 11.3223 - - - - - -
8.0 132 5.3211 - - - - - -
8.4848 133 0.0 - - - - - -
8.9697 134 0.0 - - - - - -
9.4545 135 0.0 - - - - - -
9.9394 136 0.0 - - - - - -
10.4242 137 0.0 - - - - - -
10.9091 138 0.0 - - - - - -
11.3939 139 0.0 - - - - - -
11.8788 140 0.0 - - - - - -
12.3636 141 0.0 - - - - - -
12.8485 142 0.0 - - - - - -
13.3333 143 0.0 - - - - - -
13.8182 144 0.0 - - - - - -
14.3030 145 0.0 - - - - - -
14.7879 146 0.0 - - - - - -
15.2727 147 0.0 - - - - - -
15.7576 148 0.0 0.9167 0.0168 0.0163 0.0172 0.0153 0.0172
8.2424 149 5.74 - - - - - -
8.7273 150 10.9365 - - - - - -
9.2121 151 0.4582 - - - - - -
9.6970 152 0.0 - - - - - -
10.1818 153 0.0 - - - - - -
10.6667 154 0.0 - - - - - -
11.1515 155 0.0 - - - - - -
11.6364 156 0.0 - - - - - -
12.1212 157 0.0 - - - - - -
12.6061 158 0.0 - - - - - -
13.0909 159 0.0 - - - - - -
13.5758 160 0.0 - - - - - -
14.0606 161 0.0 - - - - - -
14.5455 162 0.0 - - - - - -
15.0303 163 0.0 - - - - - -
15.5152 164 0.0 - - - - - -
16.0 165 0.0 - - - - - -
16.4848 166 0.0 - - - - - -
16.9697 167 0.0 0.9094 0.0170 0.0165 0.0175 0.0158 0.0175
9.4545 168 10.5934 - - - - - -
9.9394 169 6.5701 - - - - - -
10.4242 170 0.0 - - - - - -
10.9091 171 0.0 - - - - - -
11.3939 172 0.0 - - - - - -
11.8788 173 0.0 - - - - - -
12.3636 174 0.0 - - - - - -
12.8485 175 0.0 - - - - - -
13.3333 176 0.0 - - - - - -
13.8182 177 0.0 - - - - - -
14.3030 178 0.0 - - - - - -
14.7879 179 0.0 - - - - - -
15.2727 180 0.0 - - - - - -
15.7576 181 0.0 - - - - - -
16.2424 182 0.0 - - - - - -
16.7273 183 0.0 - - - - - -
17.2121 184 0.0 - - - - - -
17.6970 185 0.0 0.9006 0.0170 0.0257 0.0209 0.0161 0.0209
10.1818 186 4.2213 - - - - - -
10.6667 187 10.9549 - - - - - -
11.1515 188 1.6799 - - - - - -
11.6364 189 0.0 - - - - - -
12.1212 190 0.0 - - - - - -
12.6061 191 0.0 - - - - - -
13.0909 192 0.0 - - - - - -
13.5758 193 0.0 - - - - - -
14.0606 194 0.0 - - - - - -
14.5455 195 0.0 - - - - - -
15.0303 196 0.0 - - - - - -
15.5152 197 0.0 - - - - - -
16.0 198 0.0 - - - - - -
16.4848 199 0.0 - - - - - -
16.9697 200 0.0 - - - - - -
17.4545 201 0.0 - - - - - -
17.9394 202 0.0 - - - - - -
18.4242 203 0.0 - - - - - -
18.9091 204 0.0 0.8925 0.0205 0.0164 0.0214 0.0193 0.0214
11.3939 205 8.9817 - - - - - -
11.8788 206 7.7949 - - - - - -
12.3636 207 0.0 - - - - - -
12.8485 208 0.0 - - - - - -
13.3333 209 0.0 - - - - - -
13.8182 210 0.0 - - - - - -
14.3030 211 0.0 - - - - - -
14.7879 212 0.0 - - - - - -
15.2727 213 0.0 - - - - - -
15.7576 214 0.0 - - - - - -
16.2424 215 0.0 - - - - - -
16.7273 216 0.0 - - - - - -
17.2121 217 0.0 - - - - - -
17.6970 218 0.0 - - - - - -
18.1818 219 0.0 - - - - - -
18.6667 220 0.0 - - - - - -
19.1515 221 0.0 - - - - - -
19.6364 222 0.0 0.8853 0.0176 0.0188 0.0215 0.0192 0.0215
12.1212 223 2.751 - - - - - -
12.6061 224 10.9894 - - - - - -
13.0909 225 2.9355 - - - - - -
13.5758 226 0.0 - - - - - -
14.0606 227 0.0 - - - - - -
14.5455 228 0.0 - - - - - -
15.0303 229 0.0 - - - - - -
15.5152 230 0.0 - - - - - -
16.0 231 0.0 - - - - - -
16.4848 232 0.0 - - - - - -
16.9697 233 0.0 - - - - - -
17.4545 234 0.0 - - - - - -
17.9394 235 0.0 - - - - - -
18.4242 236 0.0 - - - - - -
18.9091 237 0.0 - - - - - -
19.3939 238 0.0 - - - - - -
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73.4545 1356 7.8721 - - - - - -
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74.1818 1374 3.2039 - - - - - -
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81.4545 1389 0.0 - - - - - -
81.9394 1390 0.0 - - - - - -
82.4242 1391 0.0 - - - - - -
82.9091 1392 0.0 0.7448 0.0299 0.0322 0.0332 0.0200 0.0332
75.3939 1393 6.9415 - - - - - -
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76.3636 1395 0.0 - - - - - -
76.8485 1396 0.0 - - - - - -
77.3333 1397 0.0 - - - - - -
77.8182 1398 0.0 - - - - - -
78.3030 1399 0.0 - - - - - -
78.7879 1400 0.0 0.7458 0.0278 0.0305 0.0317 0.0200 0.0317
  • 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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