---
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