
<!-- 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-support-no-label-20-sigir-tune2nd-LR10-labelled-30

This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-no-label-20](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-20) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3995

## 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: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1303        | 1.0   | 1    | 3.2415          |
| 2.3107        | 2.0   | 2    | 2.1225          |
| 1.2824        | 3.0   | 3    | 2.2623          |
| 1.0548        | 4.0   | 4    | 0.5449          |
| 1.1366        | 5.0   | 5    | 1.1446          |
| 0.5947        | 6.0   | 6    | 0.3811          |
| 0.4889        | 7.0   | 7    | 1.6445          |
| 1.2689        | 8.0   | 8    | 1.7214          |
| 0.8074        | 9.0   | 9    | 2.3152          |
| 0.7084        | 10.0  | 10   | 0.9325          |
| 1.0307        | 11.0  | 11   | 2.4217          |
| 0.7119        | 12.0  | 12   | 2.6455          |
| 1.0052        | 13.0  | 13   | 1.1594          |
| 0.7125        | 14.0  | 14   | 1.2795          |
| 0.4732        | 15.0  | 15   | 0.1245          |
| 0.8829        | 16.0  | 16   | 1.8585          |
| 0.7079        | 17.0  | 17   | 1.6644          |
| 0.6243        | 18.0  | 18   | 1.6117          |
| 1.2438        | 19.0  | 19   | 2.3044          |
| 1.0812        | 20.0  | 20   | 4.5037          |
| 0.7003        | 21.0  | 21   | 1.5862          |
| 0.867         | 22.0  | 22   | 2.1851          |
| 0.9098        | 23.0  | 23   | 1.6055          |
| 0.6214        | 24.0  | 24   | 2.6699          |
| 0.282         | 25.0  | 25   | 1.3515          |
| 0.1888        | 26.0  | 26   | 2.3864          |
| 0.6863        | 27.0  | 27   | 1.2444          |
| 0.8527        | 28.0  | 28   | 1.9603          |
| 0.9416        | 29.0  | 29   | 3.7045          |
| 0.8302        | 30.0  | 30   | 0.9336          |


### 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-support-no-label-20-sigir-tune2nd-LR10-labelled-30", "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-support-no-label-20-sigir-tune2nd-LR10-labelled-30
=========================================================================


This model is a fine-tuned version of jojoUla/bert-large-cased-sigir-support-no-label-20 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.3995


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: 30.0
* 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: 30.0
* 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: 30.0
* 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: 30.0
* 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