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officialthinkingstack
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Sentence-Matcher-v0.5

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1554
loss:ContrastiveLoss
text-embeddings-inference
Model card Files
xet
 Community
How to use from the
Use from the
sentence-transformers library
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("officialthinkingstack/Sentence-Matcher-v0.5")

sentences = [
    "Patient reports increased joint pain, swelling, and morning stiffness associated with Rheumatoid Arthritis.\nThe patient describes worsening joint pain in the hands and knees, along with increased swelling and morning stiffness lasting over 60 minutes, over the past month.\nMusculoskeletal: Increased joint pain, swelling, morning stiffness.\nRheumatoid Arthritis, managed with disease-modifying antirheumatic drugs (DMARDs).\nWorsening symptoms in a patient with Rheumatoid Arthritis.",
    "Chronic Obstructive Pulmonary Disease With (Acute) Exacerbation",
    "Enteroviral Vesicular Stomatitis With Exanthem",
    "Unspecified Juvenile Rheumatoid Arthritis Of Unspecified Site"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'A 45-year-old male patient presented to the emergency department with a chief complaint of severe lower back pain.\nHe reports that the pain started suddenly about two hours ago while lifting a heavy object.\nThe pain is located in the lower back, radiates down the right leg, and is described as sharp and stabbing.\nHe also reports numbness and tingling in the right leg.\nMusculoskeletal: Severe lower back pain radiating down the right leg, numbness and tingling in the right leg\nMusculoskeletal: Limited range of motion in the lumbar spine due to pain.\nMuscle guarding is present in the paraspinal muscles.\nMotor strength and sensation are decreased in the right leg compared to the left.\nSuspected lumbar spine herniated disc\nX-rays of the lumbar spine to confirm the diagnosis\nMRI of the lumbar spine if X-rays are inconclusive\nNeurological consultation for further evaluation and management\nPain management with medication and physical therapy',
    'Other Intervertebral Disc Displacement, Lumbar Region',
    'Postconcussional Syndrome',
]
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]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 1,554 training samples
  • Columns: anchor, positive, and label
  • Approximate statistics based on the first 1000 samples:
    anchor positive label
    type string string float
    details
    • min: 3 tokens
    • mean: 83.3 tokens
    • max: 256 tokens
    • min: 3 tokens
    • mean: 10.12 tokens
    • max: 40 tokens
    • min: 1.0
    • mean: 1.0
    • max: 1.0
  • Samples:
    anchor positive label
    Worsening sciatic pain despite conservative treatment
    She reports that her sciatic pain has been worsening despite conservative treatment with rest, ice, over-the-counter pain relievers, and lumbar spine exercises.
    She says that the pain is now radiating down her entire leg, from her lower back to her foot.
    She also reports that she has numbness and tingling in her toes.
    The pain is worse when she sits or stands for long periods of time.
    Musculoskeletal: Worsening sciatic pain radiating down the entire leg
    Neurological: Numbness and tingling in the toes of the affected leg
    Exacerbation of sciatica    		 Sciatica, Unspecified Side 1.0
    Chief Complaint: Malaria
    Patient presents with fever, chills, and headache.
    Reports a sudden onset of symptoms after returning from a recent trip to a malaria-endemic region.
    Positive for fever and chills.
    Recent travel to a malaria-endemic area.
    Fever (38.5°C), chills, and generalized body aches.
    High suspicion for malaria infection.
    Initiate prompt diagnostic testing and antimalarial treatment.
    Blood smear for malaria parasites.
    Prescribe antimalarial medications based on species identification.
    Consideration of hospitalization if severe symptoms or complications arise.
    Follow-up in two weeks for reassessment and treatment response.
    Blood smear for Plasmodium species identification.
    Complete blood count (CBC) for assessment of anemia and thrombocytopenia.
    Liver function tests to assess for malaria-related complications.    		 Unspecified Malaria 1.0
    The patient returns for a follow-up visit after being diagnosed with psychosis.
    He reports partial improvement in symptoms with medication but notes persistent paranoia and difficulty concentrating.
    Partial improvement in sleep but persistent paranoia.
    Some reduction in auditory hallucinations, persistent racing thoughts.
    Ongoing diagnosis of psychosis and mood disorder.
    Observable signs of residual psychosis and disorganized thoughts.
    Partial improvement in psychosis.
    Adjust medication dosage or consider alternative medications.
    Continue individual therapy for ongoing management.
    Monitor for side effects and therapeutic response.
    Attend scheduled therapy sessions.
    Take prescribed medications as directed.
    Report any new or worsening symptoms promptly.    		 Psychotic Disorder With Delusions Due To Known Physiological Condition 1.0
  • Loss: ContrastiveLoss with these parameters:
    {
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • warmup_ratio: 0.1

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • 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: 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: 5
  • max_steps: -1
  • lr_scheduler_type: linear
  • 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: False
  • fp16: False
  • 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
  • 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: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss
0.0102 1 0.1624
0.0204 2 0.1369
0.0306 3 0.1151
0.0408 4 0.1031
0.0510 5 0.097
0.0612 6 0.095
0.0714 7 0.1236
0.0816 8 0.1177
0.0918 9 0.0931
0.1020 10 0.1049
0.1122 11 0.0757
0.1224 12 0.0936
0.1327 13 0.0797
0.1429 14 0.0855
0.1531 15 0.079
0.1633 16 0.0666
0.1735 17 0.073
0.1837 18 0.0669
0.1939 19 0.0517
0.2041 20 0.0667
0.2143 21 0.0639
0.2245 22 0.0729
0.2347 23 0.0565
0.2449 24 0.0501
0.2551 25 0.0596
0.2653 26 0.0478
0.2755 27 0.0306
0.2857 28 0.0509
0.2959 29 0.0415
0.3061 30 0.0396
0.3163 31 0.0215
0.3265 32 0.0402
0.3367 33 0.0692
0.3469 34 0.0602
0.3571 35 0.0215
0.3673 36 0.0274
0.3776 37 0.0212
0.3878 38 0.0231
0.3980 39 0.0159
0.4082 40 0.0154
0.4184 41 0.013
0.4286 42 0.0144
0.4388 43 0.0353
0.4490 44 0.0169
0.4592 45 0.0055
0.4694 46 0.0098
0.4796 47 0.0071
0.4898 48 0.0167
0.5 49 0.0062
0.5102 50 0.0064
0.5204 51 0.0125
0.5306 52 0.0044
0.5408 53 0.0193
0.5510 54 0.0058
0.5612 55 0.0043
0.5714 56 0.0036
0.5816 57 0.0018
0.5918 58 0.0039
0.6020 59 0.0031
0.6122 60 0.0019
0.6224 61 0.003
0.6327 62 0.003
0.6429 63 0.0039
0.6531 64 0.0048
0.6633 65 0.0013
0.6735 66 0.0039
0.6837 67 0.0113
0.6939 68 0.0042
0.7041 69 0.0029
0.7143 70 0.0014
0.7245 71 0.0012
0.7347 72 0.001
0.7449 73 0.0128
0.7551 74 0.0076
0.7653 75 0.0031
0.7755 76 0.0012
0.7857 77 0.0014
0.7959 78 0.0015
0.8061 79 0.0017
0.8163 80 0.0014
0.8265 81 0.0015
0.8367 82 0.0013
0.8469 83 0.001
0.8571 84 0.0021
0.8673 85 0.0008
0.8776 86 0.0009
0.8878 87 0.0117
0.8980 88 0.003
0.9082 89 0.0008
0.9184 90 0.0068
0.9286 91 0.0014
0.9388 92 0.0014
0.9490 93 0.0007
0.9592 94 0.0011
0.9694 95 0.0009
0.9796 96 0.0008
0.9898 97 0.0011
1.0 98 0.0011
1.0102 99 0.0005
1.0204 100 0.0005
1.0306 101 0.0012
1.0408 102 0.0008
1.0510 103 0.0016
1.0612 104 0.0005
1.0714 105 0.0015
1.0816 106 0.0005
1.0918 107 0.0018
1.1020 108 0.0006
1.1122 109 0.0006
1.1224 110 0.0043
1.1327 111 0.0007
1.1429 112 0.0009
1.1531 113 0.0007
1.1633 114 0.0019
1.1735 115 0.0032
1.1837 116 0.0004
1.1939 117 0.0005
1.2041 118 0.0005
1.2143 119 0.0009
1.2245 120 0.0018
1.2347 121 0.0006
1.2449 122 0.0004
1.2551 123 0.0004
1.2653 124 0.0008
1.2755 125 0.0007
1.2857 126 0.0006
1.2959 127 0.0004
1.3061 128 0.0032
1.3163 129 0.0011
1.3265 130 0.0008
1.3367 131 0.0006
1.3469 132 0.0004
1.3571 133 0.0005
1.3673 134 0.0003
1.3776 135 0.0006
1.3878 136 0.0009
1.3980 137 0.0003
1.4082 138 0.0003
1.4184 139 0.0005
1.4286 140 0.0005
1.4388 141 0.0005
1.4490 142 0.0006
1.4592 143 0.0022
1.4694 144 0.0004
1.4796 145 0.0012
1.4898 146 0.0006
1.5 147 0.0003
1.5102 148 0.0008
1.5204 149 0.0004
1.5306 150 0.0004
1.5408 151 0.0004
1.5510 152 0.0004
1.5612 153 0.0007
1.5714 154 0.0022
1.5816 155 0.0005
1.5918 156 0.0003
1.6020 157 0.0005
1.6122 158 0.0003
1.6224 159 0.0004
1.6327 160 0.0004
1.6429 161 0.0002
1.6531 162 0.0005
1.6633 163 0.0005
1.6735 164 0.0003
1.6837 165 0.0005
1.6939 166 0.0005
1.7041 167 0.0004
1.7143 168 0.0003
1.7245 169 0.0003
1.7347 170 0.0003
1.7449 171 0.0005
1.7551 172 0.0005
1.7653 173 0.0002
1.7755 174 0.0005
1.7857 175 0.0003
1.7959 176 0.0006
1.8061 177 0.0003
1.8163 178 0.0004
1.8265 179 0.0004
1.8367 180 0.0002
1.8469 181 0.0002
1.8571 182 0.0005
1.8673 183 0.0003
1.8776 184 0.0003
1.8878 185 0.0002
1.8980 186 0.0003
1.9082 187 0.0032
1.9184 188 0.0006
1.9286 189 0.0003
1.9388 190 0.0005
1.9490 191 0.0003
1.9592 192 0.0004
1.9694 193 0.0004
1.9796 194 0.0004
1.9898 195 0.0003
2.0 196 0.0001
2.0102 197 0.0003
2.0204 198 0.0003
2.0306 199 0.0002
2.0408 200 0.0002
2.0510 201 0.0003
2.0612 202 0.0002
2.0714 203 0.0002
2.0816 204 0.0003
2.0918 205 0.0003
2.1020 206 0.0008
2.1122 207 0.0004
2.1224 208 0.0004
2.1327 209 0.0004
2.1429 210 0.0003
2.1531 211 0.0004
2.1633 212 0.0002
2.1735 213 0.0002
2.1837 214 0.0002
2.1939 215 0.0002
2.2041 216 0.0002
2.2143 217 0.0003
2.2245 218 0.0004
2.2347 219 0.0003
2.2449 220 0.0002
2.2551 221 0.0002
2.2653 222 0.0003
2.2755 223 0.0002
2.2857 224 0.0003
2.2959 225 0.0002
2.3061 226 0.0003
2.3163 227 0.0002
2.3265 228 0.0004
2.3367 229 0.0002
2.3469 230 0.0002
2.3571 231 0.001
2.3673 232 0.0002
2.3776 233 0.0006
2.3878 234 0.0003
2.3980 235 0.0003
2.4082 236 0.0005
2.4184 237 0.0004
2.4286 238 0.0011
2.4388 239 0.0009
2.4490 240 0.0004
2.4592 241 0.0003
2.4694 242 0.0003
2.4796 243 0.0002
2.4898 244 0.0004
2.5 245 0.0002
2.5102 246 0.0002
2.5204 247 0.0004
2.5306 248 0.0003
2.5408 249 0.0002
2.5510 250 0.0006
2.5612 251 0.0002
2.5714 252 0.0002
2.5816 253 0.0002
2.5918 254 0.0002
2.6020 255 0.0013
2.6122 256 0.0002
2.6224 257 0.0012
2.6327 258 0.0003
2.6429 259 0.0002
2.6531 260 0.0003
2.6633 261 0.0002
2.6735 262 0.0011
2.6837 263 0.0003
2.6939 264 0.0003
2.7041 265 0.0004
2.7143 266 0.0003
2.7245 267 0.0001
2.7347 268 0.0002
2.7449 269 0.0002
2.7551 270 0.0003
2.7653 271 0.0002
2.7755 272 0.0002
2.7857 273 0.0002
2.7959 274 0.0004
2.8061 275 0.0002
2.8163 276 0.0003
2.8265 277 0.0002
2.8367 278 0.0002
2.8469 279 0.0004
2.8571 280 0.0004
2.8673 281 0.0004
2.8776 282 0.0002
2.8878 283 0.0002
2.8980 284 0.0004
2.9082 285 0.0002
2.9184 286 0.0002
2.9286 287 0.0004
2.9388 288 0.0003
2.9490 289 0.0002
2.9592 290 0.0006
2.9694 291 0.0002
2.9796 292 0.0003
2.9898 293 0.0003
3.0 294 0.0002
3.0102 295 0.0002
3.0204 296 0.0001
3.0306 297 0.0002
3.0408 298 0.0005
3.0510 299 0.0004
3.0612 300 0.0005
3.0714 301 0.0002
3.0816 302 0.0002
3.0918 303 0.0002
3.1020 304 0.0004
3.1122 305 0.0002
3.1224 306 0.0002
3.1327 307 0.0002
3.1429 308 0.0002
3.1531 309 0.0003
3.1633 310 0.0003
3.1735 311 0.0002
3.1837 312 0.0004
3.1939 313 0.0002
3.2041 314 0.0001
3.2143 315 0.0002
3.2245 316 0.0004
3.2347 317 0.0004
3.2449 318 0.0003
3.2551 319 0.0002
3.2653 320 0.0002
3.2755 321 0.0002
3.2857 322 0.0003
3.2959 323 0.0003
3.3061 324 0.0003
3.3163 325 0.0002
3.3265 326 0.0002
3.3367 327 0.0001
3.3469 328 0.0002
3.3571 329 0.0004
3.3673 330 0.0002
3.3776 331 0.0002
3.3878 332 0.0002
3.3980 333 0.0001
3.4082 334 0.0002
3.4184 335 0.0002
3.4286 336 0.0001
3.4388 337 0.0005
3.4490 338 0.0001
3.4592 339 0.0003
3.4694 340 0.0003
3.4796 341 0.0002
3.4898 342 0.0002
3.5 343 0.0001
3.5102 344 0.0002
3.5204 345 0.0008
3.5306 346 0.0002
3.5408 347 0.0003
3.5510 348 0.0003
3.5612 349 0.0003
3.5714 350 0.0002
3.5816 351 0.0002
3.5918 352 0.0002
3.6020 353 0.0001
3.6122 354 0.0002
3.6224 355 0.0001
3.6327 356 0.0002
3.6429 357 0.0001
3.6531 358 0.0001
3.6633 359 0.0003
3.6735 360 0.0003
3.6837 361 0.0002
3.6939 362 0.0002
3.7041 363 0.0001
3.7143 364 0.0003
3.7245 365 0.0003
3.7347 366 0.0002
3.7449 367 0.0006
3.7551 368 0.0003
3.7653 369 0.0002
3.7755 370 0.0002
3.7857 371 0.0001
3.7959 372 0.0002
3.8061 373 0.0002
3.8163 374 0.0003
3.8265 375 0.0001
3.8367 376 0.0002
3.8469 377 0.0004
3.8571 378 0.0002
3.8673 379 0.0003
3.8776 380 0.0001
3.8878 381 0.0003
3.8980 382 0.0001
3.9082 383 0.0002
3.9184 384 0.0002
3.9286 385 0.0002
3.9388 386 0.0003
3.9490 387 0.0002
3.9592 388 0.0002
3.9694 389 0.0001
3.9796 390 0.0002
3.9898 391 0.0001
4.0 392 0.0001
4.0102 393 0.0001
4.0204 394 0.0002
4.0306 395 0.0001
4.0408 396 0.0007
4.0510 397 0.0002
4.0612 398 0.0002
4.0714 399 0.0001
4.0816 400 0.0001
4.0918 401 0.0002
4.1020 402 0.0002
4.1122 403 0.0001
4.1224 404 0.0001
4.1327 405 0.0002
4.1429 406 0.0004
4.1531 407 0.0004
4.1633 408 0.0006
4.1735 409 0.0001
4.1837 410 0.0002
4.1939 411 0.0002
4.2041 412 0.0001
4.2143 413 0.0001
4.2245 414 0.0001
4.2347 415 0.0001
4.2449 416 0.0003
4.2551 417 0.0001
4.2653 418 0.0002
4.2755 419 0.0001
4.2857 420 0.0002
4.2959 421 0.0003
4.3061 422 0.0004
4.3163 423 0.0002
4.3265 424 0.0003
4.3367 425 0.0001
4.3469 426 0.0001
4.3571 427 0.0002
4.3673 428 0.0002
4.3776 429 0.0002
4.3878 430 0.0002
4.3980 431 0.0002
4.4082 432 0.0001
4.4184 433 0.0003
4.4286 434 0.0002
4.4388 435 0.0003
4.4490 436 0.0003
4.4592 437 0.0003
4.4694 438 0.0001
4.4796 439 0.0002
4.4898 440 0.0002
4.5 441 0.0002
4.5102 442 0.0003
4.5204 443 0.0003
4.5306 444 0.0002
4.5408 445 0.0002
4.5510 446 0.0001
4.5612 447 0.0002
4.5714 448 0.0002
4.5816 449 0.0001
4.5918 450 0.0002
4.6020 451 0.0002
4.6122 452 0.0001
4.6224 453 0.0003
4.6327 454 0.0002
4.6429 455 0.0001
4.6531 456 0.0001
4.6633 457 0.0001
4.6735 458 0.0001
4.6837 459 0.0002
4.6939 460 0.0001
4.7041 461 0.0002
4.7143 462 0.0001
4.7245 463 0.0001
4.7347 464 0.0002
4.7449 465 0.0002
4.7551 466 0.0001
4.7653 467 0.0002
4.7755 468 0.0002
4.7857 469 0.0002
4.7959 470 0.0002
4.8061 471 0.0007
4.8163 472 0.0002
4.8265 473 0.0006
4.8367 474 0.0002
4.8469 475 0.0001
4.8571 476 0.0002
4.8673 477 0.0001
4.8776 478 0.0002
4.8878 479 0.0002
4.8980 480 0.0003
4.9082 481 0.0002
4.9184 482 0.0001
4.9286 483 0.0002
4.9388 484 0.0002
4.9490 485 0.0002
4.9592 486 0.0002
4.9694 487 0.0002
4.9796 488 0.0002
4.9898 489 0.0004
5.0 490 0.0002

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 4.1.0
  • Transformers: 4.52.3
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.7.0
  • Datasets: 2.14.4
  • Tokenizers: 0.21.1

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",
}

ContrastiveLoss 

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
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