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redis
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langcache-reranker-v1-miniL6-softmnrl-triplet

Text Ranking
sentence-transformers
Safetensors
English
bert
cross-encoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:1452533
loss:MultipleNegativesRankingLoss
Eval Results
Model card Files
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Community
adbaral commited on
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Add new CrossEncoder model

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Files changed (2) hide show
  1. README.md +588 -0
  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,6 +19,45 @@ datasets:
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  - redis/langcache-sentencepairs-v1
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  pipeline_tag: text-ranking
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  library_name: sentence-transformers
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22
  ---
23
 
24
  # Redis fine-tuned CrossEncoder model for semantic caching on LangCache
@@ -110,6 +149,25 @@ You can finetune this model on your own dataset.
110
  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
111
  -->
112
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
113
  <!--
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  ## Bias, Risks and Limitations
115
 
@@ -178,6 +236,536 @@ You can finetune this model on your own dataset.
178
  }
179
  ```
180
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
181
  ### Framework Versions
182
  - Python: 3.12.3
183
  - Sentence Transformers: 5.1.0
 
19
  - redis/langcache-sentencepairs-v1
20
  pipeline_tag: text-ranking
21
  library_name: sentence-transformers
22
+ metrics:
23
+ - accuracy
24
+ - accuracy_threshold
25
+ - f1
26
+ - f1_threshold
27
+ - precision
28
+ - recall
29
+ - average_precision
30
+ model-index:
31
+ - name: Redis fine-tuned CrossEncoder model for semantic caching on LangCache
32
+ results:
33
+ - task:
34
+ type: cross-encoder-classification
35
+ name: Cross Encoder Classification
36
+ dataset:
37
+ name: test cls
38
+ type: test_cls
39
+ metrics:
40
+ - type: accuracy
41
+ value: 0.82683284693894
42
+ name: Accuracy
43
+ - type: accuracy_threshold
44
+ value: -0.0703125
45
+ name: Accuracy Threshold
46
+ - type: f1
47
+ value: 0.8097739600622851
48
+ name: F1
49
+ - type: f1_threshold
50
+ value: -0.27734375
51
+ name: F1 Threshold
52
+ - type: precision
53
+ value: 0.7490107942135419
54
+ name: Precision
55
+ - type: recall
56
+ value: 0.881266294227188
57
+ name: Recall
58
+ - type: average_precision
59
+ value: 0.8720293693345842
60
+ name: Average Precision
61
  ---
62
 
63
  # Redis fine-tuned CrossEncoder model for semantic caching on LangCache
 
149
  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
150
  -->
151
 
152
+ ## Evaluation
153
+
154
+ ### Metrics
155
+
156
+ #### Cross Encoder Classification
157
+
158
+ * Dataset: `test_cls`
159
+ * Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
160
+
161
+ | Metric | Value |
162
+ |:----------------------|:----------|
163
+ | accuracy | 0.8268 |
164
+ | accuracy_threshold | -0.0703 |
165
+ | f1 | 0.8098 |
166
+ | f1_threshold | -0.2773 |
167
+ | precision | 0.749 |
168
+ | recall | 0.8813 |
169
+ | **average_precision** | **0.872** |
170
+
171
  <!--
172
  ## Bias, Risks and Limitations
173
 
 
236
  }
237
  ```
238
 
239
+ ### Training Hyperparameters
240
+ #### Non-Default Hyperparameters
241
+
242
+ - `eval_strategy`: steps
243
+ - `per_device_train_batch_size`: 48
244
+ - `per_device_eval_batch_size`: 48
245
+ - `learning_rate`: 0.0002
246
+ - `weight_decay`: 0.001
247
+ - `num_train_epochs`: 50
248
+ - `warmup_ratio`: 0.1
249
+ - `load_best_model_at_end`: True
250
+ - `optim`: adamw_torch
251
+ - `ddp_find_unused_parameters`: False
252
+ - `push_to_hub`: True
253
+ - `hub_model_id`: redis/langcache-reranker-v1-miniL6-softmnrl-triplet
254
+ - `eval_on_start`: True
255
+ - `batch_sampler`: no_duplicates
256
+
257
+ #### All Hyperparameters
258
+ <details><summary>Click to expand</summary>
259
+
260
+ - `overwrite_output_dir`: False
261
+ - `do_predict`: False
262
+ - `eval_strategy`: steps
263
+ - `prediction_loss_only`: True
264
+ - `per_device_train_batch_size`: 48
265
+ - `per_device_eval_batch_size`: 48
266
+ - `per_gpu_train_batch_size`: None
267
+ - `per_gpu_eval_batch_size`: None
268
+ - `gradient_accumulation_steps`: 1
269
+ - `eval_accumulation_steps`: None
270
+ - `torch_empty_cache_steps`: None
271
+ - `learning_rate`: 0.0002
272
+ - `weight_decay`: 0.001
273
+ - `adam_beta1`: 0.9
274
+ - `adam_beta2`: 0.999
275
+ - `adam_epsilon`: 1e-08
276
+ - `max_grad_norm`: 1.0
277
+ - `num_train_epochs`: 50
278
+ - `max_steps`: -1
279
+ - `lr_scheduler_type`: linear
280
+ - `lr_scheduler_kwargs`: {}
281
+ - `warmup_ratio`: 0.1
282
+ - `warmup_steps`: 0
283
+ - `log_level`: passive
284
+ - `log_level_replica`: warning
285
+ - `log_on_each_node`: True
286
+ - `logging_nan_inf_filter`: True
287
+ - `save_safetensors`: True
288
+ - `save_on_each_node`: False
289
+ - `save_only_model`: False
290
+ - `restore_callback_states_from_checkpoint`: False
291
+ - `no_cuda`: False
292
+ - `use_cpu`: False
293
+ - `use_mps_device`: False
294
+ - `seed`: 42
295
+ - `data_seed`: None
296
+ - `jit_mode_eval`: False
297
+ - `use_ipex`: False
298
+ - `bf16`: False
299
+ - `fp16`: False
300
+ - `fp16_opt_level`: O1
301
+ - `half_precision_backend`: auto
302
+ - `bf16_full_eval`: False
303
+ - `fp16_full_eval`: False
304
+ - `tf32`: None
305
+ - `local_rank`: 2
306
+ - `ddp_backend`: None
307
+ - `tpu_num_cores`: None
308
+ - `tpu_metrics_debug`: False
309
+ - `debug`: []
310
+ - `dataloader_drop_last`: True
311
+ - `dataloader_num_workers`: 0
312
+ - `dataloader_prefetch_factor`: None
313
+ - `past_index`: -1
314
+ - `disable_tqdm`: False
315
+ - `remove_unused_columns`: True
316
+ - `label_names`: None
317
+ - `load_best_model_at_end`: True
318
+ - `ignore_data_skip`: False
319
+ - `fsdp`: []
320
+ - `fsdp_min_num_params`: 0
321
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
322
+ - `fsdp_transformer_layer_cls_to_wrap`: None
323
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
324
+ - `parallelism_config`: None
325
+ - `deepspeed`: None
326
+ - `label_smoothing_factor`: 0.0
327
+ - `optim`: adamw_torch
328
+ - `optim_args`: None
329
+ - `adafactor`: False
330
+ - `group_by_length`: False
331
+ - `length_column_name`: length
332
+ - `ddp_find_unused_parameters`: False
333
+ - `ddp_bucket_cap_mb`: None
334
+ - `ddp_broadcast_buffers`: False
335
+ - `dataloader_pin_memory`: True
336
+ - `dataloader_persistent_workers`: False
337
+ - `skip_memory_metrics`: True
338
+ - `use_legacy_prediction_loop`: False
339
+ - `push_to_hub`: True
340
+ - `resume_from_checkpoint`: None
341
+ - `hub_model_id`: redis/langcache-reranker-v1-miniL6-softmnrl-triplet
342
+ - `hub_strategy`: every_save
343
+ - `hub_private_repo`: None
344
+ - `hub_always_push`: False
345
+ - `hub_revision`: None
346
+ - `gradient_checkpointing`: False
347
+ - `gradient_checkpointing_kwargs`: None
348
+ - `include_inputs_for_metrics`: False
349
+ - `include_for_metrics`: []
350
+ - `eval_do_concat_batches`: True
351
+ - `fp16_backend`: auto
352
+ - `push_to_hub_model_id`: None
353
+ - `push_to_hub_organization`: None
354
+ - `mp_parameters`:
355
+ - `auto_find_batch_size`: False
356
+ - `full_determinism`: False
357
+ - `torchdynamo`: None
358
+ - `ray_scope`: last
359
+ - `ddp_timeout`: 1800
360
+ - `torch_compile`: False
361
+ - `torch_compile_backend`: None
362
+ - `torch_compile_mode`: None
363
+ - `include_tokens_per_second`: False
364
+ - `include_num_input_tokens_seen`: False
365
+ - `neftune_noise_alpha`: None
366
+ - `optim_target_modules`: None
367
+ - `batch_eval_metrics`: False
368
+ - `eval_on_start`: True
369
+ - `use_liger_kernel`: False
370
+ - `liger_kernel_config`: None
371
+ - `eval_use_gather_object`: False
372
+ - `average_tokens_across_devices`: True
373
+ - `prompts`: None
374
+ - `batch_sampler`: no_duplicates
375
+ - `multi_dataset_batch_sampler`: proportional
376
+ - `router_mapping`: {}
377
+ - `learning_rate_mapping`: {}
378
+
379
+ </details>
380
+
381
+ ### Training Logs
382
+ <details><summary>Click to expand</summary>
383
+
384
+ | Epoch | Step | Training Loss | Validation Loss | test_cls_average_precision |
385
+ |:----------:|:---------:|:-------------:|:---------------:|:--------------------------:|
386
+ | 0 | 0 | - | 0.3223 | 0.5734 |
387
+ | 0.1322 | 1000 | 0.4286 | 0.3215 | 0.5735 |
388
+ | 0.2644 | 2000 | 0.4241 | 0.3151 | 0.5743 |
389
+ | 0.3966 | 3000 | 0.4182 | 0.3038 | 0.5759 |
390
+ | 0.5288 | 4000 | 0.4036 | 0.2876 | 0.5782 |
391
+ | 0.6609 | 5000 | 0.3919 | 0.2619 | 0.5830 |
392
+ | 0.7931 | 6000 | 0.3694 | 0.2290 | 0.5914 |
393
+ | 0.9253 | 7000 | 0.3481 | 0.1966 | 0.6029 |
394
+ | 1.0575 | 8000 | 0.3109 | 0.1650 | 0.6253 |
395
+ | 1.1897 | 9000 | 0.2665 | 0.1384 | 0.6591 |
396
+ | 1.3219 | 10000 | 0.2281 | 0.1154 | 0.6921 |
397
+ | 1.4541 | 11000 | 0.1984 | 0.0928 | 0.7189 |
398
+ | 1.5863 | 12000 | 0.1794 | 0.0814 | 0.7287 |
399
+ | 1.7184 | 13000 | 0.1619 | 0.0698 | 0.7398 |
400
+ | 1.8506 | 14000 | 0.1498 | 0.0619 | 0.7506 |
401
+ | 1.9828 | 15000 | 0.1409 | 0.0581 | 0.7653 |
402
+ | 2.1150 | 16000 | 0.1315 | 0.0537 | 0.7760 |
403
+ | 2.2472 | 17000 | 0.1239 | 0.0495 | 0.7809 |
404
+ | 2.3794 | 18000 | 0.1157 | 0.0471 | 0.7804 |
405
+ | 2.5116 | 19000 | 0.1093 | 0.0415 | 0.7912 |
406
+ | 2.6438 | 20000 | 0.1026 | 0.0428 | 0.8006 |
407
+ | 2.7759 | 21000 | 0.0958 | 0.0393 | 0.8013 |
408
+ | 2.9081 | 22000 | 0.0922 | 0.0387 | 0.8152 |
409
+ | 3.0403 | 23000 | 0.0873 | 0.0415 | 0.8117 |
410
+ | 3.1725 | 24000 | 0.0823 | 0.0382 | 0.8130 |
411
+ | 3.3047 | 25000 | 0.0807 | 0.0369 | 0.8141 |
412
+ | 3.4369 | 26000 | 0.0772 | 0.0370 | 0.8275 |
413
+ | 3.5691 | 27000 | 0.0734 | 0.0348 | 0.8197 |
414
+ | 3.7013 | 28000 | 0.0709 | 0.0335 | 0.8242 |
415
+ | 3.8334 | 29000 | 0.067 | 0.0363 | 0.8309 |
416
+ | **3.9656** | **30000** | **0.0675** | **0.0359** | **0.8327** |
417
+ | 4.0978 | 31000 | 0.0629 | 0.0337 | 0.8413 |
418
+ | 4.2300 | 32000 | 0.0611 | 0.0350 | 0.8418 |
419
+ | 4.3622 | 33000 | 0.0618 | 0.0372 | 0.8415 |
420
+ | 4.4944 | 34000 | 0.0585 | 0.0341 | 0.8437 |
421
+ | 4.6266 | 35000 | 0.0569 | 0.0364 | 0.8473 |
422
+ | 4.7588 | 36000 | 0.055 | 0.0355 | 0.8430 |
423
+ | 4.8909 | 37000 | 0.0529 | 0.0316 | 0.8468 |
424
+ | 5.0231 | 38000 | 0.0522 | 0.0346 | 0.8454 |
425
+ | 5.1553 | 39000 | 0.0501 | 0.0384 | 0.8493 |
426
+ | 5.2875 | 40000 | 0.0503 | 0.0345 | 0.8527 |
427
+ | 5.4197 | 41000 | 0.0487 | 0.0321 | 0.8542 |
428
+ | 5.5519 | 42000 | 0.0465 | 0.0321 | 0.8475 |
429
+ | 5.6841 | 43000 | 0.0453 | 0.0316 | 0.8487 |
430
+ | 5.8163 | 44000 | 0.0426 | 0.0355 | 0.8554 |
431
+ | 5.9484 | 45000 | 0.043 | 0.0329 | 0.8564 |
432
+ | 6.0806 | 46000 | 0.0405 | 0.0358 | 0.8513 |
433
+ | 6.2128 | 47000 | 0.0398 | 0.0345 | 0.8578 |
434
+ | 6.3450 | 48000 | 0.0406 | 0.0336 | 0.8605 |
435
+ | 6.4772 | 49000 | 0.0381 | 0.0324 | 0.8535 |
436
+ | 6.6094 | 50000 | 0.0377 | 0.0322 | 0.8522 |
437
+ | 6.7416 | 51000 | 0.0357 | 0.0321 | 0.8541 |
438
+ | 6.8738 | 52000 | 0.035 | 0.0338 | 0.8633 |
439
+ | 7.0059 | 53000 | 0.035 | 0.0348 | 0.8627 |
440
+ | 7.1381 | 54000 | 0.033 | 0.0341 | 0.8650 |
441
+ | 7.2703 | 55000 | 0.0347 | 0.0341 | 0.8621 |
442
+ | 7.4025 | 56000 | 0.0339 | 0.0327 | 0.8629 |
443
+ | 7.5347 | 57000 | 0.0325 | 0.0315 | 0.8559 |
444
+ | 7.6669 | 58000 | 0.0313 | 0.0353 | 0.8616 |
445
+ | 7.7991 | 59000 | 0.0305 | 0.0353 | 0.8622 |
446
+ | 7.9313 | 60000 | 0.0296 | 0.0358 | 0.8613 |
447
+ | 8.0635 | 61000 | 0.0292 | 0.0348 | 0.8652 |
448
+ | 8.1956 | 62000 | 0.0301 | 0.0366 | 0.8660 |
449
+ | 8.3278 | 63000 | 0.03 | 0.0336 | 0.8617 |
450
+ | 8.4600 | 64000 | 0.0287 | 0.0336 | 0.8649 |
451
+ | 8.5922 | 65000 | 0.0279 | 0.0315 | 0.8628 |
452
+ | 8.7244 | 66000 | 0.027 | 0.0322 | 0.8559 |
453
+ | 8.8566 | 67000 | 0.026 | 0.0336 | 0.8623 |
454
+ | 8.9888 | 68000 | 0.0268 | 0.0369 | 0.8628 |
455
+ | 9.1210 | 69000 | 0.0259 | 0.0333 | 0.8651 |
456
+ | 9.2531 | 70000 | 0.0261 | 0.0350 | 0.8682 |
457
+ | 9.3853 | 71000 | 0.0261 | 0.0332 | 0.8692 |
458
+ | 9.5175 | 72000 | 0.0253 | 0.0336 | 0.8659 |
459
+ | 9.6497 | 73000 | 0.0252 | 0.0342 | 0.8673 |
460
+ | 9.7819 | 74000 | 0.0243 | 0.0348 | 0.8613 |
461
+ | 9.9141 | 75000 | 0.0244 | 0.0338 | 0.8647 |
462
+ | 10.0463 | 76000 | 0.0238 | 0.0349 | 0.8672 |
463
+ | 10.1785 | 77000 | 0.0239 | 0.0359 | 0.8674 |
464
+ | 10.3106 | 78000 | 0.0241 | 0.0337 | 0.8696 |
465
+ | 10.4428 | 79000 | 0.0236 | 0.0349 | 0.8687 |
466
+ | 10.5750 | 80000 | 0.0234 | 0.0348 | 0.8648 |
467
+ | 10.7072 | 81000 | 0.0225 | 0.0345 | 0.8677 |
468
+ | 10.8394 | 82000 | 0.0217 | 0.0354 | 0.8695 |
469
+ | 10.9716 | 83000 | 0.0226 | 0.0339 | 0.8702 |
470
+ | 11.1038 | 84000 | 0.0215 | 0.0354 | 0.8717 |
471
+ | 11.2360 | 85000 | 0.022 | 0.0364 | 0.8687 |
472
+ | 11.3681 | 86000 | 0.022 | 0.0348 | 0.8740 |
473
+ | 11.5003 | 87000 | 0.0217 | 0.0353 | 0.8675 |
474
+ | 11.6325 | 88000 | 0.0221 | 0.0338 | 0.8678 |
475
+ | 11.7647 | 89000 | 0.0213 | 0.0324 | 0.8697 |
476
+ | 11.8969 | 90000 | 0.021 | 0.0336 | 0.8668 |
477
+ | 12.0291 | 91000 | 0.0206 | 0.0352 | 0.8675 |
478
+ | 12.1613 | 92000 | 0.0203 | 0.0344 | 0.8710 |
479
+ | 12.2935 | 93000 | 0.0207 | 0.0349 | 0.8675 |
480
+ | 12.4256 | 94000 | 0.0206 | 0.0339 | 0.8676 |
481
+ | 12.5578 | 95000 | 0.0199 | 0.0342 | 0.8732 |
482
+ | 12.6900 | 96000 | 0.0202 | 0.0323 | 0.8664 |
483
+ | 12.8222 | 97000 | 0.0192 | 0.0357 | 0.8688 |
484
+ | 12.9544 | 98000 | 0.0196 | 0.0359 | 0.8713 |
485
+ | 13.0866 | 99000 | 0.0196 | 0.0357 | 0.8687 |
486
+ | 13.2188 | 100000 | 0.0195 | 0.0347 | 0.8659 |
487
+ | 13.3510 | 101000 | 0.0198 | 0.0343 | 0.8702 |
488
+ | 13.4831 | 102000 | 0.0192 | 0.0329 | 0.8689 |
489
+ | 13.6153 | 103000 | 0.0191 | 0.0336 | 0.8679 |
490
+ | 13.7475 | 104000 | 0.0186 | 0.0326 | 0.8674 |
491
+ | 13.8797 | 105000 | 0.0183 | 0.0338 | 0.8687 |
492
+ | 14.0119 | 106000 | 0.0186 | 0.0346 | 0.8689 |
493
+ | 14.1441 | 107000 | 0.0177 | 0.0357 | 0.8717 |
494
+ | 14.2763 | 108000 | 0.0193 | 0.0344 | 0.8733 |
495
+ | 14.4085 | 109000 | 0.0186 | 0.0323 | 0.8742 |
496
+ | 14.5406 | 110000 | 0.018 | 0.0336 | 0.8722 |
497
+ | 14.6728 | 111000 | 0.0177 | 0.0353 | 0.8705 |
498
+ | 14.8050 | 112000 | 0.0176 | 0.0338 | 0.8678 |
499
+ | 14.9372 | 113000 | 0.0178 | 0.0348 | 0.8698 |
500
+ | 15.0694 | 114000 | 0.017 | 0.0353 | 0.8702 |
501
+ | 15.2016 | 115000 | 0.0181 | 0.0349 | 0.8721 |
502
+ | 15.3338 | 116000 | 0.0182 | 0.0341 | 0.8705 |
503
+ | 15.4660 | 117000 | 0.0171 | 0.0343 | 0.8715 |
504
+ | 15.5981 | 118000 | 0.0176 | 0.0341 | 0.8696 |
505
+ | 15.7303 | 119000 | 0.0173 | 0.0336 | 0.8706 |
506
+ | 15.8625 | 120000 | 0.0161 | 0.0342 | 0.8715 |
507
+ | 15.9947 | 121000 | 0.0174 | 0.0349 | 0.8701 |
508
+ | 16.1269 | 122000 | 0.0171 | 0.0341 | 0.8715 |
509
+ | 16.2591 | 123000 | 0.0171 | 0.0342 | 0.8720 |
510
+ | 16.3913 | 124000 | 0.0174 | 0.0336 | 0.8726 |
511
+ | 16.5235 | 125000 | 0.0167 | 0.0339 | 0.8694 |
512
+ | 16.6557 | 126000 | 0.0169 | 0.0344 | 0.8671 |
513
+ | 16.7878 | 127000 | 0.016 | 0.0341 | 0.8666 |
514
+ | 16.9200 | 128000 | 0.0163 | 0.0342 | 0.8696 |
515
+ | 17.0522 | 129000 | 0.0163 | 0.0342 | 0.8687 |
516
+ | 17.1844 | 130000 | 0.0163 | 0.0347 | 0.8709 |
517
+ | 17.3166 | 131000 | 0.017 | 0.0335 | 0.8719 |
518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
+ | 24.8513 | 188000 | 0.014 | 0.0345 | 0.8722 |
575
+ | 24.9835 | 189000 | 0.0141 | 0.0353 | 0.8709 |
576
+ | 25.1157 | 190000 | 0.0137 | 0.0349 | 0.8719 |
577
+ | 25.2479 | 191000 | 0.0142 | 0.0345 | 0.8711 |
578
+ | 25.3800 | 192000 | 0.0143 | 0.0334 | 0.8716 |
579
+ | 25.5122 | 193000 | 0.0137 | 0.0332 | 0.8717 |
580
+ | 25.6444 | 194000 | 0.0143 | 0.0339 | 0.8720 |
581
+ | 25.7766 | 195000 | 0.0136 | 0.0338 | 0.8703 |
582
+ | 25.9088 | 196000 | 0.0134 | 0.0333 | 0.8710 |
583
+ | 26.0410 | 197000 | 0.0136 | 0.0350 | 0.8708 |
584
+ | 26.1732 | 198000 | 0.0136 | 0.0345 | 0.8709 |
585
+ | 26.3054 | 199000 | 0.0142 | 0.0340 | 0.8714 |
586
+ | 26.4375 | 200000 | 0.0141 | 0.0335 | 0.8722 |
587
+ | 26.5697 | 201000 | 0.0146 | 0.0343 | 0.8717 |
588
+ | 26.7019 | 202000 | 0.0136 | 0.0341 | 0.8713 |
589
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590
+ | 26.9663 | 204000 | 0.014 | 0.0345 | 0.8706 |
591
+ | 27.0985 | 205000 | 0.0135 | 0.0349 | 0.8715 |
592
+ | 27.2307 | 206000 | 0.0135 | 0.0337 | 0.8713 |
593
+ | 27.3629 | 207000 | 0.0146 | 0.0334 | 0.8717 |
594
+ | 27.4950 | 208000 | 0.0138 | 0.0337 | 0.8717 |
595
+ | 27.6272 | 209000 | 0.0136 | 0.0331 | 0.8723 |
596
+ | 27.7594 | 210000 | 0.0133 | 0.0343 | 0.8717 |
597
+ | 27.8916 | 211000 | 0.0137 | 0.0341 | 0.8722 |
598
+ | 28.0238 | 212000 | 0.0132 | 0.0340 | 0.8718 |
599
+ | 28.1560 | 213000 | 0.0136 | 0.0344 | 0.8720 |
600
+ | 28.2882 | 214000 | 0.0143 | 0.0337 | 0.8720 |
601
+ | 28.4204 | 215000 | 0.0136 | 0.0340 | 0.8729 |
602
+ | 28.5525 | 216000 | 0.014 | 0.0334 | 0.8721 |
603
+ | 28.6847 | 217000 | 0.0131 | 0.0338 | 0.8726 |
604
+ | 28.8169 | 218000 | 0.0131 | 0.0337 | 0.8726 |
605
+ | 28.9491 | 219000 | 0.0136 | 0.0346 | 0.8726 |
606
+ | 29.0813 | 220000 | 0.0132 | 0.0347 | 0.8721 |
607
+ | 29.2135 | 221000 | 0.0136 | 0.0344 | 0.8719 |
608
+ | 29.3457 | 222000 | 0.0137 | 0.0345 | 0.8724 |
609
+ | 29.4779 | 223000 | 0.0138 | 0.0337 | 0.8723 |
610
+ | 29.6100 | 224000 | 0.013 | 0.0337 | 0.8724 |
611
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612
+ | 29.8744 | 226000 | 0.0132 | 0.0338 | 0.8720 |
613
+ | 30.0066 | 227000 | 0.0133 | 0.0335 | 0.8721 |
614
+ | 30.1388 | 228000 | 0.013 | 0.0340 | 0.8715 |
615
+ | 30.2710 | 229000 | 0.0144 | 0.0332 | 0.8726 |
616
+ | 30.4032 | 230000 | 0.014 | 0.0346 | 0.8726 |
617
+ | 30.5354 | 231000 | 0.0137 | 0.0330 | 0.8726 |
618
+ | 30.6675 | 232000 | 0.0131 | 0.0342 | 0.8724 |
619
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620
+ | 30.9319 | 234000 | 0.0135 | 0.0342 | 0.8723 |
621
+ | 31.0641 | 235000 | 0.0138 | 0.0346 | 0.8722 |
622
+ | 31.1963 | 236000 | 0.0133 | 0.0347 | 0.8721 |
623
+ | 31.3285 | 237000 | 0.0137 | 0.0335 | 0.8723 |
624
+ | 31.4607 | 238000 | 0.0137 | 0.0337 | 0.8725 |
625
+ | 31.5929 | 239000 | 0.0131 | 0.0340 | 0.8723 |
626
+ | 31.7250 | 240000 | 0.0129 | 0.0334 | 0.8725 |
627
+ | 31.8572 | 241000 | 0.0133 | 0.0336 | 0.8731 |
628
+ | 31.9894 | 242000 | 0.0137 | 0.0343 | 0.8726 |
629
+ | 32.1216 | 243000 | 0.0132 | 0.0329 | 0.8722 |
630
+ | 32.2538 | 244000 | 0.0135 | 0.0338 | 0.8724 |
631
+ | 32.3860 | 245000 | 0.0129 | 0.0344 | 0.8724 |
632
+ | 32.5182 | 246000 | 0.0136 | 0.0342 | 0.8724 |
633
+ | 32.6504 | 247000 | 0.0133 | 0.0331 | 0.8720 |
634
+ | 32.7826 | 248000 | 0.0128 | 0.0337 | 0.8718 |
635
+ | 32.9147 | 249000 | 0.0127 | 0.0338 | 0.8717 |
636
+ | 33.0469 | 250000 | 0.013 | 0.0328 | 0.8717 |
637
+ | 33.1791 | 251000 | 0.0135 | 0.0337 | 0.8723 |
638
+ | 33.3113 | 252000 | 0.0131 | 0.0334 | 0.8722 |
639
+ | 33.4435 | 253000 | 0.0134 | 0.0339 | 0.8723 |
640
+ | 33.5757 | 254000 | 0.0135 | 0.0338 | 0.8724 |
641
+ | 33.7079 | 255000 | 0.013 | 0.0341 | 0.8722 |
642
+ | 33.8401 | 256000 | 0.0126 | 0.0334 | 0.8720 |
643
+ | 33.9722 | 257000 | 0.0136 | 0.0338 | 0.8719 |
644
+ | 34.1044 | 258000 | 0.0123 | 0.0338 | 0.8722 |
645
+ | 34.2366 | 259000 | 0.0135 | 0.0336 | 0.8719 |
646
+ | 34.3688 | 260000 | 0.0136 | 0.0343 | 0.8724 |
647
+ | 34.5010 | 261000 | 0.0134 | 0.0341 | 0.8722 |
648
+ | 34.6332 | 262000 | 0.0136 | 0.0343 | 0.8722 |
649
+ | 34.7654 | 263000 | 0.0131 | 0.0344 | 0.8719 |
650
+ | 34.8976 | 264000 | 0.0128 | 0.0343 | 0.8719 |
651
+ | 35.0297 | 265000 | 0.0129 | 0.0336 | 0.8718 |
652
+ | 35.1619 | 266000 | 0.0128 | 0.0334 | 0.8720 |
653
+ | 35.2941 | 267000 | 0.013 | 0.0340 | 0.8719 |
654
+ | 35.4263 | 268000 | 0.0133 | 0.0341 | 0.8723 |
655
+ | 35.5585 | 269000 | 0.0132 | 0.0331 | 0.8723 |
656
+ | 35.6907 | 270000 | 0.0127 | 0.0335 | 0.8721 |
657
+ | 35.8229 | 271000 | 0.0123 | 0.0334 | 0.8721 |
658
+ | 35.9551 | 272000 | 0.0135 | 0.0343 | 0.8721 |
659
+ | 36.0872 | 273000 | 0.0125 | 0.0345 | 0.8721 |
660
+ | 36.2194 | 274000 | 0.0134 | 0.0336 | 0.8719 |
661
+ | 36.3516 | 275000 | 0.0132 | 0.0338 | 0.8720 |
662
+ | 36.4838 | 276000 | 0.0136 | 0.0331 | 0.8722 |
663
+ | 36.6160 | 277000 | 0.0133 | 0.0335 | 0.8726 |
664
+ | 36.7482 | 278000 | 0.0125 | 0.0336 | 0.8726 |
665
+ | 36.8804 | 279000 | 0.0122 | 0.0344 | 0.8724 |
666
+ | 37.0126 | 280000 | 0.013 | 0.0336 | 0.8722 |
667
+ | 37.1447 | 281000 | 0.0132 | 0.0333 | 0.8724 |
668
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669
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670
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671
+ | 37.6735 | 285000 | 0.0129 | 0.0329 | 0.8723 |
672
+ | 37.8057 | 286000 | 0.013 | 0.0327 | 0.8722 |
673
+ | 37.9379 | 287000 | 0.0124 | 0.0338 | 0.8722 |
674
+ | 38.0701 | 288000 | 0.0131 | 0.0338 | 0.8721 |
675
+ | 38.2022 | 289000 | 0.0129 | 0.0342 | 0.8723 |
676
+ | 38.3344 | 290000 | 0.013 | 0.0336 | 0.8720 |
677
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678
+ | 38.5988 | 292000 | 0.0129 | 0.0338 | 0.8722 |
679
+ | 38.7310 | 293000 | 0.0122 | 0.0337 | 0.8722 |
680
+ | 38.8632 | 294000 | 0.0123 | 0.0338 | 0.8722 |
681
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682
+ | 39.1276 | 296000 | 0.0128 | 0.0333 | 0.8720 |
683
+ | 39.2597 | 297000 | 0.0135 | 0.0336 | 0.8720 |
684
+ | 39.3919 | 298000 | 0.0132 | 0.0342 | 0.8721 |
685
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686
+ | 39.6563 | 300000 | 0.0125 | 0.0339 | 0.8720 |
687
+ | 39.7885 | 301000 | 0.0125 | 0.0343 | 0.8720 |
688
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689
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690
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691
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692
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693
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694
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695
+ | 40.8460 | 309000 | 0.013 | 0.0332 | 0.8721 |
696
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697
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698
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699
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700
+ | 41.5069 | 314000 | 0.0132 | 0.0335 | 0.8722 |
701
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702
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703
+ | 41.9035 | 317000 | 0.0125 | 0.0340 | 0.8720 |
704
+ | 42.0357 | 318000 | 0.0122 | 0.0342 | 0.8720 |
705
+ | 42.1679 | 319000 | 0.0129 | 0.0337 | 0.8720 |
706
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707
+ | 42.4323 | 321000 | 0.013 | 0.0332 | 0.8720 |
708
+ | 42.5644 | 322000 | 0.0141 | 0.0349 | 0.8721 |
709
+ | 42.6966 | 323000 | 0.013 | 0.0334 | 0.8721 |
710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
+ | 44.9438 | 340000 | 0.0124 | 0.0333 | 0.8721 |
727
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728
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729
+ | 45.3404 | 343000 | 0.0133 | 0.0335 | 0.8721 |
730
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731
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732
+ | 45.7369 | 346000 | 0.0129 | 0.0343 | 0.8721 |
733
+ | 45.8691 | 347000 | 0.0125 | 0.0335 | 0.8721 |
734
+ | 46.0013 | 348000 | 0.0133 | 0.0344 | 0.8721 |
735
+ | 46.1335 | 349000 | 0.013 | 0.0332 | 0.8720 |
736
+ | 46.2657 | 350000 | 0.0128 | 0.0337 | 0.8721 |
737
+ | 46.3979 | 351000 | 0.0132 | 0.0334 | 0.8721 |
738
+ | 46.5301 | 352000 | 0.0127 | 0.0343 | 0.8721 |
739
+ | 46.6623 | 353000 | 0.0127 | 0.0334 | 0.8721 |
740
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741
+ | 46.9266 | 355000 | 0.013 | 0.0339 | 0.8721 |
742
+ | 47.0588 | 356000 | 0.0126 | 0.0340 | 0.8721 |
743
+ | 47.1910 | 357000 | 0.0132 | 0.0336 | 0.8721 |
744
+ | 47.3232 | 358000 | 0.0138 | 0.0334 | 0.8721 |
745
+ | 47.4554 | 359000 | 0.0133 | 0.0336 | 0.8720 |
746
+ | 47.5876 | 360000 | 0.0135 | 0.0340 | 0.8720 |
747
+ | 47.7198 | 361000 | 0.0129 | 0.0341 | 0.8721 |
748
+ | 47.8519 | 362000 | 0.0123 | 0.0334 | 0.8721 |
749
+ | 47.9841 | 363000 | 0.0126 | 0.0334 | 0.8721 |
750
+ | 48.1163 | 364000 | 0.0121 | 0.0337 | 0.8721 |
751
+ | 48.2485 | 365000 | 0.0127 | 0.0342 | 0.8720 |
752
+ | 48.3807 | 366000 | 0.0124 | 0.0336 | 0.8721 |
753
+ | 48.5129 | 367000 | 0.0125 | 0.0338 | 0.8721 |
754
+ | 48.6451 | 368000 | 0.0125 | 0.0341 | 0.8720 |
755
+ | 48.7773 | 369000 | 0.0122 | 0.0333 | 0.8721 |
756
+ | 48.9095 | 370000 | 0.0123 | 0.0336 | 0.8721 |
757
+ | 49.0416 | 371000 | 0.0124 | 0.0341 | 0.8720 |
758
+ | 49.1738 | 372000 | 0.0132 | 0.0330 | 0.8720 |
759
+ | 49.3060 | 373000 | 0.0128 | 0.0342 | 0.8720 |
760
+ | 49.4382 | 374000 | 0.0132 | 0.0341 | 0.8720 |
761
+ | 49.5704 | 375000 | 0.013 | 0.0334 | 0.8721 |
762
+ | 49.7026 | 376000 | 0.0126 | 0.0340 | 0.8720 |
763
+ | 49.8348 | 377000 | 0.0126 | 0.0337 | 0.8720 |
764
+ | 49.9670 | 378000 | 0.0131 | 0.0337 | 0.8720 |
765
+
766
+ * The bold row denotes the saved checkpoint.
767
+ </details>
768
+
769
  ### Framework Versions
770
  - Python: 3.12.3
771
  - Sentence Transformers: 5.1.0
model.safetensors CHANGED
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