
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-large-uncased-finetuned-lowR100-4-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: 2.2624

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 1    | 5.4063          |
| 5.9654        | 2.0   | 2    | 6.1913          |
| 5.9654        | 3.0   | 3    | 5.8608          |
| 5.5864        | 4.0   | 4    | 4.4613          |
| 5.5864        | 5.0   | 5    | 5.0257          |
| 5.1093        | 6.0   | 6    | 4.7405          |
| 5.1093        | 7.0   | 7    | 4.7516          |
| 4.3965        | 8.0   | 8    | 3.1646          |
| 4.3965        | 9.0   | 9    | 3.7378          |
| 3.7825        | 10.0  | 10   | 2.0073          |
| 3.7825        | 11.0  | 11   | 5.5725          |
| 3.009         | 12.0  | 12   | 3.0077          |
| 3.009         | 13.0  | 13   | 2.6288          |
| 2.7427        | 14.0  | 14   | 2.8630          |
| 2.7427        | 15.0  | 15   | 2.3270          |
| 2.4122        | 16.0  | 16   | 3.3092          |
| 2.4122        | 17.0  | 17   | 2.7499          |
| 2.3707        | 18.0  | 18   | 3.2892          |
| 2.3707        | 19.0  | 19   | 2.9385          |
| 2.6243        | 20.0  | 20   | 1.5626          |
| 2.6243        | 21.0  | 21   | 1.0104          |
| 2.1606        | 22.0  | 22   | 3.3464          |
| 2.1606        | 23.0  | 23   | 3.9334          |
| 2.1419        | 24.0  | 24   | 1.7512          |
| 2.1419        | 25.0  | 25   | 2.5855          |
| 2.2265        | 26.0  | 26   | 2.2896          |
| 2.2265        | 27.0  | 27   | 2.3118          |
| 2.2539        | 28.0  | 28   | 2.9997          |
| 2.2539        | 29.0  | 29   | 3.0682          |
| 2.3866        | 30.0  | 30   | 3.3435          |
| 2.3866        | 31.0  | 31   | 3.5848          |
| 2.1788        | 32.0  | 32   | 2.4658          |
| 2.1788        | 33.0  | 33   | 2.4060          |
| 2.0932        | 34.0  | 34   | 2.8497          |
| 2.0932        | 35.0  | 35   | 3.8500          |
| 1.9799        | 36.0  | 36   | 1.9458          |
| 1.9799        | 37.0  | 37   | 2.4557          |
| 2.0164        | 38.0  | 38   | 3.3370          |
| 2.0164        | 39.0  | 39   | 3.6850          |
| 2.1732        | 40.0  | 40   | 1.7986          |


### 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-lowR100-4-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-lowR100-4-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: 2.2624


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