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# bert-base-german-cased-fine-tuned-ner

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germa_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0966
- Precision: 0.8089
- Recall: 0.8728
- F1: 0.8397
- Accuracy: 0.9749

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.159         | 1.0   | 737  | 0.0922          | 0.7472    | 0.8461 | 0.7936 | 0.9703   |
| 0.0714        | 2.0   | 1474 | 0.0916          | 0.7886    | 0.8713 | 0.8279 | 0.9731   |
| 0.0319        | 3.0   | 2211 | 0.0966          | 0.8089    | 0.8728 | 0.8397 | 0.9749   |


### 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"], "datasets": ["germa_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-fine-tuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "germa_ner", "type": "germa_ner", "args": "default"}, "metrics": [{"type": "precision", "value": 0.8089260808926081, "name": "Precision"}, {"type": "recall", "value": 0.872836719337848, "name": "Recall"}, {"type": "f1", "value": 0.8396670285921101, "name": "F1"}, {"type": "accuracy", "value": 0.9748511630761677, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:germa_ner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-germa_ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-german-cased-fine-tuned-ner
=====================================


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


* Loss: 0.0966
* Precision: 0.8089
* Recall: 0.8728
* F1: 0.8397
* Accuracy: 0.9749


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: 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: 3


### 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: 2e-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: 3 ### 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 #dataset-germa_ner #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: 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: 3 ### 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 #dataset-germa_ner #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: 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: 3### Training results### Framework versions


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