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# gelectra-base-germanquad-finetuned-squad

This model is a fine-tuned version of [deepset/gelectra-base-germanquad](https://huggingface.co/deepset/gelectra-base-germanquad) on the germanquad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5011

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 180  | 1.7745          |
| No log        | 2.0   | 360  | 1.8828          |
| 1.0639        | 3.0   | 540  | 1.9312          |
| 1.0639        | 4.0   | 720  | 2.1928          |
| 1.0639        | 5.0   | 900  | 2.2980          |
| 0.6008        | 6.0   | 1080 | 2.3243          |
| 0.6008        | 7.0   | 1260 | 2.3856          |
| 0.6008        | 8.0   | 1440 | 2.4762          |
| 0.4335        | 9.0   | 1620 | 2.4866          |
| 0.4335        | 10.0  | 1800 | 2.5011          |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["germanquad"], "model-index": [{"name": "gelectra-base-germanquad-finetuned-squad", "results": []}]}
transformers pytorch tensorboard electra question-answering generated_from_trainer dataset:germanquad license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-germanquad #license-mit #endpoints_compatible #region-us 

gelectra-base-germanquad-finetuned-squad
========================================


This model is a fine-tuned version of deepset/gelectra-base-germanquad on the germanquad dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5011


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: 10


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-germanquad #license-mit #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: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-germanquad #license-mit #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: 10### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2