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# bert-base-german-cased-20000-ner

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0826
- Precision: 0.8904
- Recall: 0.8693
- F1: 0.8797
- Accuracy: 0.9832

## 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: 5e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.11  | 64   | 0.0840          | 0.8076    | 0.7842 | 0.7957 | 0.9752   |
| No log        | 0.23  | 128  | 0.0787          | 0.8119    | 0.7735 | 0.7922 | 0.9746   |
| No log        | 0.34  | 192  | 0.0677          | 0.8264    | 0.8362 | 0.8313 | 0.9794   |
| No log        | 0.45  | 256  | 0.0630          | 0.8440    | 0.8125 | 0.8280 | 0.9801   |
| No log        | 0.57  | 320  | 0.0664          | 0.8035    | 0.8391 | 0.8209 | 0.9782   |
| No log        | 0.68  | 384  | 0.0674          | 0.8850    | 0.8285 | 0.8558 | 0.9819   |
| No log        | 0.79  | 448  | 0.0631          | 0.8834    | 0.8598 | 0.8714 | 0.9825   |
| 0.094         | 0.9   | 512  | 0.0572          | 0.8933    | 0.8462 | 0.8691 | 0.9832   |
| 0.094         | 1.02  | 576  | 0.0728          | 0.8520    | 0.8681 | 0.8600 | 0.9795   |
| 0.094         | 1.13  | 640  | 0.0784          | 0.8496    | 0.8717 | 0.8605 | 0.9800   |
| 0.094         | 1.24  | 704  | 0.0721          | 0.8868    | 0.8527 | 0.8695 | 0.9814   |
| 0.094         | 1.36  | 768  | 0.0700          | 0.8755    | 0.8362 | 0.8554 | 0.9808   |
| 0.094         | 1.47  | 832  | 0.0590          | 0.8662    | 0.8610 | 0.8636 | 0.9822   |
| 0.094         | 1.58  | 896  | 0.0615          | 0.8692    | 0.8764 | 0.8728 | 0.9821   |
| 0.094         | 1.7   | 960  | 0.0670          | 0.8812    | 0.8557 | 0.8683 | 0.9826   |
| 0.0413        | 1.81  | 1024 | 0.0623          | 0.9061    | 0.8557 | 0.8802 | 0.9843   |
| 0.0413        | 1.92  | 1088 | 0.0570          | 0.8891    | 0.8770 | 0.8830 | 0.9833   |
| 0.0413        | 2.04  | 1152 | 0.0643          | 0.8859    | 0.8859 | 0.8859 | 0.9831   |
| 0.0413        | 2.15  | 1216 | 0.0705          | 0.8824    | 0.8740 | 0.8782 | 0.9830   |
| 0.0413        | 2.26  | 1280 | 0.0698          | 0.8818    | 0.8557 | 0.8685 | 0.9824   |
| 0.0413        | 2.37  | 1344 | 0.0826          | 0.8904    | 0.8693 | 0.8797 | 0.9832   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-20000-ner", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert-base-german-cased-20000-ner
================================


This model is a fine-tuned version of bert-base-german-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0826
* Precision: 0.8904
* Recall: 0.8693
* F1: 0.8797
* Accuracy: 0.9832


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: 5e-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: 10


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 10 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 10 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 10### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1