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# bert-large-uncased-finetuned-lowR100-5-uncased-DA-20

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9006

## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5116        | 1.0   | 1    | 6.5297          |
| 6.6949        | 2.0   | 2    | 6.9289          |
| 6.0946        | 3.0   | 3    | 7.6464          |
| 5.8742        | 4.0   | 4    | 4.8191          |
| 5.4365        | 5.0   | 5    | 6.1273          |
| 5.171         | 6.0   | 6    | 4.5528          |
| 4.4944        | 7.0   | 7    | 4.8541          |
| 4.1146        | 8.0   | 8    | 3.4321          |
| 3.4689        | 9.0   | 9    | 2.4818          |
| 3.6228        | 10.0  | 10   | 2.4444          |
| 3.147         | 11.0  | 11   | 1.0668          |
| 2.969         | 12.0  | 12   | 3.5394          |
| 2.9788        | 13.0  | 13   | 3.1681          |
| 2.9108        | 14.0  | 14   | 1.6325          |
| 2.9377        | 15.0  | 15   | 2.0480          |
| 2.6179        | 16.0  | 16   | 2.6157          |
| 2.8978        | 17.0  | 17   | 3.3663          |
| 2.6496        | 18.0  | 18   | 2.6341          |
| 2.592         | 19.0  | 19   | 2.6462          |
| 2.5212        | 20.0  | 20   | 2.2172          |
| 2.402         | 21.0  | 21   | 3.3419          |
| 2.3146        | 22.0  | 22   | 1.8095          |
| 2.5215        | 23.0  | 23   | 2.7622          |
| 2.1736        | 24.0  | 24   | 3.9402          |
| 2.4366        | 25.0  | 25   | 2.3742          |
| 2.1603        | 26.0  | 26   | 2.4520          |
| 2.21          | 27.0  | 27   | 3.8185          |
| 2.1954        | 28.0  | 28   | 4.0015          |
| 2.6556        | 29.0  | 29   | 2.4132          |
| 2.3936        | 30.0  | 30   | 3.8690          |
| 2.2442        | 31.0  | 31   | 3.7408          |
| 2.2486        | 32.0  | 32   | 2.5657          |
| 2.5066        | 33.0  | 33   | 3.6632          |
| 2.0527        | 34.0  | 34   | 2.9892          |
| 2.6207        | 35.0  | 35   | 3.5594          |
| 2.296         | 36.0  | 36   | 2.3785          |
| 2.4068        | 37.0  | 37   | 3.6126          |
| 2.257         | 38.0  | 38   | 1.0477          |
| 2.0597        | 39.0  | 39   | 1.5386          |
| 2.1702        | 40.0  | 40   | 2.4686          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-uncased-finetuned-lowR100-5-uncased-DA-20", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-large-uncased-finetuned-lowR100-5-uncased-DA-20
====================================================


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


* Loss: 1.9006


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: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #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: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #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: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2