
<!-- 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-cased-sigir-LR100-0-prepend-40

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1764

## 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: 4e-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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6453        | 1.0   | 3    | 2.0522          |
| 2.0488        | 2.0   | 6    | 1.7600          |
| 1.9917        | 3.0   | 9    | 2.3036          |
| 1.6084        | 4.0   | 12   | 1.4050          |
| 1.856         | 5.0   | 15   | 1.3598          |
| 1.6471        | 6.0   | 18   | 1.5274          |
| 1.2358        | 7.0   | 21   | 1.6642          |
| 1.4355        | 8.0   | 24   | 1.6109          |
| 1.5753        | 9.0   | 27   | 1.8690          |
| 1.5374        | 10.0  | 30   | 1.7986          |
| 1.5063        | 11.0  | 33   | 1.4979          |
| 1.2185        | 12.0  | 36   | 0.7390          |
| 1.6042        | 13.0  | 39   | 1.1280          |
| 1.1938        | 14.0  | 42   | 1.1252          |
| 1.3215        | 15.0  | 45   | 1.6827          |
| 1.0789        | 16.0  | 48   | 1.6349          |
| 1.095         | 17.0  | 51   | 2.6303          |
| 1.0088        | 18.0  | 54   | 0.9429          |
| 1.015         | 19.0  | 57   | 1.4165          |
| 1.2432        | 20.0  | 60   | 2.1061          |
| 1.3365        | 21.0  | 63   | 1.5785          |
| 1.2704        | 22.0  | 66   | 2.1850          |
| 0.972         | 23.0  | 69   | 1.7769          |
| 0.9052        | 24.0  | 72   | 1.5376          |
| 0.976         | 25.0  | 75   | 2.1072          |
| 1.1134        | 26.0  | 78   | 2.4425          |
| 0.8328        | 27.0  | 81   | 1.5937          |
| 1.1662        | 28.0  | 84   | 1.3542          |
| 0.8575        | 29.0  | 87   | 1.2236          |
| 0.728         | 30.0  | 90   | 1.2229          |
| 1.1601        | 31.0  | 93   | 2.3723          |
| 0.9426        | 32.0  | 96   | 1.6974          |
| 0.8246        | 33.0  | 99   | 1.6610          |
| 0.9777        | 34.0  | 102  | 1.1179          |
| 0.7588        | 35.0  | 105  | 1.8809          |
| 0.6929        | 36.0  | 108  | 1.9128          |
| 0.6794        | 37.0  | 111  | 1.2689          |
| 0.811         | 38.0  | 114  | 1.6715          |
| 0.6805        | 39.0  | 117  | 2.0424          |
| 0.9157        | 40.0  | 120  | 1.4210          |


### 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-cased-sigir-LR100-0-prepend-40", "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-cased-sigir-LR100-0-prepend-40
=========================================


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


* Loss: 1.1764


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: 4e-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: 4e-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: 4e-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: 4e-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