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# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR100-40

This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4444

## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1928        | 1.0   | 1    | 5.0343          |
| 3.8865        | 2.0   | 2    | 4.7751          |
| 4.0526        | 3.0   | 3    | 2.2212          |
| 2.3444        | 4.0   | 4    | 1.6810          |
| 1.596         | 5.0   | 5    | 1.3135          |
| 1.6805        | 6.0   | 6    | 1.2568          |
| 1.1736        | 7.0   | 7    | 1.5288          |
| 1.2663        | 8.0   | 8    | 1.4556          |
| 1.3703        | 9.0   | 9    | 1.1139          |
| 0.9768        | 10.0  | 10   | 1.0658          |
| 1.0132        | 11.0  | 11   | 1.2556          |
| 0.9896        | 12.0  | 12   | 1.1046          |
| 1.1184        | 13.0  | 13   | 1.0522          |
| 0.8142        | 14.0  | 14   | 1.3122          |
| 0.706         | 15.0  | 15   | 1.0713          |
| 0.7227        | 16.0  | 16   | 1.4111          |
| 0.7169        | 17.0  | 17   | 0.5603          |
| 0.7922        | 18.0  | 18   | 1.0911          |
| 0.7763        | 19.0  | 19   | 0.6882          |
| 0.5832        | 20.0  | 20   | 1.4459          |
| 0.7265        | 21.0  | 21   | 1.5459          |
| 0.7249        | 22.0  | 22   | 0.9200          |
| 0.5397        | 23.0  | 23   | 1.0976          |
| 0.5063        | 24.0  | 24   | 1.1201          |
| 0.6569        | 25.0  | 25   | 1.0701          |
| 0.472         | 26.0  | 26   | 1.7735          |
| 0.6124        | 27.0  | 27   | 1.3597          |
| 0.6042        | 28.0  | 28   | 0.9292          |
| 0.5232        | 29.0  | 29   | 1.4994          |
| 0.4961        | 30.0  | 30   | 1.2059          |
| 0.371         | 31.0  | 31   | 1.2648          |
| 0.4746        | 32.0  | 32   | 1.0907          |
| 0.4901        | 33.0  | 33   | 1.2564          |
| 0.5066        | 34.0  | 34   | 1.9231          |
| 0.6352        | 35.0  | 35   | 1.0160          |
| 0.5672        | 36.0  | 36   | 1.2958          |
| 0.5139        | 37.0  | 37   | 0.9384          |
| 0.5583        | 38.0  | 38   | 1.9518          |
| 0.5443        | 39.0  | 39   | 1.4243          |
| 0.5935        | 40.0  | 40   | 1.3882          |


### Framework versions

- Transformers 4.26.1
- 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-refute-no-label-40-2nd-test-LR100-40", "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-support-refute-no-label-40-2nd-test-LR100-40
===================================================================


This model is a fine-tuned version of jojoUla/bert-large-cased-sigir-support-refute-no-label-40 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4444


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: 64
* eval\_batch\_size: 64
* 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 4.26.1
* 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: 64
* eval\_batch\_size: 64
* 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 4.26.1
* 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: 64
* eval\_batch\_size: 64
* 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 4.26.1
* 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: 64
* eval\_batch\_size: 64
* 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2