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# german-bert-finetuned-ner

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0907
- Precision: 0.8143
- Recall: 0.8180
- F1: 0.8161
- Accuracy: 0.9893

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 120  | 0.0715          | 0.7092    | 0.8    | 0.7518 | 0.9881   |
| No log        | 2.0   | 240  | 0.0935          | 0.6588    | 0.8157 | 0.7289 | 0.9845   |
| No log        | 3.0   | 360  | 0.0664          | 0.7303    | 0.7910 | 0.7594 | 0.9885   |
| No log        | 4.0   | 480  | 0.0647          | 0.6691    | 0.8225 | 0.7379 | 0.9872   |
| 0.0027        | 5.0   | 600  | 0.0752          | 0.8409    | 0.7955 | 0.8176 | 0.9900   |
| 0.0027        | 6.0   | 720  | 0.0658          | 0.7105    | 0.8382 | 0.7691 | 0.9887   |
| 0.0027        | 7.0   | 840  | 0.0818          | 0.8364    | 0.8045 | 0.8202 | 0.9896   |
| 0.0027        | 8.0   | 960  | 0.0791          | 0.7660    | 0.8315 | 0.7974 | 0.9892   |
| 0.0013        | 9.0   | 1080 | 0.0791          | 0.7730    | 0.8112 | 0.7917 | 0.9893   |
| 0.0013        | 10.0  | 1200 | 0.0809          | 0.8117    | 0.8135 | 0.8126 | 0.9889   |
| 0.0013        | 11.0  | 1320 | 0.0851          | 0.8085    | 0.8157 | 0.8121 | 0.9894   |
| 0.0013        | 12.0  | 1440 | 0.0875          | 0.8361    | 0.8022 | 0.8188 | 0.9894   |
| 0.0004        | 13.0  | 1560 | 0.0892          | 0.8395    | 0.8112 | 0.8251 | 0.9893   |
| 0.0004        | 14.0  | 1680 | 0.0857          | 0.7978    | 0.8337 | 0.8154 | 0.9894   |
| 0.0004        | 15.0  | 1800 | 0.0848          | 0.7931    | 0.8270 | 0.8097 | 0.9895   |
| 0.0004        | 16.0  | 1920 | 0.0867          | 0.8053    | 0.8180 | 0.8116 | 0.9896   |
| 0.0002        | 17.0  | 2040 | 0.0866          | 0.7842    | 0.8247 | 0.8039 | 0.9891   |
| 0.0002        | 18.0  | 2160 | 0.0885          | 0.7952    | 0.8202 | 0.8075 | 0.9893   |
| 0.0002        | 19.0  | 2280 | 0.0895          | 0.7948    | 0.8180 | 0.8062 | 0.9894   |
| 0.0002        | 20.0  | 2400 | 0.0907          | 0.8143    | 0.8180 | 0.8161 | 0.9893   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

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

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


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0907
* Precision: 0.8143
* Recall: 0.8180
* F1: 0.8161
* Accuracy: 0.9893


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1