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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: jobdescription
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# jobdescription
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4789
- F1: 0.5701
- Roc Auc: 0.7465
- Accuracy: 0.2801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.2983 | 0.82 | 500 | 0.2704 | 0.2291 | 0.5655 | 0.0767 |
| 0.2502 | 1.65 | 1000 | 0.2516 | 0.3179 | 0.5999 | 0.1206 |
| 0.2354 | 2.47 | 1500 | 0.2390 | 0.3442 | 0.6093 | 0.1651 |
| 0.2169 | 3.29 | 2000 | 0.2327 | 0.4040 | 0.6366 | 0.2022 |
| 0.1988 | 4.12 | 2500 | 0.2310 | 0.4561 | 0.6669 | 0.2127 |
| 0.1809 | 4.94 | 3000 | 0.2332 | 0.4599 | 0.6655 | 0.2226 |
| 0.1637 | 5.77 | 3500 | 0.2331 | 0.5096 | 0.7112 | 0.2226 |
| 0.1499 | 6.59 | 4000 | 0.2331 | 0.5159 | 0.7101 | 0.2239 |
| 0.1384 | 7.41 | 4500 | 0.2404 | 0.5121 | 0.6987 | 0.2319 |
| 0.1253 | 8.24 | 5000 | 0.2443 | 0.5177 | 0.7048 | 0.2288 |
| 0.1108 | 9.06 | 5500 | 0.2509 | 0.5352 | 0.7272 | 0.2319 |
| 0.0974 | 9.88 | 6000 | 0.2669 | 0.5309 | 0.7214 | 0.2375 |
| 0.0844 | 10.71 | 6500 | 0.2650 | 0.5420 | 0.7334 | 0.2393 |
| 0.076 | 11.53 | 7000 | 0.2793 | 0.5263 | 0.7158 | 0.2344 |
| 0.0672 | 12.36 | 7500 | 0.2904 | 0.5453 | 0.7340 | 0.2369 |
| 0.0607 | 13.18 | 8000 | 0.3024 | 0.5424 | 0.7270 | 0.2529 |
| 0.0549 | 14.0 | 8500 | 0.3026 | 0.5524 | 0.7311 | 0.2684 |
| 0.0464 | 14.83 | 9000 | 0.3211 | 0.5538 | 0.7386 | 0.2505 |
| 0.0411 | 15.65 | 9500 | 0.3292 | 0.5591 | 0.7408 | 0.2672 |
| 0.0356 | 16.47 | 10000 | 0.3417 | 0.5633 | 0.7537 | 0.2492 |
| 0.0335 | 17.3 | 10500 | 0.3447 | 0.5601 | 0.7463 | 0.2536 |
| 0.0295 | 18.12 | 11000 | 0.3447 | 0.5678 | 0.7465 | 0.2715 |
| 0.0262 | 18.95 | 11500 | 0.3539 | 0.5642 | 0.7437 | 0.2653 |
| 0.0237 | 19.77 | 12000 | 0.3709 | 0.5631 | 0.7393 | 0.2801 |
| 0.0206 | 20.59 | 12500 | 0.3715 | 0.5617 | 0.7443 | 0.2783 |
| 0.0181 | 21.42 | 13000 | 0.3783 | 0.5672 | 0.7513 | 0.2641 |
| 0.0192 | 22.24 | 13500 | 0.3931 | 0.5622 | 0.7402 | 0.2672 |
| 0.0173 | 23.06 | 14000 | 0.3902 | 0.5665 | 0.7471 | 0.2709 |
| 0.0166 | 23.89 | 14500 | 0.4031 | 0.5649 | 0.7452 | 0.2740 |
| 0.0141 | 24.71 | 15000 | 0.4120 | 0.5632 | 0.7421 | 0.2764 |
| 0.0131 | 25.54 | 15500 | 0.4071 | 0.5644 | 0.7428 | 0.2845 |
| 0.013 | 26.36 | 16000 | 0.4122 | 0.5668 | 0.7412 | 0.2857 |
| 0.0121 | 27.18 | 16500 | 0.4253 | 0.5714 | 0.7505 | 0.2771 |
| 0.0109 | 28.01 | 17000 | 0.4323 | 0.5687 | 0.7462 | 0.2764 |
| 0.0112 | 28.83 | 17500 | 0.4433 | 0.5600 | 0.7401 | 0.2839 |
| 0.0099 | 29.65 | 18000 | 0.4374 | 0.5670 | 0.7446 | 0.2814 |
| 0.0106 | 30.48 | 18500 | 0.4395 | 0.5644 | 0.7488 | 0.2690 |
| 0.0104 | 31.3 | 19000 | 0.4369 | 0.5724 | 0.7498 | 0.2752 |
| 0.0085 | 32.13 | 19500 | 0.4469 | 0.5660 | 0.7430 | 0.2777 |
| 0.0093 | 32.95 | 20000 | 0.4483 | 0.5698 | 0.7463 | 0.2808 |
| 0.0085 | 33.77 | 20500 | 0.4549 | 0.5704 | 0.7580 | 0.2653 |
| 0.0093 | 34.6 | 21000 | 0.4579 | 0.5664 | 0.7420 | 0.2863 |
| 0.009 | 35.42 | 21500 | 0.4560 | 0.5726 | 0.7486 | 0.2808 |
| 0.0075 | 36.24 | 22000 | 0.4650 | 0.5635 | 0.7502 | 0.2715 |
| 0.0081 | 37.07 | 22500 | 0.4647 | 0.5659 | 0.7502 | 0.2715 |
| 0.0074 | 37.89 | 23000 | 0.4662 | 0.5674 | 0.7503 | 0.2758 |
| 0.0077 | 38.71 | 23500 | 0.4710 | 0.5676 | 0.7460 | 0.2771 |
| 0.0065 | 39.54 | 24000 | 0.4701 | 0.5659 | 0.7461 | 0.2801 |
| 0.0076 | 40.36 | 24500 | 0.4673 | 0.5687 | 0.7452 | 0.2777 |
| 0.0075 | 41.19 | 25000 | 0.4692 | 0.5643 | 0.7430 | 0.2715 |
| 0.0071 | 42.01 | 25500 | 0.4743 | 0.5697 | 0.7490 | 0.2771 |
| 0.0071 | 42.83 | 26000 | 0.4705 | 0.5678 | 0.7459 | 0.2703 |
| 0.0063 | 43.66 | 26500 | 0.4711 | 0.5682 | 0.7448 | 0.2777 |
| 0.0071 | 44.48 | 27000 | 0.4722 | 0.5671 | 0.7442 | 0.2715 |
| 0.0061 | 45.3 | 27500 | 0.4714 | 0.5680 | 0.7441 | 0.2789 |
| 0.0065 | 46.13 | 28000 | 0.4781 | 0.5712 | 0.7487 | 0.2764 |
| 0.0067 | 46.95 | 28500 | 0.4770 | 0.5699 | 0.7439 | 0.2764 |
| 0.0065 | 47.78 | 29000 | 0.4790 | 0.5697 | 0.7463 | 0.2789 |
| 0.006 | 48.6 | 29500 | 0.4782 | 0.5698 | 0.7463 | 0.2801 |
| 0.0058 | 49.42 | 30000 | 0.4789 | 0.5701 | 0.7465 | 0.2801 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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