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# bert_uncased_L-2_H-128_A-2-finetuned-parsed

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2883

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.0   | 59    | 4.6900          |
| No log        | 2.0   | 118   | 4.6347          |
| No log        | 3.0   | 177   | 4.6578          |
| No log        | 4.0   | 236   | 4.5731          |
| No log        | 5.0   | 295   | 4.6258          |
| No log        | 6.0   | 354   | 4.6365          |
| No log        | 7.0   | 413   | 4.7292          |
| No log        | 8.0   | 472   | 4.4789          |
| 4.5634        | 9.0   | 531   | 4.3161          |
| 4.5634        | 10.0  | 590   | 4.6929          |
| 4.5634        | 11.0  | 649   | 4.5543          |
| 4.5634        | 12.0  | 708   | 4.3739          |
| 4.5634        | 13.0  | 767   | 4.6118          |
| 4.5634        | 14.0  | 826   | 4.4036          |
| 4.5634        | 15.0  | 885   | 4.3940          |
| 4.5634        | 16.0  | 944   | 4.5944          |
| 4.0896        | 17.0  | 1003  | 4.3630          |
| 4.0896        | 18.0  | 1062  | 4.0447          |
| 4.0896        | 19.0  | 1121  | 4.3832          |
| 4.0896        | 20.0  | 1180  | 4.0535          |
| 4.0896        | 21.0  | 1239  | 4.5213          |
| 4.0896        | 22.0  | 1298  | 4.5887          |
| 4.0896        | 23.0  | 1357  | 4.5211          |
| 4.0896        | 24.0  | 1416  | 4.1876          |
| 4.0896        | 25.0  | 1475  | 4.5861          |
| 3.9145        | 26.0  | 1534  | 4.3581          |
| 3.9145        | 27.0  | 1593  | 4.6545          |
| 3.9145        | 28.0  | 1652  | 4.4919          |
| 3.9145        | 29.0  | 1711  | 4.1109          |
| 3.9145        | 30.0  | 1770  | 4.2736          |
| 3.9145        | 31.0  | 1829  | 4.6461          |
| 3.9145        | 32.0  | 1888  | 4.3111          |
| 3.9145        | 33.0  | 1947  | 4.2909          |
| 3.8088        | 34.0  | 2006  | 4.1168          |
| 3.8088        | 35.0  | 2065  | 4.2329          |
| 3.8088        | 36.0  | 2124  | 4.5285          |
| 3.8088        | 37.0  | 2183  | 4.4841          |
| 3.8088        | 38.0  | 2242  | 4.2489          |
| 3.8088        | 39.0  | 2301  | 4.2384          |
| 3.8088        | 40.0  | 2360  | 4.3610          |
| 3.8088        | 41.0  | 2419  | 4.2758          |
| 3.8088        | 42.0  | 2478  | 4.2895          |
| 3.7034        | 43.0  | 2537  | 4.2824          |
| 3.7034        | 44.0  | 2596  | 4.4997          |
| 3.7034        | 45.0  | 2655  | 4.5091          |
| 3.7034        | 46.0  | 2714  | 4.0883          |
| 3.7034        | 47.0  | 2773  | 4.2018          |
| 3.7034        | 48.0  | 2832  | 4.3701          |
| 3.7034        | 49.0  | 2891  | 4.0764          |
| 3.7034        | 50.0  | 2950  | 4.6149          |
| 3.6455        | 51.0  | 3009  | 4.3629          |
| 3.6455        | 52.0  | 3068  | 4.2199          |
| 3.6455        | 53.0  | 3127  | 4.3543          |
| 3.6455        | 54.0  | 3186  | 4.7006          |
| 3.6455        | 55.0  | 3245  | 4.1633          |
| 3.6455        | 56.0  | 3304  | 4.5183          |
| 3.6455        | 57.0  | 3363  | 4.1918          |
| 3.6455        | 58.0  | 3422  | 4.4810          |
| 3.6455        | 59.0  | 3481  | 4.1398          |
| 3.5468        | 60.0  | 3540  | 3.9632          |
| 3.5468        | 61.0  | 3599  | 4.4640          |
| 3.5468        | 62.0  | 3658  | 4.0500          |
| 3.5468        | 63.0  | 3717  | 4.3956          |
| 3.5468        | 64.0  | 3776  | 4.3922          |
| 3.5468        | 65.0  | 3835  | 4.2513          |
| 3.5468        | 66.0  | 3894  | 4.4475          |
| 3.5468        | 67.0  | 3953  | 4.3037          |
| 3.4975        | 68.0  | 4012  | 4.1568          |
| 3.4975        | 69.0  | 4071  | 4.2253          |
| 3.4975        | 70.0  | 4130  | 4.1202          |
| 3.4975        | 71.0  | 4189  | 4.4421          |
| 3.4975        | 72.0  | 4248  | 4.3548          |
| 3.4975        | 73.0  | 4307  | 4.1671          |
| 3.4975        | 74.0  | 4366  | 4.4090          |
| 3.4975        | 75.0  | 4425  | 4.1064          |
| 3.4975        | 76.0  | 4484  | 4.2109          |
| 3.44          | 77.0  | 4543  | 4.3244          |
| 3.44          | 78.0  | 4602  | 4.1995          |
| 3.44          | 79.0  | 4661  | 4.4518          |
| 3.44          | 80.0  | 4720  | 4.1991          |
| 3.44          | 81.0  | 4779  | 4.4183          |
| 3.44          | 82.0  | 4838  | 4.2173          |
| 3.44          | 83.0  | 4897  | 4.1721          |
| 3.44          | 84.0  | 4956  | 4.1931          |
| 3.3916        | 85.0  | 5015  | 4.3280          |
| 3.3916        | 86.0  | 5074  | 4.3347          |
| 3.3916        | 87.0  | 5133  | 4.3243          |
| 3.3916        | 88.0  | 5192  | 4.2708          |
| 3.3916        | 89.0  | 5251  | 4.1580          |
| 3.3916        | 90.0  | 5310  | 4.0348          |
| 3.3916        | 91.0  | 5369  | 4.0605          |
| 3.3916        | 92.0  | 5428  | 4.2083          |
| 3.3916        | 93.0  | 5487  | 4.2378          |
| 3.3817        | 94.0  | 5546  | 4.2171          |
| 3.3817        | 95.0  | 5605  | 3.9581          |
| 3.3817        | 96.0  | 5664  | 4.1668          |
| 3.3817        | 97.0  | 5723  | 4.0394          |
| 3.3817        | 98.0  | 5782  | 4.2231          |
| 3.3817        | 99.0  | 5841  | 4.1900          |
| 3.3817        | 100.0 | 5900  | 4.3041          |
| 3.3817        | 101.0 | 5959  | 4.3827          |
| 3.3526        | 102.0 | 6018  | 4.0975          |
| 3.3526        | 103.0 | 6077  | 4.3543          |
| 3.3526        | 104.0 | 6136  | 4.2104          |
| 3.3526        | 105.0 | 6195  | 4.2408          |
| 3.3526        | 106.0 | 6254  | 4.4281          |
| 3.3526        | 107.0 | 6313  | 4.4816          |
| 3.3526        | 108.0 | 6372  | 4.1995          |
| 3.3526        | 109.0 | 6431  | 4.1844          |
| 3.3526        | 110.0 | 6490  | 4.2414          |
| 3.3035        | 111.0 | 6549  | 4.3478          |
| 3.3035        | 112.0 | 6608  | 3.9579          |
| 3.3035        | 113.0 | 6667  | 4.2558          |
| 3.3035        | 114.0 | 6726  | 4.0050          |
| 3.3035        | 115.0 | 6785  | 4.1944          |
| 3.3035        | 116.0 | 6844  | 4.0384          |
| 3.3035        | 117.0 | 6903  | 4.5749          |
| 3.3035        | 118.0 | 6962  | 4.3816          |
| 3.2884        | 119.0 | 7021  | 4.0829          |
| 3.2884        | 120.0 | 7080  | 4.1100          |
| 3.2884        | 121.0 | 7139  | 4.3181          |
| 3.2884        | 122.0 | 7198  | 4.2051          |
| 3.2884        | 123.0 | 7257  | 4.1495          |
| 3.2884        | 124.0 | 7316  | 4.2398          |
| 3.2884        | 125.0 | 7375  | 4.2553          |
| 3.2884        | 126.0 | 7434  | 4.0788          |
| 3.2884        | 127.0 | 7493  | 4.4999          |
| 3.2817        | 128.0 | 7552  | 4.4331          |
| 3.2817        | 129.0 | 7611  | 4.3983          |
| 3.2817        | 130.0 | 7670  | 4.1597          |
| 3.2817        | 131.0 | 7729  | 4.2732          |
| 3.2817        | 132.0 | 7788  | 4.1203          |
| 3.2817        | 133.0 | 7847  | 4.4417          |
| 3.2817        | 134.0 | 7906  | 4.0591          |
| 3.2817        | 135.0 | 7965  | 4.0435          |
| 3.252         | 136.0 | 8024  | 4.0461          |
| 3.252         | 137.0 | 8083  | 4.2521          |
| 3.252         | 138.0 | 8142  | 4.2749          |
| 3.252         | 139.0 | 8201  | 4.1346          |
| 3.252         | 140.0 | 8260  | 4.0411          |
| 3.252         | 141.0 | 8319  | 4.0656          |
| 3.252         | 142.0 | 8378  | 4.3978          |
| 3.252         | 143.0 | 8437  | 4.0533          |
| 3.252         | 144.0 | 8496  | 3.9734          |
| 3.217         | 145.0 | 8555  | 4.2113          |
| 3.217         | 146.0 | 8614  | 4.5480          |
| 3.217         | 147.0 | 8673  | 4.1805          |
| 3.217         | 148.0 | 8732  | 4.2144          |
| 3.217         | 149.0 | 8791  | 4.1457          |
| 3.217         | 150.0 | 8850  | 4.3311          |
| 3.217         | 151.0 | 8909  | 4.1565          |
| 3.217         | 152.0 | 8968  | 4.3584          |
| 3.2183        | 153.0 | 9027  | 4.3837          |
| 3.2183        | 154.0 | 9086  | 4.0912          |
| 3.2183        | 155.0 | 9145  | 4.0785          |
| 3.2183        | 156.0 | 9204  | 4.2501          |
| 3.2183        | 157.0 | 9263  | 4.1515          |
| 3.2183        | 158.0 | 9322  | 4.0559          |
| 3.2183        | 159.0 | 9381  | 3.9969          |
| 3.2183        | 160.0 | 9440  | 4.0528          |
| 3.2183        | 161.0 | 9499  | 3.9618          |
| 3.2109        | 162.0 | 9558  | 4.2596          |
| 3.2109        | 163.0 | 9617  | 4.0760          |
| 3.2109        | 164.0 | 9676  | 4.2589          |
| 3.2109        | 165.0 | 9735  | 4.2227          |
| 3.2109        | 166.0 | 9794  | 4.3354          |
| 3.2109        | 167.0 | 9853  | 4.3471          |
| 3.2109        | 168.0 | 9912  | 4.1578          |
| 3.2109        | 169.0 | 9971  | 4.4163          |
| 3.1868        | 170.0 | 10030 | 4.0754          |
| 3.1868        | 171.0 | 10089 | 4.2543          |
| 3.1868        | 172.0 | 10148 | 3.9498          |
| 3.1868        | 173.0 | 10207 | 4.0863          |
| 3.1868        | 174.0 | 10266 | 4.3090          |
| 3.1868        | 175.0 | 10325 | 4.2731          |
| 3.1868        | 176.0 | 10384 | 4.1997          |
| 3.1868        | 177.0 | 10443 | 4.2273          |
| 3.1905        | 178.0 | 10502 | 4.3560          |
| 3.1905        | 179.0 | 10561 | 4.3330          |
| 3.1905        | 180.0 | 10620 | 4.1770          |
| 3.1905        | 181.0 | 10679 | 3.8779          |
| 3.1905        | 182.0 | 10738 | 4.2199          |
| 3.1905        | 183.0 | 10797 | 4.1409          |
| 3.1905        | 184.0 | 10856 | 4.3601          |
| 3.1905        | 185.0 | 10915 | 4.2380          |
| 3.1905        | 186.0 | 10974 | 4.4688          |
| 3.1774        | 187.0 | 11033 | 4.2305          |
| 3.1774        | 188.0 | 11092 | 3.9129          |
| 3.1774        | 189.0 | 11151 | 4.2889          |
| 3.1774        | 190.0 | 11210 | 3.8790          |
| 3.1774        | 191.0 | 11269 | 4.4458          |
| 3.1774        | 192.0 | 11328 | 4.2899          |
| 3.1774        | 193.0 | 11387 | 4.4378          |
| 3.1774        | 194.0 | 11446 | 4.2316          |
| 3.179         | 195.0 | 11505 | 4.0360          |
| 3.179         | 196.0 | 11564 | 4.1284          |
| 3.179         | 197.0 | 11623 | 4.3879          |
| 3.179         | 198.0 | 11682 | 4.0715          |
| 3.179         | 199.0 | 11741 | 4.1888          |
| 3.179         | 200.0 | 11800 | 4.3268          |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bert_uncased_L-2_H-128_A-2-finetuned-parsed", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer base_model:google/bert_uncased_L-2_H-128_A-2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_uncased\_L-2\_H-128\_A-2-finetuned-parsed
===============================================


This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 4.2883


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1