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# auto_bot

This model is a fine-tuned version of [deepset/gelectra-base-germanquad](https://huggingface.co/deepset/gelectra-base-germanquad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1041

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 1    | 2.7932          |
| No log        | 2.0   | 2    | 2.8536          |
| No log        | 3.0   | 3    | 2.8856          |
| No log        | 4.0   | 4    | 2.9470          |
| No log        | 5.0   | 5    | 2.9948          |
| No log        | 6.0   | 6    | 3.0745          |
| No log        | 7.0   | 7    | 3.1467          |
| No log        | 8.0   | 8    | 3.2018          |
| No log        | 9.0   | 9    | 3.2549          |
| No log        | 10.0  | 10   | 3.2827          |
| No log        | 11.0  | 11   | 3.2572          |
| No log        | 12.0  | 12   | 3.1982          |
| No log        | 13.0  | 13   | 3.1229          |
| No log        | 14.0  | 14   | 3.0666          |
| No log        | 15.0  | 15   | 3.0209          |
| No log        | 16.0  | 16   | 2.9706          |
| No log        | 17.0  | 17   | 2.9060          |
| No log        | 18.0  | 18   | 2.8304          |
| No log        | 19.0  | 19   | 2.7950          |
| No log        | 20.0  | 20   | 2.7435          |
| No log        | 21.0  | 21   | 2.7194          |
| No log        | 22.0  | 22   | 2.7012          |
| No log        | 23.0  | 23   | 2.6803          |
| No log        | 24.0  | 24   | 2.6647          |
| No log        | 25.0  | 25   | 2.6490          |
| No log        | 26.0  | 26   | 2.6476          |
| No log        | 27.0  | 27   | 2.6626          |
| No log        | 28.0  | 28   | 2.6928          |
| No log        | 29.0  | 29   | 2.7398          |
| No log        | 30.0  | 30   | 2.7371          |
| No log        | 31.0  | 31   | 2.7501          |
| No log        | 32.0  | 32   | 2.7698          |
| No log        | 33.0  | 33   | 2.7965          |
| No log        | 34.0  | 34   | 2.8332          |
| No log        | 35.0  | 35   | 2.8756          |
| No log        | 36.0  | 36   | 2.9246          |
| No log        | 37.0  | 37   | 2.9754          |
| No log        | 38.0  | 38   | 3.0306          |
| No log        | 39.0  | 39   | 3.0738          |
| No log        | 40.0  | 40   | 3.1037          |
| No log        | 41.0  | 41   | 3.1197          |
| No log        | 42.0  | 42   | 3.1269          |
| No log        | 43.0  | 43   | 3.1520          |
| No log        | 44.0  | 44   | 3.1566          |
| No log        | 45.0  | 45   | 3.1706          |
| No log        | 46.0  | 46   | 3.1815          |
| No log        | 47.0  | 47   | 3.1709          |
| No log        | 48.0  | 48   | 3.1615          |
| No log        | 49.0  | 49   | 3.1367          |
| No log        | 50.0  | 50   | 3.1303          |
| No log        | 51.0  | 51   | 3.1252          |
| No log        | 52.0  | 52   | 3.1182          |
| No log        | 53.0  | 53   | 3.1105          |
| No log        | 54.0  | 54   | 3.0899          |
| No log        | 55.0  | 55   | 3.0767          |
| No log        | 56.0  | 56   | 3.0598          |
| No log        | 57.0  | 57   | 3.0419          |
| No log        | 58.0  | 58   | 3.0298          |
| No log        | 59.0  | 59   | 3.0371          |
| No log        | 60.0  | 60   | 3.0315          |
| No log        | 61.0  | 61   | 3.0238          |
| No log        | 62.0  | 62   | 3.0137          |
| No log        | 63.0  | 63   | 3.0129          |
| No log        | 64.0  | 64   | 3.0188          |
| No log        | 65.0  | 65   | 3.0242          |
| No log        | 66.0  | 66   | 3.0289          |
| No log        | 67.0  | 67   | 3.0293          |
| No log        | 68.0  | 68   | 3.0229          |
| No log        | 69.0  | 69   | 3.0187          |
| No log        | 70.0  | 70   | 3.0121          |
| No log        | 71.0  | 71   | 3.0028          |
| No log        | 72.0  | 72   | 2.9944          |
| No log        | 73.0  | 73   | 2.9858          |
| No log        | 74.0  | 74   | 2.9779          |
| No log        | 75.0  | 75   | 2.9792          |
| No log        | 76.0  | 76   | 2.9778          |
| No log        | 77.0  | 77   | 2.9800          |
| No log        | 78.0  | 78   | 2.9846          |
| No log        | 79.0  | 79   | 2.9932          |
| No log        | 80.0  | 80   | 3.0056          |
| No log        | 81.0  | 81   | 3.0129          |
| No log        | 82.0  | 82   | 3.0216          |
| No log        | 83.0  | 83   | 3.0312          |
| No log        | 84.0  | 84   | 3.0401          |
| No log        | 85.0  | 85   | 3.0507          |
| No log        | 86.0  | 86   | 3.0582          |
| No log        | 87.0  | 87   | 3.0625          |
| No log        | 88.0  | 88   | 3.0660          |
| No log        | 89.0  | 89   | 3.0694          |
| No log        | 90.0  | 90   | 3.0757          |
| No log        | 91.0  | 91   | 3.0818          |
| No log        | 92.0  | 92   | 3.0873          |
| No log        | 93.0  | 93   | 3.0904          |
| No log        | 94.0  | 94   | 3.0936          |
| No log        | 95.0  | 95   | 3.0975          |
| No log        | 96.0  | 96   | 3.1001          |
| No log        | 97.0  | 97   | 3.1019          |
| No log        | 98.0  | 98   | 3.1030          |
| No log        | 99.0  | 99   | 3.1038          |
| No log        | 100.0 | 100  | 3.1041          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "auto_bot", "results": []}]}
transformers pytorch electra question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

auto\_bot
=========


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


* Loss: 3.1041


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: 100


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* 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: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #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: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #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: 100### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2