
<!-- 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-LR100-1-cased-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: 2.2085

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 6.661         | 1.0   | 1    | 6.7088          |
| 7.1425        | 2.0   | 2    | 7.0634          |
| 6.8918        | 3.0   | 3    | 6.2872          |
| 6.1875        | 4.0   | 4    | 5.5826          |
| 5.6201        | 5.0   | 5    | 5.4365          |
| 5.181         | 6.0   | 6    | 3.7720          |
| 5.0548        | 7.0   | 7    | 5.5019          |
| 4.3957        | 8.0   | 8    | 3.2004          |
| 3.993         | 9.0   | 9    | 2.4284          |
| 3.593         | 10.0  | 10   | 3.2126          |
| 3.754         | 11.0  | 11   | 2.7146          |
| 3.061         | 12.0  | 12   | 2.5308          |
| 3.0496        | 13.0  | 13   | 2.8430          |
| 3.1128        | 14.0  | 14   | 1.2934          |
| 2.7098        | 15.0  | 15   | 1.5709          |
| 2.5303        | 16.0  | 16   | 1.9032          |
| 2.3475        | 17.0  | 17   | 2.1788          |
| 2.4054        | 18.0  | 18   | 1.5836          |
| 2.6168        | 19.0  | 19   | 3.7077          |
| 2.5972        | 20.0  | 20   | 2.8996          |
| 2.287         | 21.0  | 21   | 2.1028          |
| 2.1383        | 22.0  | 22   | 2.0755          |
| 2.443         | 23.0  | 23   | 1.6498          |
| 2.0233        | 24.0  | 24   | 2.2023          |
| 2.2446        | 25.0  | 25   | 2.4627          |
| 1.9087        | 26.0  | 26   | 2.3244          |
| 2.1685        | 27.0  | 27   | 1.9509          |
| 1.9055        | 28.0  | 28   | 2.6149          |
| 1.9063        | 29.0  | 29   | 2.0499          |
| 2.3587        | 30.0  | 30   | 1.1757          |
| 2.0389        | 31.0  | 31   | 1.1181          |
| 1.9223        | 32.0  | 32   | 1.6205          |
| 2.0361        | 33.0  | 33   | 1.8381          |
| 2.1823        | 34.0  | 34   | 0.7964          |
| 2.2411        | 35.0  | 35   | 2.0179          |
| 1.8976        | 36.0  | 36   | 1.1467          |
| 1.9321        | 37.0  | 37   | 1.5334          |
| 2.257         | 38.0  | 38   | 2.1575          |
| 2.0543        | 39.0  | 39   | 1.5084          |
| 1.7383        | 40.0  | 40   | 1.8176          |


### 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-LR100-1-cased-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-LR100-1-cased-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: 2.2085


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