---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-noisy-pretrain-fine-tuned
  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. -->

# bert-base-german-cased-noisy-pretrain-fine-tuned

This model is a fine-tuned version of [tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData](https://huggingface.co/tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2925
- Precision: 0.7933
- Recall: 0.7457
- F1: 0.7688
- Accuracy: 0.9147

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 33   | 0.3093          | 0.7456    | 0.6029 | 0.6667 | 0.8808   |
| No log        | 2.0   | 66   | 0.2587          | 0.7774    | 0.7286 | 0.7522 | 0.9078   |
| No log        | 3.0   | 99   | 0.2529          | 0.7775    | 0.7686 | 0.7730 | 0.9136   |
| No log        | 4.0   | 132  | 0.2598          | 0.8063    | 0.7257 | 0.7639 | 0.9147   |
| No log        | 5.0   | 165  | 0.2783          | 0.7927    | 0.7429 | 0.7670 | 0.9159   |
| No log        | 6.0   | 198  | 0.2899          | 0.8019    | 0.74   | 0.7697 | 0.9165   |
| No log        | 7.0   | 231  | 0.2925          | 0.7933    | 0.7457 | 0.7688 | 0.9147   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1