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# distilbert-base-uncased-finetuned-nitro

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3930

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8306        | 1.0   | 5    | 3.2319          |
| 2.5782        | 2.0   | 10   | 2.4410          |
| 2.6843        | 3.0   | 15   | 2.2280          |
| 2.4366        | 4.0   | 20   | 1.9308          |
| 2.3116        | 5.0   | 25   | 2.3071          |
| 2.2877        | 6.0   | 30   | 2.5556          |
| 2.4158        | 7.0   | 35   | 2.6973          |
| 2.2463        | 8.0   | 40   | 1.9751          |
| 2.2046        | 9.0   | 45   | 2.2329          |
| 1.675         | 10.0  | 50   | 2.3977          |
| 2.398         | 11.0  | 55   | 2.1521          |
| 2.1949        | 12.0  | 60   | 2.5294          |
| 1.9113        | 13.0  | 65   | 2.1196          |
| 2.1362        | 14.0  | 70   | 2.3208          |
| 2.8067        | 15.0  | 75   | 3.0091          |
| 1.8936        | 16.0  | 80   | 2.3815          |
| 1.785         | 17.0  | 85   | 2.6941          |
| 2.0525        | 18.0  | 90   | 2.5582          |
| 2.2101        | 19.0  | 95   | 2.6840          |
| 1.8584        | 20.0  | 100  | 2.2681          |
| 1.7072        | 21.0  | 105  | 2.3054          |
| 1.603         | 22.0  | 110  | 2.7518          |
| 1.8076        | 23.0  | 115  | 2.3547          |
| 1.9246        | 24.0  | 120  | 2.1844          |
| 1.6282        | 25.0  | 125  | 1.5606          |
| 2.087         | 26.0  | 130  | 2.1277          |
| 1.7423        | 27.0  | 135  | 1.8514          |
| 1.8819        | 28.0  | 140  | 1.9988          |
| 2.2313        | 29.0  | 145  | 1.8946          |
| 2.108         | 30.0  | 150  | 2.3753          |
| 1.9609        | 31.0  | 155  | 2.5666          |
| 2.2095        | 32.0  | 160  | 1.8381          |
| 2.1499        | 33.0  | 165  | 2.4213          |
| 1.5674        | 34.0  | 170  | 2.2492          |
| 2.0289        | 35.0  | 175  | 2.1717          |
| 1.9795        | 36.0  | 180  | 2.3377          |
| 1.977         | 37.0  | 185  | 1.8292          |
| 1.6325        | 38.0  | 190  | 2.5112          |
| 1.8168        | 39.0  | 195  | 2.2321          |
| 1.4628        | 40.0  | 200  | 2.3930          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-nitro", "results": []}]}
transformers pytorch distilbert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-nitro
=======================================


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.3930


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 200
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2