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

This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.8871
- Recall: 0.9013
- F1: 0.8941
- Accuracy: 0.9848

## 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: 96
- eval_batch_size: 96
- 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        | 0.34  | 64   | 0.0573          | 0.8859    | 0.8526 | 0.8689 | 0.9837   |
| No log        | 0.68  | 128  | 0.0654          | 0.8107    | 0.8957 | 0.8511 | 0.9808   |
| No log        | 1.02  | 192  | 0.0531          | 0.8654    | 0.8846 | 0.8749 | 0.9842   |
| No log        | 1.35  | 256  | 0.0467          | 0.8847    | 0.8853 | 0.8850 | 0.9857   |
| No log        | 1.69  | 320  | 0.0466          | 0.9102    | 0.8883 | 0.8992 | 0.9864   |
| No log        | 2.03  | 384  | 0.0467          | 0.8794    | 0.8951 | 0.8872 | 0.9854   |
| No log        | 2.37  | 448  | 0.0520          | 0.8864    | 0.9001 | 0.8932 | 0.9851   |
| 0.0531        | 2.71  | 512  | 0.0549          | 0.8894    | 0.8877 | 0.8885 | 0.9854   |
| 0.0531        | 3.05  | 576  | 0.0534          | 0.8942    | 0.8920 | 0.8931 | 0.9857   |
| 0.0531        | 3.39  | 640  | 0.0526          | 0.8917    | 0.8994 | 0.8956 | 0.9856   |
| 0.0531        | 3.72  | 704  | 0.0576          | 0.9049    | 0.8976 | 0.9012 | 0.9857   |
| 0.0531        | 4.06  | 768  | 0.0700          | 0.8529    | 0.9229 | 0.8865 | 0.9830   |
| 0.0531        | 4.4   | 832  | 0.0657          | 0.8716    | 0.9167 | 0.8936 | 0.9840   |
| 0.0531        | 4.74  | 896  | 0.0617          | 0.8871    | 0.9013 | 0.8941 | 0.9848   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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

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


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


* Loss: 0.0617
* Precision: 0.8871
* Recall: 0.9013
* F1: 0.8941
* Accuracy: 0.9848


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: 96
* eval\_batch\_size: 96
* 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 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* 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 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #base_model-dbmdz/bert-base-german-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* 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 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #base_model-dbmdz/bert-base-german-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* 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 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3