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README.md CHANGED
@@ -5,18 +5,18 @@ base_model: pborchert/BusinessBERT
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: bottleneckBERTsmall
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # bottleneckBERTsmall
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  This model is a fine-tuned version of [pborchert/BusinessBERT](https://huggingface.co/pborchert/BusinessBERT) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2366
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  ## Model description
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@@ -41,32 +41,284 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 500
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- - num_epochs: 5
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:------:|:----:|:---------------:|
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- | 3.0373 | 0.2565 | 500 | 2.8721 |
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- | 2.9211 | 0.5131 | 1000 | 2.7851 |
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- | 2.8562 | 0.7696 | 1500 | 2.6988 |
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- | 2.6908 | 1.0262 | 2000 | 2.6324 |
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- | 2.6446 | 1.2827 | 2500 | 2.5663 |
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- | 2.6452 | 1.5393 | 3000 | 2.5348 |
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- | 2.5931 | 1.7958 | 3500 | 2.4756 |
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- | 2.5241 | 2.0523 | 4000 | 2.4865 |
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- | 2.4826 | 2.3089 | 4500 | 2.4326 |
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- | 2.4639 | 2.5654 | 5000 | 2.4269 |
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- | 2.4046 | 2.8220 | 5500 | 2.3856 |
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- | 2.3768 | 3.0785 | 6000 | 2.3570 |
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- | 2.3793 | 3.3350 | 6500 | 2.3415 |
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- | 2.4273 | 3.5916 | 7000 | 2.3277 |
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- | 2.3792 | 3.8481 | 7500 | 2.2969 |
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- | 2.324 | 4.1047 | 8000 | 2.2780 |
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- | 2.2997 | 4.3612 | 8500 | 2.2658 |
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- | 2.2756 | 4.6178 | 9000 | 2.2601 |
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- | 2.2141 | 4.8743 | 9500 | 2.2366 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  model-index:
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+ - name: working
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  results: []
10
  ---
11
 
12
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
  should probably proofread and complete it, then remove this comment. -->
14
 
15
+ # working
16
 
17
  This model is a fine-tuned version of [pborchert/BusinessBERT](https://huggingface.co/pborchert/BusinessBERT) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.3070
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  ## Model description
22
 
 
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  - seed: 42
42
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 7
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | No log | 0.0257 | 50 | 5.4950 |
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+ | No log | 0.0513 | 100 | 5.2169 |
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+ | No log | 0.0770 | 150 | 5.0495 |
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+ | No log | 0.1026 | 200 | 4.9407 |
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+ | No log | 0.1283 | 250 | 4.8442 |
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+ | No log | 0.1539 | 300 | 4.7955 |
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+ | No log | 0.1796 | 350 | 4.6896 |
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+ | No log | 0.2052 | 400 | 4.6560 |
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+ | No log | 0.2309 | 450 | 4.6657 |
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+ | 5.1165 | 0.2565 | 500 | 4.5673 |
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+ | 5.1165 | 0.2822 | 550 | 4.5526 |
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+ | 5.1165 | 0.3079 | 600 | 4.5253 |
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+ | 5.1165 | 0.3335 | 650 | 4.4882 |
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+ | 5.1165 | 0.3592 | 700 | 4.4493 |
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+ | 5.1165 | 0.3848 | 750 | 4.4193 |
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+ | 5.1165 | 0.4105 | 800 | 4.3838 |
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+ | 5.1165 | 0.4361 | 850 | 4.3737 |
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+ | 5.1165 | 0.4618 | 900 | 4.3586 |
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+ | 5.1165 | 0.4874 | 950 | 4.3631 |
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+ | 4.5802 | 0.5131 | 1000 | 4.3204 |
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+ | 4.5802 | 0.5387 | 1050 | 4.3004 |
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+ | 4.5802 | 0.5644 | 1100 | 4.2959 |
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+ | 4.5802 | 0.5900 | 1150 | 4.2635 |
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+ | 4.5802 | 0.6157 | 1200 | 4.2373 |
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+ | 4.5802 | 0.6414 | 1250 | 4.2351 |
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+ | 4.5802 | 0.6670 | 1300 | 4.2160 |
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+ | 4.5802 | 0.6927 | 1350 | 4.1734 |
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+ | 4.5802 | 0.7183 | 1400 | 4.1644 |
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+ | 4.5802 | 0.7440 | 1450 | 4.1413 |
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+ | 4.3933 | 0.7696 | 1500 | 4.1446 |
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+ | 4.3933 | 0.7953 | 1550 | 4.1304 |
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+ | 4.3933 | 0.8209 | 1600 | 4.1446 |
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+ | 4.3933 | 0.8466 | 1650 | 4.1190 |
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+ | 4.3933 | 0.8722 | 1700 | 4.1191 |
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+ | 4.3933 | 0.8979 | 1750 | 4.0745 |
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+ | 4.3933 | 0.9236 | 1800 | 4.0714 |
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+ | 4.3933 | 0.9492 | 1850 | 4.0761 |
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+ | 4.3933 | 0.9749 | 1900 | 4.0438 |
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+ | 4.3933 | 1.0005 | 1950 | 4.0607 |
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+ | 4.2488 | 1.0262 | 2000 | 4.0196 |
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+ | 4.2488 | 1.0518 | 2050 | 4.0295 |
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+ | 4.2488 | 1.0775 | 2100 | 3.9847 |
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+ | 4.2488 | 1.1031 | 2150 | 3.9741 |
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+ | 4.2488 | 1.1288 | 2200 | 3.9577 |
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+ | 4.2488 | 1.1544 | 2250 | 3.9609 |
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+ | 4.2488 | 1.1801 | 2300 | 3.9989 |
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+ | 4.2488 | 1.2057 | 2350 | 3.9628 |
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+ | 4.2488 | 1.2314 | 2400 | 3.9308 |
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+ | 4.2488 | 1.2571 | 2450 | 3.9223 |
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+ | 4.0985 | 1.2827 | 2500 | 3.9455 |
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+ | 4.0985 | 1.3084 | 2550 | 3.9289 |
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+ | 4.0985 | 1.3340 | 2600 | 3.9327 |
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+ | 4.0985 | 1.3597 | 2650 | 3.8934 |
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+ | 4.0985 | 1.3853 | 2700 | 3.8884 |
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+ | 4.0985 | 1.4110 | 2750 | 3.9077 |
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+ | 4.0985 | 1.4366 | 2800 | 3.8825 |
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+ | 4.0985 | 1.4623 | 2850 | 3.8765 |
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+ | 4.0985 | 1.4879 | 2900 | 3.8477 |
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+ | 4.0985 | 1.5136 | 2950 | 3.8432 |
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+ | 4.009 | 1.5393 | 3000 | 3.8259 |
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+ | 4.009 | 1.5649 | 3050 | 3.8392 |
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+ | 4.009 | 1.5906 | 3100 | 3.8668 |
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+ | 4.009 | 1.6162 | 3150 | 3.8354 |
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+ | 4.009 | 1.6419 | 3200 | 3.8150 |
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+ | 4.009 | 1.6675 | 3250 | 3.8325 |
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+ | 4.009 | 1.6932 | 3300 | 3.8268 |
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+ | 4.009 | 1.7188 | 3350 | 3.8147 |
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+ | 4.009 | 1.7445 | 3400 | 3.7826 |
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+ | 4.009 | 1.7701 | 3450 | 3.7822 |
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+ | 3.9676 | 1.7958 | 3500 | 3.7950 |
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+ | 3.9676 | 1.8214 | 3550 | 3.7839 |
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+ | 3.9676 | 1.8471 | 3600 | 3.7646 |
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+ | 3.9676 | 1.8728 | 3650 | 3.7747 |
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+ | 3.9676 | 1.8984 | 3700 | 3.7583 |
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+ | 3.9676 | 1.9241 | 3750 | 3.7438 |
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+ | 3.9676 | 1.9497 | 3800 | 3.7603 |
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+ | 3.9676 | 1.9754 | 3850 | 3.7299 |
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+ | 3.9676 | 2.0010 | 3900 | 3.7391 |
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+ | 3.9676 | 2.0267 | 3950 | 3.7117 |
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+ | 3.8702 | 2.0523 | 4000 | 3.7592 |
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+ | 3.8702 | 2.0780 | 4050 | 3.7178 |
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+ | 3.8702 | 2.1036 | 4100 | 3.7501 |
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+ | 3.8702 | 2.1293 | 4150 | 3.7182 |
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+ | 3.8702 | 2.1550 | 4200 | 3.7127 |
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+ | 3.8702 | 2.1806 | 4250 | 3.7032 |
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+ | 3.8702 | 2.2063 | 4300 | 3.7028 |
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+ | 3.8702 | 2.2319 | 4350 | 3.6726 |
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+ | 3.8702 | 2.2576 | 4400 | 3.6591 |
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+ | 3.8702 | 2.2832 | 4450 | 3.6744 |
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+ | 3.8324 | 2.3089 | 4500 | 3.6995 |
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+ | 3.8324 | 2.3345 | 4550 | 3.6777 |
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+ | 3.8324 | 2.3602 | 4600 | 3.7105 |
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+ | 3.8324 | 2.3858 | 4650 | 3.6624 |
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+ | 3.8324 | 2.4115 | 4700 | 3.6701 |
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+ | 3.8324 | 2.4371 | 4750 | 3.6298 |
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+ | 3.8324 | 2.4628 | 4800 | 3.6582 |
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+ | 3.8324 | 2.4885 | 4850 | 3.6668 |
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+ | 3.8324 | 2.5141 | 4900 | 3.6393 |
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+ | 3.8324 | 2.5398 | 4950 | 3.6194 |
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+ | 3.783 | 2.5654 | 5000 | 3.6175 |
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+ | 3.783 | 2.5911 | 5050 | 3.6144 |
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+ | 3.783 | 2.6167 | 5100 | 3.6148 |
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+ | 3.783 | 2.6424 | 5150 | 3.5929 |
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+ | 3.783 | 2.6680 | 5200 | 3.6180 |
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+ | 3.783 | 2.6937 | 5250 | 3.6065 |
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+ | 3.783 | 2.7193 | 5300 | 3.6218 |
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+ | 3.783 | 2.7450 | 5350 | 3.6123 |
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+ | 3.783 | 2.7707 | 5400 | 3.6084 |
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+ | 3.783 | 2.7963 | 5450 | 3.5988 |
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+ | 3.7504 | 2.8220 | 5500 | 3.5943 |
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+ | 3.7504 | 2.8476 | 5550 | 3.5656 |
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+ | 3.7504 | 2.8733 | 5600 | 3.6005 |
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+ | 3.7504 | 2.8989 | 5650 | 3.5971 |
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+ | 3.7504 | 2.9246 | 5700 | 3.5795 |
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+ | 3.7504 | 2.9502 | 5750 | 3.5698 |
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+ | 3.7504 | 2.9759 | 5800 | 3.5793 |
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+ | 3.7504 | 3.0015 | 5850 | 3.5790 |
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+ | 3.7504 | 3.0272 | 5900 | 3.5504 |
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+ | 3.7504 | 3.0528 | 5950 | 3.5628 |
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+ | 3.6774 | 3.0785 | 6000 | 3.5494 |
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+ | 3.6774 | 3.1042 | 6050 | 3.5776 |
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+ | 3.6774 | 3.1298 | 6100 | 3.5380 |
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+ | 3.6774 | 3.1555 | 6150 | 3.5350 |
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+ | 3.6774 | 3.1811 | 6200 | 3.5347 |
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+ | 3.6774 | 3.2068 | 6250 | 3.5553 |
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+ | 3.6774 | 3.2324 | 6300 | 3.5684 |
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+ | 3.6774 | 3.2581 | 6350 | 3.5194 |
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+ | 3.6774 | 3.2837 | 6400 | 3.5157 |
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+ | 3.6774 | 3.3094 | 6450 | 3.5307 |
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+ | 3.643 | 3.3350 | 6500 | 3.5300 |
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+ | 3.643 | 3.3607 | 6550 | 3.5182 |
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+ | 3.643 | 3.3864 | 6600 | 3.5084 |
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+ | 3.643 | 3.4120 | 6650 | 3.5237 |
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+ | 3.643 | 3.4377 | 6700 | 3.5138 |
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+ | 3.643 | 3.4633 | 6750 | 3.4951 |
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+ | 3.643 | 3.4890 | 6800 | 3.4666 |
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+ | 3.643 | 3.5146 | 6850 | 3.5216 |
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+ | 3.643 | 3.5403 | 6900 | 3.5231 |
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+ | 3.643 | 3.5659 | 6950 | 3.4880 |
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+ | 3.6139 | 3.5916 | 7000 | 3.5065 |
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+ | 3.6139 | 3.6172 | 7050 | 3.4963 |
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+ | 3.6139 | 3.6429 | 7100 | 3.4861 |
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+ | 3.6139 | 3.6685 | 7150 | 3.4852 |
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+ | 3.6139 | 3.6942 | 7200 | 3.4753 |
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+ | 3.6139 | 3.7199 | 7250 | 3.4783 |
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+ | 3.6139 | 3.7455 | 7300 | 3.4762 |
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+ | 3.6139 | 3.7712 | 7350 | 3.4914 |
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+ | 3.6139 | 3.7968 | 7400 | 3.4560 |
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+ | 3.6139 | 3.8225 | 7450 | 3.4587 |
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+ | 3.5909 | 3.8481 | 7500 | 3.4825 |
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+ | 3.5909 | 3.8738 | 7550 | 3.4616 |
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+ | 3.5909 | 3.8994 | 7600 | 3.4469 |
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+ | 3.5909 | 3.9251 | 7650 | 3.4609 |
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+ | 3.5909 | 3.9507 | 7700 | 3.4284 |
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+ | 3.5909 | 3.9764 | 7750 | 3.4680 |
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+ | 3.5909 | 4.0021 | 7800 | 3.4610 |
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+ | 3.5909 | 4.0277 | 7850 | 3.4718 |
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+ | 3.5909 | 4.0534 | 7900 | 3.4455 |
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+ | 3.5909 | 4.0790 | 7950 | 3.4548 |
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+ | 3.539 | 4.1047 | 8000 | 3.4461 |
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+ | 3.539 | 4.1303 | 8050 | 3.4435 |
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+ | 3.539 | 4.1560 | 8100 | 3.4412 |
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+ | 3.539 | 4.1816 | 8150 | 3.4331 |
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+ | 3.539 | 4.2073 | 8200 | 3.4258 |
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+ | 3.539 | 4.2329 | 8250 | 3.4367 |
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+ | 3.539 | 4.2586 | 8300 | 3.4259 |
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+ | 3.539 | 4.2842 | 8350 | 3.4338 |
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+ | 3.539 | 4.3099 | 8400 | 3.4274 |
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+ | 3.539 | 4.3356 | 8450 | 3.4556 |
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+ | 3.5276 | 4.3612 | 8500 | 3.4288 |
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+ | 3.5276 | 4.3869 | 8550 | 3.4279 |
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+ | 3.5276 | 4.4125 | 8600 | 3.4191 |
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+ | 3.5276 | 4.4382 | 8650 | 3.3882 |
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+ | 3.5276 | 4.4638 | 8700 | 3.4083 |
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+ | 3.5276 | 4.4895 | 8750 | 3.4005 |
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+ | 3.5276 | 4.5151 | 8800 | 3.3910 |
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+ | 3.5276 | 4.5408 | 8850 | 3.4416 |
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+ | 3.5276 | 4.5664 | 8900 | 3.4154 |
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+ | 3.5276 | 4.5921 | 8950 | 3.4116 |
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+ | 3.4812 | 4.6178 | 9000 | 3.3965 |
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+ | 3.4812 | 4.6434 | 9050 | 3.3789 |
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+ | 3.4812 | 4.6691 | 9100 | 3.3688 |
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+ | 3.4812 | 4.6947 | 9150 | 3.3994 |
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+ | 3.4812 | 4.7204 | 9200 | 3.3970 |
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+ | 3.4812 | 4.7460 | 9250 | 3.3965 |
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+ | 3.4812 | 4.7717 | 9300 | 3.4146 |
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+ | 3.4812 | 4.7973 | 9350 | 3.3628 |
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+ | 3.4812 | 4.8230 | 9400 | 3.4125 |
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+ | 3.4812 | 4.8486 | 9450 | 3.3942 |
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+ | 3.5039 | 4.8743 | 9500 | 3.3965 |
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+ | 3.5039 | 4.8999 | 9550 | 3.3620 |
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+ | 3.5039 | 4.9256 | 9600 | 3.4002 |
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+ | 3.5039 | 4.9513 | 9650 | 3.3658 |
243
+ | 3.5039 | 4.9769 | 9700 | 3.3547 |
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+ | 3.5039 | 5.0026 | 9750 | 3.3852 |
245
+ | 3.5039 | 5.0282 | 9800 | 3.3811 |
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+ | 3.5039 | 5.0539 | 9850 | 3.3980 |
247
+ | 3.5039 | 5.0795 | 9900 | 3.3577 |
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+ | 3.5039 | 5.1052 | 9950 | 3.3668 |
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+ | 3.4764 | 5.1308 | 10000 | 3.3734 |
250
+ | 3.4764 | 5.1565 | 10050 | 3.3895 |
251
+ | 3.4764 | 5.1821 | 10100 | 3.3553 |
252
+ | 3.4764 | 5.2078 | 10150 | 3.3640 |
253
+ | 3.4764 | 5.2335 | 10200 | 3.3691 |
254
+ | 3.4764 | 5.2591 | 10250 | 3.3724 |
255
+ | 3.4764 | 5.2848 | 10300 | 3.3703 |
256
+ | 3.4764 | 5.3104 | 10350 | 3.3383 |
257
+ | 3.4764 | 5.3361 | 10400 | 3.3653 |
258
+ | 3.4764 | 5.3617 | 10450 | 3.3709 |
259
+ | 3.4419 | 5.3874 | 10500 | 3.3848 |
260
+ | 3.4419 | 5.4130 | 10550 | 3.3522 |
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+ | 3.4419 | 5.4387 | 10600 | 3.3370 |
262
+ | 3.4419 | 5.4643 | 10650 | 3.3667 |
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+ | 3.4419 | 5.4900 | 10700 | 3.3621 |
264
+ | 3.4419 | 5.5156 | 10750 | 3.3706 |
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+ | 3.4419 | 5.5413 | 10800 | 3.3339 |
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+ | 3.4419 | 5.5670 | 10850 | 3.3561 |
267
+ | 3.4419 | 5.5926 | 10900 | 3.3574 |
268
+ | 3.4419 | 5.6183 | 10950 | 3.3452 |
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+ | 3.4277 | 5.6439 | 11000 | 3.3431 |
270
+ | 3.4277 | 5.6696 | 11050 | 3.3331 |
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+ | 3.4277 | 5.6952 | 11100 | 3.3397 |
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+ | 3.4277 | 5.7209 | 11150 | 3.3414 |
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+ | 3.4277 | 5.7465 | 11200 | 3.3307 |
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+ | 3.4277 | 5.7722 | 11250 | 3.3241 |
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+ | 3.4277 | 5.7978 | 11300 | 3.3572 |
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+ | 3.4277 | 5.8235 | 11350 | 3.3402 |
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+ | 3.4277 | 5.8492 | 11400 | 3.3145 |
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+ | 3.4277 | 5.8748 | 11450 | 3.3312 |
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+ | 3.4197 | 5.9005 | 11500 | 3.3520 |
280
+ | 3.4197 | 5.9261 | 11550 | 3.3387 |
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+ | 3.4197 | 5.9518 | 11600 | 3.3213 |
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+ | 3.4197 | 5.9774 | 11650 | 3.3300 |
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+ | 3.4197 | 6.0031 | 11700 | 3.3201 |
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+ | 3.4197 | 6.0287 | 11750 | 3.3075 |
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+ | 3.4197 | 6.0544 | 11800 | 3.3329 |
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+ | 3.4197 | 6.0800 | 11850 | 3.3202 |
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+ | 3.4197 | 6.1057 | 11900 | 3.3527 |
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+ | 3.4197 | 6.1313 | 11950 | 3.3377 |
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+ | 3.3882 | 6.1570 | 12000 | 3.3125 |
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+ | 3.3882 | 6.1827 | 12050 | 3.3278 |
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+ | 3.3882 | 6.2083 | 12100 | 3.3224 |
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+ | 3.3882 | 6.2340 | 12150 | 3.3224 |
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+ | 3.3882 | 6.2596 | 12200 | 3.3093 |
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+ | 3.3882 | 6.2853 | 12250 | 3.3184 |
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+ | 3.3882 | 6.3109 | 12300 | 3.3520 |
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+ | 3.3882 | 6.3366 | 12350 | 3.3085 |
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+ | 3.3882 | 6.3622 | 12400 | 3.2921 |
298
+ | 3.3882 | 6.3879 | 12450 | 3.3209 |
299
+ | 3.3896 | 6.4135 | 12500 | 3.3323 |
300
+ | 3.3896 | 6.4392 | 12550 | 3.3098 |
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+ | 3.3896 | 6.4649 | 12600 | 3.3157 |
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+ | 3.3896 | 6.4905 | 12650 | 3.3032 |
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+ | 3.3896 | 6.5162 | 12700 | 3.3247 |
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+ | 3.3896 | 6.5418 | 12750 | 3.3171 |
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+ | 3.3896 | 6.5675 | 12800 | 3.2847 |
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+ | 3.3896 | 6.5931 | 12850 | 3.3035 |
307
+ | 3.3896 | 6.6188 | 12900 | 3.2980 |
308
+ | 3.3896 | 6.6444 | 12950 | 3.3195 |
309
+ | 3.3973 | 6.6701 | 13000 | 3.3071 |
310
+ | 3.3973 | 6.6957 | 13050 | 3.3037 |
311
+ | 3.3973 | 6.7214 | 13100 | 3.3250 |
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+ | 3.3973 | 6.7470 | 13150 | 3.3324 |
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+ | 3.3973 | 6.7727 | 13200 | 3.3228 |
314
+ | 3.3973 | 6.7984 | 13250 | 3.2844 |
315
+ | 3.3973 | 6.8240 | 13300 | 3.3302 |
316
+ | 3.3973 | 6.8497 | 13350 | 3.3019 |
317
+ | 3.3973 | 6.8753 | 13400 | 3.3113 |
318
+ | 3.3973 | 6.9010 | 13450 | 3.3134 |
319
+ | 3.3858 | 6.9266 | 13500 | 3.2894 |
320
+ | 3.3858 | 6.9523 | 13550 | 3.3094 |
321
+ | 3.3858 | 6.9779 | 13600 | 3.3070 |
322
 
323
 
324
  ### Framework versions