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# bert-german-ner

This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3196
- Precision: 0.8334
- Recall: 0.8620
- F1: 0.8474
- Accuracy: 0.9292

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 300  | 0.3617          | 0.7310    | 0.7733 | 0.7516 | 0.8908   |
| 0.5428        | 2.0   | 600  | 0.2897          | 0.7789    | 0.8395 | 0.8081 | 0.9132   |
| 0.5428        | 3.0   | 900  | 0.2805          | 0.8147    | 0.8465 | 0.8303 | 0.9221   |
| 0.2019        | 4.0   | 1200 | 0.2816          | 0.8259    | 0.8498 | 0.8377 | 0.9260   |
| 0.1215        | 5.0   | 1500 | 0.2942          | 0.8332    | 0.8599 | 0.8463 | 0.9285   |
| 0.1215        | 6.0   | 1800 | 0.3053          | 0.8293    | 0.8619 | 0.8452 | 0.9287   |
| 0.0814        | 7.0   | 2100 | 0.3190          | 0.8249    | 0.8634 | 0.8437 | 0.9267   |
| 0.0814        | 8.0   | 2400 | 0.3196          | 0.8334    | 0.8620 | 0.8474 | 0.9292   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dbmdz/bert-base-german-cased", "model-index": [{"name": "bert-german-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "validation", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.8333588604686782, "name": "Precision"}, {"type": "recall", "value": 0.8620088719898605, "name": "Recall"}, {"type": "f1", "value": 0.8474417880227396, "name": "F1"}, {"type": "accuracy", "value": 0.9292245320451997, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer dataset:conll2003 base_model:dbmdz/bert-base-german-cased license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-dbmdz/bert-base-german-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-german-ner
===============


This model is a fine-tuned version of dbmdz/bert-base-german-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3196
* Precision: 0.8334
* Recall: 0.8620
* F1: 0.8474
* Accuracy: 0.9292


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: 8


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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: 8 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-dbmdz/bert-base-german-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 8 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-dbmdz/bert-base-german-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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: 8### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2