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# bert-base-uncased-de-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the germeval_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1374
- Precision: 0.8109
- Recall: 0.7720
- F1: 0.7910
- Accuracy: 0.9786

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

The model was trained on data that follows the [`IOB`](<https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>) convention. Full tagset with indices:

```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.104         | 1.0   | 3000  | 0.0973          | 0.7027    | 0.7323 | 0.7172 | 0.9712   |
| 0.0597        | 2.0   | 6000  | 0.0942          | 0.8135    | 0.7172 | 0.7623 | 0.9766   |
| 0.0345        | 3.0   | 9000  | 0.1051          | 0.7924    | 0.7569 | 0.7742 | 0.9773   |
| 0.0172        | 4.0   | 12000 | 0.1170          | 0.8074    | 0.7628 | 0.7844 | 0.9779   |
| 0.0092        | 5.0   | 15000 | 0.1264          | 0.8068    | 0.7803 | 0.7933 | 0.9788   |
| 0.0035        | 6.0   | 18000 | 0.1374          | 0.8109    | 0.7720 | 0.7910 | 0.9786   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
{"language": ["de"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["germeval_14"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Mein Name ist Wolfgang und ich  lebe in Berlin", "example_title": "Example 1"}, {"text": "Mein Name ist Sarah und ich lebe in London", "example_title": "Example 2"}, {"text": "Mein Name ist Clara und ich lebe in Berkeley, California.", "example_title": "Example 3"}], "model-index": [{"name": "bert-base-uncased-de-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "germeval_14", "type": "germeval_14", "config": "germeval_14", "split": "test", "args": "germeval_14"}, "metrics": [{"type": "precision", "value": 0.8109431552054502, "name": "Precision"}, {"type": "recall", "value": 0.771990271584921, "name": "Recall"}, {"type": "f1", "value": 0.7909874364032811, "name": "F1"}, {"type": "accuracy", "value": 0.9786213727432309, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer de dataset:germeval_14 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #de #dataset-germeval_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-de-ner
========================


This model is a fine-tuned version of bert-base-uncased on the germeval\_14 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1374
* Precision: 0.8109
* Recall: 0.7720
* F1: 0.7910
* Accuracy: 0.9786


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


The model was trained on data that follows the 'IOB'>) convention. Full tagset with indices:


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #de #dataset-germeval_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #de #dataset-germeval_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2