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# toy

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4798        | 1.0   | 231   | 0.2252          |
| 0.3378        | 2.0   | 462   | 0.1777          |
| 0.1024        | 3.0   | 693   | 0.1586          |
| 0.0736        | 4.0   | 924   | 0.1664          |
| 0.1237        | 5.0   | 1155  | 0.1692          |
| 0.1049        | 6.0   | 1386  | 0.1818          |
| 0.0239        | 7.0   | 1617  | 0.2127          |
| 0.0036        | 8.0   | 1848  | 0.1888          |
| 0.0051        | 9.0   | 2079  | 0.2061          |
| 0.0003        | 10.0  | 2310  | 0.1905          |
| 0.0005        | 11.0  | 2541  | 0.2011          |
| 0.0003        | 12.0  | 2772  | 0.1928          |
| 0.0029        | 13.0  | 3003  | 0.2563          |
| 0.0002        | 14.0  | 3234  | 0.2076          |
| 0.0002        | 15.0  | 3465  | 0.1980          |
| 0.0001        | 16.0  | 3696  | 0.2013          |
| 0.0001        | 17.0  | 3927  | 0.2089          |
| 0.0001        | 18.0  | 4158  | 0.1984          |
| 0.0001        | 19.0  | 4389  | 0.2017          |
| 0.0001        | 20.0  | 4620  | 0.2013          |
| 0.0001        | 21.0  | 4851  | 0.2142          |
| 0.0001        | 22.0  | 5082  | 0.1943          |
| 0.0001        | 23.0  | 5313  | 0.2003          |
| 0.0           | 24.0  | 5544  | 0.2015          |
| 0.0001        | 25.0  | 5775  | 0.2031          |
| 0.0002        | 26.0  | 6006  | 0.2600          |
| 0.0022        | 27.0  | 6237  | 0.2269          |
| 0.0           | 28.0  | 6468  | 0.2125          |
| 0.0           | 29.0  | 6699  | 0.2172          |
| 0.0           | 30.0  | 6930  | 0.2185          |
| 0.0           | 31.0  | 7161  | 0.2004          |
| 0.0           | 32.0  | 7392  | 0.2077          |
| 0.0           | 33.0  | 7623  | 0.2333          |
| 0.0003        | 34.0  | 7854  | 0.2102          |
| 0.0           | 35.0  | 8085  | 0.2095          |
| 0.0           | 36.0  | 8316  | 0.2030          |
| 0.0           | 37.0  | 8547  | 0.2038          |
| 0.0           | 38.0  | 8778  | 0.2062          |
| 0.0           | 39.0  | 9009  | 0.2080          |
| 0.0           | 40.0  | 9240  | 0.2083          |
| 0.0           | 41.0  | 9471  | 0.2063          |
| 0.0           | 42.0  | 9702  | 0.2146          |
| 0.0           | 43.0  | 9933  | 0.2168          |
| 0.0           | 44.0  | 10164 | 0.2112          |
| 0.0           | 45.0  | 10395 | 0.2109          |
| 0.0           | 46.0  | 10626 | 0.2116          |
| 0.0           | 47.0  | 10857 | 0.2122          |
| 0.0           | 48.0  | 11088 | 0.2122          |
| 0.0           | 49.0  | 11319 | 0.2124          |
| 0.0           | 50.0  | 11550 | 0.2124          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0
- Datasets 1.18.3
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "toy", "results": []}]}
transformers pytorch distilbert question-answering generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

toy
===


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2124


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0
* Datasets 1.18.3
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0
* Datasets 1.18.3
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0
* Datasets 1.18.3
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0
* Datasets 1.18.3
* Tokenizers 0.11.6