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# bert-large-cased-sigir-LR100-1-cased-150

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: 2.4908

## 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: 0.00015
- 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: 150.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0223        | 1.0   | 1    | 6.3939          |
| 7.139         | 2.0   | 2    | 7.0236          |
| 7.0156        | 3.0   | 3    | 7.0893          |
| 7.2089        | 4.0   | 4    | 5.3251          |
| 5.7375        | 5.0   | 5    | 3.5431          |
| 3.8594        | 6.0   | 6    | 3.2612          |
| 3.2379        | 7.0   | 7    | 2.8294          |
| 3.2598        | 8.0   | 8    | 3.1189          |
| 3.0582        | 9.0   | 9    | 2.3748          |
| 2.6474        | 10.0  | 10   | 3.0741          |
| 3.0331        | 11.0  | 11   | 1.8630          |
| 2.7435        | 12.0  | 12   | 2.5527          |
| 2.3269        | 13.0  | 13   | 2.1300          |
| 2.0739        | 14.0  | 14   | 2.6337          |
| 1.9651        | 15.0  | 15   | 1.8872          |
| 2.1553        | 16.0  | 16   | 2.2753          |
| 1.828         | 17.0  | 17   | 2.6648          |
| 1.6217        | 18.0  | 18   | 2.5176          |
| 1.681         | 19.0  | 19   | 1.0566          |
| 1.5415        | 20.0  | 20   | 1.7287          |
| 1.1874        | 21.0  | 21   | 1.8245          |
| 1.1892        | 22.0  | 22   | 3.3104          |
| 1.3973        | 23.0  | 23   | 1.7072          |
| 1.132         | 24.0  | 24   | 2.7579          |
| 1.2451        | 25.0  | 25   | 2.0380          |
| 1.2669        | 26.0  | 26   | 2.7239          |
| 1.1353        | 27.0  | 27   | 2.3039          |
| 1.319         | 28.0  | 28   | 1.9489          |
| 1.1246        | 29.0  | 29   | 2.1581          |
| 1.332         | 30.0  | 30   | 2.5226          |
| 1.2593        | 31.0  | 31   | 2.1640          |
| 1.2299        | 32.0  | 32   | 1.1443          |
| 1.0047        | 33.0  | 33   | 1.5572          |
| 0.7948        | 34.0  | 34   | 1.8577          |
| 1.151         | 35.0  | 35   | 1.9391          |
| 0.905         | 36.0  | 36   | 2.2505          |
| 1.0517        | 37.0  | 37   | 2.4417          |
| 1.1055        | 38.0  | 38   | 1.9173          |
| 0.837         | 39.0  | 39   | 2.2970          |
| 0.911         | 40.0  | 40   | 1.6180          |
| 0.7643        | 41.0  | 41   | 1.4066          |
| 0.8754        | 42.0  | 42   | 2.7884          |
| 1.2184        | 43.0  | 43   | 2.3925          |
| 0.8623        | 44.0  | 44   | 2.0238          |
| 0.7886        | 45.0  | 45   | 2.7872          |
| 0.7089        | 46.0  | 46   | 2.7488          |
| 0.5684        | 47.0  | 47   | 2.3370          |
| 0.7299        | 48.0  | 48   | 2.1650          |
| 0.6906        | 49.0  | 49   | 2.7732          |
| 0.6876        | 50.0  | 50   | 3.0168          |
| 0.3967        | 51.0  | 51   | 2.1091          |
| 0.6191        | 52.0  | 52   | 1.0602          |
| 0.5068        | 53.0  | 53   | 3.1522          |
| 0.7189        | 54.0  | 54   | 3.0988          |
| 0.6768        | 55.0  | 55   | 1.7392          |
| 0.6247        | 56.0  | 56   | 1.8365          |
| 0.5057        | 57.0  | 57   | 2.8095          |
| 0.65          | 58.0  | 58   | 2.8217          |
| 0.5398        | 59.0  | 59   | 2.3459          |
| 0.5703        | 60.0  | 60   | 1.6460          |
| 0.5543        | 61.0  | 61   | 1.7489          |
| 0.5031        | 62.0  | 62   | 2.4358          |
| 0.447         | 63.0  | 63   | 2.5432          |
| 0.5056        | 64.0  | 64   | 3.0374          |
| 0.5161        | 65.0  | 65   | 2.2071          |
| 0.4509        | 66.0  | 66   | 3.4950          |
| 0.3973        | 67.0  | 67   | 3.0061          |
| 0.4469        | 68.0  | 68   | 2.7547          |
| 0.4062        | 69.0  | 69   | 3.3072          |
| 0.2518        | 70.0  | 70   | 2.1072          |
| 0.2339        | 71.0  | 71   | 2.1484          |
| 0.5058        | 72.0  | 72   | 3.2433          |
| 0.2827        | 73.0  | 73   | 1.6271          |
| 0.3282        | 74.0  | 74   | 2.1436          |
| 0.3415        | 75.0  | 75   | 2.8307          |
| 0.437         | 76.0  | 76   | 2.5679          |
| 0.5487        | 77.0  | 77   | 1.1248          |
| 0.3464        | 78.0  | 78   | 3.0531          |
| 0.3801        | 79.0  | 79   | 2.9731          |
| 0.3805        | 80.0  | 80   | 1.8667          |
| 0.2179        | 81.0  | 81   | 3.1658          |
| 0.2429        | 82.0  | 82   | 1.8698          |
| 0.3341        | 83.0  | 83   | 2.6730          |
| 0.2662        | 84.0  | 84   | 2.1040          |
| 0.4266        | 85.0  | 85   | 3.5671          |
| 0.3154        | 86.0  | 86   | 2.4055          |
| 0.2319        | 87.0  | 87   | 1.4615          |
| 0.1723        | 88.0  | 88   | 2.7438          |
| 0.3301        | 89.0  | 89   | 2.8391          |
| 0.2512        | 90.0  | 90   | 2.3172          |
| 0.3017        | 91.0  | 91   | 2.9586          |
| 0.4352        | 92.0  | 92   | 2.3134          |
| 0.3637        | 93.0  | 93   | 2.2590          |
| 0.3656        | 94.0  | 94   | 3.0915          |
| 0.3036        | 95.0  | 95   | 2.0150          |
| 0.2135        | 96.0  | 96   | 3.3688          |
| 0.3807        | 97.0  | 97   | 2.3753          |
| 0.1097        | 98.0  | 98   | 2.7441          |
| 0.1848        | 99.0  | 99   | 2.2326          |
| 0.2767        | 100.0 | 100  | 2.3659          |
| 0.1802        | 101.0 | 101  | 1.9263          |
| 0.3092        | 102.0 | 102  | 2.4455          |
| 0.2286        | 103.0 | 103  | 3.0056          |
| 0.2489        | 104.0 | 104  | 2.1951          |
| 0.2924        | 105.0 | 105  | 2.5564          |
| 0.2349        | 106.0 | 106  | 2.9558          |
| 0.421         | 107.0 | 107  | 3.6553          |
| 0.3556        | 108.0 | 108  | 2.4322          |
| 0.256         | 109.0 | 109  | 2.4172          |
| 0.0799        | 110.0 | 110  | 2.0181          |
| 0.2025        | 111.0 | 111  | 3.1769          |
| 0.2607        | 112.0 | 112  | 2.7245          |
| 0.3622        | 113.0 | 113  | 3.2589          |
| 0.1399        | 114.0 | 114  | 3.1256          |
| 0.262         | 115.0 | 115  | 2.5244          |
| 0.1744        | 116.0 | 116  | 2.3620          |
| 0.1238        | 117.0 | 117  | 2.8665          |
| 0.1841        | 118.0 | 118  | 3.4656          |
| 0.1641        | 119.0 | 119  | 2.8976          |
| 0.1843        | 120.0 | 120  | 3.4858          |
| 0.3355        | 121.0 | 121  | 4.1324          |
| 0.1236        | 122.0 | 122  | 3.1793          |
| 0.3187        | 123.0 | 123  | 3.3354          |
| 0.2337        | 124.0 | 124  | 3.2495          |
| 0.1969        | 125.0 | 125  | 3.0704          |
| 0.0901        | 126.0 | 126  | 1.3137          |
| 0.27          | 127.0 | 127  | 3.2272          |
| 0.1994        | 128.0 | 128  | 3.1340          |
| 0.2378        | 129.0 | 129  | 2.9427          |
| 0.1278        | 130.0 | 130  | 1.7681          |
| 0.1155        | 131.0 | 131  | 2.3328          |
| 0.1916        | 132.0 | 132  | 2.3694          |
| 0.2023        | 133.0 | 133  | 2.7201          |
| 0.112         | 134.0 | 134  | 2.8713          |
| 0.0806        | 135.0 | 135  | 2.3181          |
| 0.167         | 136.0 | 136  | 2.2217          |
| 0.0989        | 137.0 | 137  | 2.0763          |
| 0.1576        | 138.0 | 138  | 1.7752          |
| 0.0724        | 139.0 | 139  | 3.3534          |
| 0.0856        | 140.0 | 140  | 1.3508          |
| 0.1947        | 141.0 | 141  | 1.7321          |
| 0.2182        | 142.0 | 142  | 3.1232          |
| 0.1011        | 143.0 | 143  | 1.8502          |
| 0.1927        | 144.0 | 144  | 1.8540          |
| 0.2783        | 145.0 | 145  | 3.4478          |
| 0.1224        | 146.0 | 146  | 2.1718          |
| 0.1484        | 147.0 | 147  | 3.3487          |
| 0.1906        | 148.0 | 148  | 2.1033          |
| 0.1279        | 149.0 | 149  | 2.6356          |
| 0.1071        | 150.0 | 150  | 2.6271          |


### 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-150", "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-150
========================================


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: 2.4908


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: 0.00015
* 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: 150.0
* 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: 0.00015
* 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: 150.0
* 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: 0.00015
* 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: 150.0
* 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: 0.00015
* 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: 150.0
* 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