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# my_awesome_model

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the gnad10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3414
- F1: 0.9001

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5884        | 1.0   | 578  | 0.3510          | 0.8940 |
| 0.2389        | 2.0   | 1156 | 0.3414          | 0.9001 |


### Framework versions

- Transformers 4.26.0
- Pytorch 2.0.0.dev20230126+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["gnad10"], "metrics": ["f1"], "model-index": [{"name": "my_awesome_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "gnad10", "type": "gnad10", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "f1", "value": 0.9001230044993416, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:gnad10 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-gnad10 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

my\_awesome\_model
==================


This model is a fine-tuned version of bert-base-german-cased on the gnad10 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3414
* F1: 0.9001


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: 2


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0.dev20230126+cu117
* 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: 2 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0.dev20230126+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-gnad10 #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: 2 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0.dev20230126+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-gnad10 #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: 2### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 2.0.0.dev20230126+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2