---
license: mit
base_model: dathi103/gbert-job-extended
tags:
- generated_from_trainer
model-index:
- name: gerskill-gbert-job-extended
  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. -->

# gerskill-gbert-job-extended

This model is a fine-tuned version of [dathi103/gbert-job-extended](https://huggingface.co/dathi103/gbert-job-extended) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0806
- Hard: {'precision': 0.7995867768595041, 'recall': 0.8524229074889867, 'f1': 0.8251599147121534, 'number': 454}
- Soft: {'precision': 0.7804878048780488, 'recall': 0.7804878048780488, 'f1': 0.7804878048780488, 'number': 82}
- Overall Precision: 0.7968
- Overall Recall: 0.8414
- Overall F1: 0.8185
- Overall Accuracy: 0.9750

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Hard                                                                                                     | Soft                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log        | 1.0   | 178  | 0.1035          | {'precision': 0.6715867158671587, 'recall': 0.801762114537445, 'f1': 0.7309236947791165, 'number': 454}  | {'precision': 0.6105263157894737, 'recall': 0.7073170731707317, 'f1': 0.6553672316384181, 'number': 82} | 0.6625            | 0.7873         | 0.7195     | 0.9592           |
| No log        | 2.0   | 356  | 0.0762          | {'precision': 0.7698744769874477, 'recall': 0.8105726872246696, 'f1': 0.7896995708154506, 'number': 454} | {'precision': 0.7532467532467533, 'recall': 0.7073170731707317, 'f1': 0.7295597484276729, 'number': 82} | 0.7676            | 0.7948         | 0.7809     | 0.9705           |
| 0.1183        | 3.0   | 534  | 0.0713          | {'precision': 0.7958762886597938, 'recall': 0.8502202643171806, 'f1': 0.8221512247071352, 'number': 454} | {'precision': 0.7974683544303798, 'recall': 0.7682926829268293, 'f1': 0.782608695652174, 'number': 82}  | 0.7961            | 0.8377         | 0.8164     | 0.9735           |
| 0.1183        | 4.0   | 712  | 0.0785          | {'precision': 0.7962962962962963, 'recall': 0.8524229074889867, 'f1': 0.823404255319149, 'number': 454}  | {'precision': 0.7901234567901234, 'recall': 0.7804878048780488, 'f1': 0.7852760736196319, 'number': 82} | 0.7954            | 0.8414         | 0.8178     | 0.9739           |
| 0.1183        | 5.0   | 890  | 0.0806          | {'precision': 0.7995867768595041, 'recall': 0.8524229074889867, 'f1': 0.8251599147121534, 'number': 454} | {'precision': 0.7804878048780488, 'recall': 0.7804878048780488, 'f1': 0.7804878048780488, 'number': 82} | 0.7968            | 0.8414         | 0.8185     | 0.9750           |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2