
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pixel-tiny-cont

This model was trained from scratch on the wikipedia + bookcorpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8024

## 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: 0.0006
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 250000

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.7411        | 0.06  | 1000   | 0.9070          |
| 0.7395        | 0.12  | 2000   | 0.9064          |
| 0.7387        | 0.18  | 3000   | 0.9047          |
| 0.7382        | 0.25  | 4000   | 0.9015          |
| 0.7381        | 0.31  | 5000   | 0.9044          |
| 0.7379        | 0.37  | 6000   | 0.9042          |
| 0.7379        | 0.43  | 7000   | 0.9054          |
| 0.7378        | 0.49  | 8000   | 0.9035          |
| 0.7378        | 0.55  | 9000   | 0.9026          |
| 0.7371        | 0.61  | 10000  | 0.9038          |
| 0.7369        | 0.67  | 11000  | 0.9027          |
| 0.7368        | 0.74  | 12000  | 0.9022          |
| 0.7368        | 0.8   | 13000  | 0.8987          |
| 0.7374        | 0.86  | 14000  | 0.9014          |
| 0.7369        | 0.92  | 15000  | 0.9002          |
| 0.7369        | 0.98  | 16000  | 0.9002          |
| 0.7372        | 1.04  | 17000  | 0.9019          |
| 0.737         | 1.1   | 18000  | 0.9001          |
| 0.737         | 1.16  | 19000  | 0.9006          |
| 0.7369        | 1.23  | 20000  | 0.9007          |
| 0.7365        | 1.29  | 21000  | 0.8698          |
| 0.7363        | 1.35  | 22000  | 0.8700          |
| 0.7366        | 1.41  | 23000  | 0.9021          |
| 0.7362        | 1.47  | 24000  | 0.8763          |
| 0.7082        | 1.53  | 25000  | 0.8719          |
| 0.6774        | 1.59  | 26000  | 0.8876          |
| 0.6525        | 1.65  | 27000  | 0.8905          |
| 0.6022        | 1.72  | 28000  | 0.8856          |
| 0.5874        | 1.78  | 29000  | 0.8794          |
| 0.5765        | 1.84  | 30000  | 0.8806          |
| 0.5685        | 1.9   | 31000  | 0.8747          |
| 0.564         | 1.96  | 32000  | 0.8779          |
| 0.5606        | 2.02  | 33000  | 0.8762          |
| 0.5574        | 2.08  | 34000  | 0.8703          |
| 0.5528        | 2.14  | 35000  | 0.8664          |
| 0.5494        | 2.21  | 36000  | 0.8717          |
| 0.5448        | 2.27  | 37000  | 0.8673          |
| 0.5419        | 2.33  | 38000  | 0.8637          |
| 0.5385        | 2.39  | 39000  | 0.8634          |
| 0.536         | 2.45  | 40000  | 0.8661          |
| 0.5336        | 2.51  | 41000  | 0.8631          |
| 0.5316        | 2.57  | 42000  | 0.8606          |
| 0.5297        | 2.63  | 43000  | 0.8589          |
| 0.5305        | 2.7   | 44000  | 0.8570          |
| 0.5262        | 2.76  | 45000  | 0.8559          |
| 0.5247        | 2.82  | 46000  | 0.8634          |
| 0.5235        | 2.88  | 47000  | 0.8606          |
| 0.5227        | 2.94  | 48000  | 0.8610          |
| 0.5206        | 3.0   | 49000  | 0.8610          |
| 0.5194        | 3.06  | 50000  | 0.8611          |
| 0.5183        | 3.12  | 51000  | 0.8579          |
| 0.5175        | 3.19  | 52000  | 0.8598          |
| 0.5163        | 3.25  | 53000  | 0.8521          |
| 0.5156        | 3.31  | 54000  | 0.8550          |
| 0.5148        | 3.37  | 55000  | 0.8504          |
| 0.5139        | 3.43  | 56000  | 0.8530          |
| 0.5133        | 3.49  | 57000  | 0.8589          |
| 0.5126        | 3.55  | 58000  | 0.8561          |
| 0.5119        | 3.62  | 59000  | 0.8574          |
| 0.5127        | 3.68  | 60000  | 0.8624          |
| 0.5105        | 3.74  | 61000  | 0.8522          |
| 0.5099        | 3.8   | 62000  | 0.8550          |
| 0.5094        | 3.86  | 63000  | 0.8537          |
| 0.509         | 3.92  | 64000  | 0.8535          |
| 0.5091        | 3.98  | 65000  | 0.8592          |
| 0.5079        | 4.04  | 66000  | 0.8554          |
| 0.5074        | 4.11  | 67000  | 0.8516          |
| 0.5069        | 4.17  | 68000  | 0.8491          |
| 0.5066        | 4.23  | 69000  | 0.8571          |
| 0.5068        | 4.29  | 70000  | 0.8536          |
| 0.5066        | 4.35  | 71000  | 0.9288          |
| 0.5051        | 4.41  | 72000  | 0.8597          |
| 0.5045        | 4.47  | 73000  | 0.8555          |
| 0.5043        | 4.53  | 74000  | 0.8547          |
| 0.5039        | 4.6   | 75000  | 0.8561          |
| 0.504         | 4.66  | 76000  | 0.8541          |
| 0.5026        | 4.72  | 77000  | 0.8490          |
| 0.5024        | 4.78  | 78000  | 0.8499          |
| 0.5019        | 4.84  | 79000  | 0.8522          |
| 0.5014        | 4.9   | 80000  | 0.8508          |
| 0.5008        | 4.96  | 81000  | 0.8512          |
| 0.5002        | 5.02  | 82000  | 0.8470          |
| 0.4995        | 5.09  | 83000  | 0.8462          |
| 0.4991        | 5.15  | 84000  | 0.8455          |
| 0.4982        | 5.21  | 85000  | 0.8465          |
| 0.4978        | 5.27  | 86000  | 0.8434          |
| 0.4969        | 5.33  | 87000  | 0.8432          |
| 0.4964        | 5.39  | 88000  | 0.8417          |
| 0.4957        | 5.45  | 89000  | 0.8363          |
| 0.495         | 5.51  | 90000  | 0.8392          |
| 0.4946        | 5.58  | 91000  | 0.8401          |
| 0.4935        | 5.64  | 92000  | 0.8373          |
| 0.4929        | 5.7   | 93000  | 0.8401          |
| 0.492         | 5.76  | 94000  | 0.8356          |
| 0.4912        | 5.82  | 95000  | 0.8334          |
| 0.4904        | 5.88  | 96000  | 0.8281          |
| 0.4898        | 5.94  | 97000  | 0.8338          |
| 0.4891        | 6.0   | 98000  | 0.8300          |
| 0.4882        | 6.07  | 99000  | 0.8262          |
| 0.4876        | 6.13  | 100000 | 0.8172          |
| 0.4868        | 6.19  | 101000 | 0.8240          |
| 0.4861        | 6.25  | 102000 | 0.8212          |
| 0.4854        | 6.31  | 103000 | 0.8243          |
| 0.4847        | 6.37  | 104000 | 0.8228          |
| 0.4841        | 6.43  | 105000 | 0.8185          |
| 0.4837        | 6.5   | 106000 | 0.8177          |
| 0.4827        | 6.56  | 107000 | 0.8140          |
| 0.4819        | 6.62  | 108000 | 0.8147          |
| 0.4813        | 6.68  | 109000 | 0.8172          |
| 0.4807        | 6.74  | 110000 | 0.8149          |
| 0.4801        | 6.8   | 111000 | 0.8152          |
| 0.4792        | 6.86  | 112000 | 0.8089          |
| 0.4785        | 6.92  | 113000 | 0.8084          |
| 0.4777        | 6.99  | 114000 | 0.8103          |
| 0.477         | 7.05  | 115000 | 0.8104          |
| 0.4772        | 7.11  | 116000 | 0.8142          |
| 0.4754        | 7.17  | 117000 | 0.8159          |
| 0.4748        | 7.23  | 118000 | 0.8092          |
| 0.4738        | 7.29  | 119000 | 0.8036          |
| 0.473         | 7.35  | 120000 | 0.8085          |
| 0.4724        | 7.41  | 121000 | 0.8084          |
| 0.4714        | 7.48  | 122000 | 0.8066          |
| 0.4705        | 7.54  | 123000 | 0.8094          |
| 0.4699        | 7.6   | 124000 | 0.8095          |
| 0.4693        | 7.66  | 125000 | 0.8101          |
| 0.4685        | 7.72  | 126000 | 0.8092          |
| 0.4679        | 7.78  | 127000 | 0.8025          |
| 0.4672        | 7.84  | 128000 | 0.8000          |
| 0.4665        | 7.9   | 129000 | 0.8020          |
| 0.4659        | 7.97  | 130000 | 0.8022          |
| 0.4653        | 8.03  | 131000 | 0.8071          |
| 0.4647        | 8.09  | 132000 | 0.7994          |
| 0.4639        | 8.15  | 133000 | 0.8034          |
| 0.4634        | 8.21  | 134000 | 0.8022          |
| 0.4656        | 8.27  | 135000 | 0.8052          |
| 0.4623        | 8.33  | 136000 | 0.7989          |
| 0.4617        | 8.39  | 137000 | 0.7993          |
| 0.4612        | 8.46  | 138000 | 0.8003          |
| 0.4608        | 8.52  | 139000 | 0.7990          |
| 0.4603        | 8.58  | 140000 | 0.8074          |
| 0.4597        | 8.64  | 141000 | 0.8089          |
| 0.4591        | 8.7   | 142000 | 0.8040          |
| 0.4586        | 8.76  | 143000 | 0.7993          |
| 0.4584        | 8.82  | 144000 | 0.8004          |
| 0.4594        | 8.88  | 145000 | 0.7991          |
| 0.4574        | 8.95  | 146000 | 0.7956          |
| 0.4571        | 9.01  | 147000 | 0.7948          |
| 0.4565        | 9.07  | 148000 | 0.7982          |
| 0.4563        | 9.13  | 149000 | 0.7960          |
| 0.4555        | 9.19  | 150000 | 0.8043          |
| 0.4551        | 9.25  | 151000 | 0.8021          |
| 0.4549        | 9.31  | 152000 | 0.7972          |
| 0.4545        | 9.38  | 153000 | 0.8003          |
| 0.4542        | 9.44  | 154000 | 0.8000          |
| 0.4539        | 9.5   | 155000 | 0.7960          |
| 0.4533        | 9.56  | 156000 | 0.8035          |
| 0.453         | 9.62  | 157000 | 0.7953          |
| 0.4527        | 9.68  | 158000 | 0.7937          |
| 0.4524        | 9.74  | 159000 | 0.8021          |
| 0.4519        | 9.8   | 160000 | 0.8028          |
| 0.4517        | 9.87  | 161000 | 0.8006          |
| 0.4514        | 9.93  | 162000 | 0.8067          |
| 0.4512        | 9.99  | 163000 | 0.7990          |
| 0.4508        | 10.05 | 164000 | 0.8041          |
| 0.4504        | 10.11 | 165000 | 0.7995          |
| 0.4501        | 10.17 | 166000 | 0.7979          |
| 0.4499        | 10.23 | 167000 | 0.7969          |
| 0.4497        | 10.29 | 168000 | 0.8041          |
| 0.4495        | 10.36 | 169000 | 0.8050          |
| 0.4492        | 10.42 | 170000 | 0.7999          |
| 0.4494        | 10.48 | 171000 | 0.7992          |
| 0.4486        | 10.54 | 172000 | 0.8019          |
| 0.4485        | 10.6  | 173000 | 0.8026          |
| 0.4483        | 10.66 | 174000 | 0.8009          |
| 0.448         | 10.72 | 175000 | 0.8022          |
| 0.4479        | 10.78 | 176000 | 0.8016          |
| 0.4476        | 10.85 | 177000 | 0.7988          |
| 0.4474        | 10.91 | 178000 | 0.8025          |
| 0.4471        | 10.97 | 179000 | 0.8035          |
| 0.4471        | 11.03 | 180000 | 0.7983          |
| 0.4467        | 11.09 | 181000 | 0.8010          |
| 0.4463        | 11.15 | 182000 | 0.8035          |
| 0.4463        | 11.21 | 183000 | 0.8049          |
| 0.4462        | 11.27 | 184000 | 0.7998          |
| 0.4459        | 11.34 | 185000 | 0.7988          |
| 0.4457        | 11.4  | 186000 | 0.8064          |
| 0.4456        | 11.46 | 187000 | 0.8042          |
| 0.4454        | 11.52 | 188000 | 0.7998          |
| 0.4453        | 11.58 | 189000 | 0.8026          |
| 0.4449        | 11.64 | 190000 | 0.7993          |
| 0.4448        | 11.7  | 191000 | 0.8037          |
| 0.4448        | 11.76 | 192000 | 0.8038          |
| 0.4445        | 11.83 | 193000 | 0.8010          |
| 0.4442        | 11.89 | 194000 | 0.7977          |
| 0.4443        | 11.95 | 195000 | 0.8008          |
| 0.4441        | 12.01 | 196000 | 0.8048          |
| 0.4439        | 12.07 | 197000 | 0.8034          |
| 0.4438        | 12.13 | 198000 | 0.8052          |
| 0.4437        | 12.19 | 199000 | 0.8041          |
| 0.4434        | 12.25 | 200000 | 0.8001          |
| 0.4434        | 12.32 | 201000 | 0.8013          |
| 0.4432        | 12.38 | 202000 | 0.7987          |
| 0.443         | 12.44 | 203000 | 0.7962          |
| 0.443         | 12.5  | 204000 | 0.8017          |
| 0.4429        | 12.56 | 205000 | 0.7996          |
| 0.4428        | 12.62 | 206000 | 0.7997          |
| 0.4425        | 12.68 | 207000 | 0.8017          |
| 0.4424        | 12.75 | 208000 | 0.8008          |
| 0.4424        | 12.81 | 209000 | 0.8052          |
| 0.4422        | 12.87 | 210000 | 0.8004          |
| 0.4421        | 12.93 | 211000 | 0.8023          |
| 0.4421        | 12.99 | 212000 | 0.8014          |
| 0.442         | 13.05 | 213000 | 0.7999          |
| 0.4418        | 13.11 | 214000 | 0.8019          |
| 0.4417        | 13.17 | 215000 | 0.7996          |
| 0.4416        | 13.24 | 216000 | 0.8007          |
| 0.4414        | 13.3  | 217000 | 0.8029          |
| 0.4415        | 13.36 | 218000 | 0.7990          |
| 0.4413        | 13.42 | 219000 | 0.7997          |
| 0.4413        | 13.48 | 220000 | 0.7997          |
| 0.4412        | 13.54 | 221000 | 0.7996          |
| 0.4411        | 13.6  | 222000 | 0.8003          |
| 0.4411        | 13.66 | 223000 | 0.7993          |
| 0.4411        | 13.73 | 224000 | 0.8005          |
| 0.4409        | 13.79 | 225000 | 0.8013          |
| 0.4409        | 13.85 | 226000 | 0.8016          |
| 0.4409        | 13.91 | 227000 | 0.7994          |
| 0.4408        | 13.97 | 228000 | 0.8023          |
| 0.4407        | 14.03 | 229000 | 0.8013          |
| 0.4406        | 14.09 | 230000 | 0.8038          |
| 0.4408        | 14.15 | 231000 | 0.7994          |
| 0.4406        | 14.22 | 232000 | 0.8007          |
| 0.4404        | 14.28 | 233000 | 0.8006          |
| 0.4403        | 14.34 | 234000 | 0.7987          |
| 0.4405        | 14.4  | 235000 | 0.8010          |
| 0.4404        | 14.46 | 236000 | 0.7982          |
| 0.4404        | 14.52 | 237000 | 0.7985          |
| 0.4403        | 14.58 | 238000 | 0.8016          |
| 0.4402        | 14.64 | 239000 | 0.8025          |
| 0.4402        | 14.71 | 240000 | 0.8020          |
| 0.4401        | 14.77 | 241000 | 0.8009          |
| 0.4401        | 14.83 | 242000 | 0.8015          |
| 0.4401        | 14.89 | 243000 | 0.8010          |
| 0.44          | 14.95 | 244000 | 0.7996          |
| 0.4402        | 15.01 | 245000 | 0.8014          |
| 0.44          | 15.07 | 246000 | 0.8007          |
| 0.44          | 15.13 | 247000 | 0.7984          |
| 0.44          | 15.2  | 248000 | 0.8009          |
| 0.4399        | 15.26 | 249000 | 0.8006          |
| 0.4399        | 15.32 | 250000 | 0.8016          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1

{"tags": ["masked-auto-encoding", "generated_from_trainer"], "model-index": [{"name": "pixel-tiny-cont", "results": []}]}
transformers pytorch pixel masked-auto-encoding generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #pixel #masked-auto-encoding #generated_from_trainer #endpoints_compatible #region-us 

pixel-tiny-cont
===============


This model was trained from scratch on the wikipedia + bookcorpus dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8024


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: 0.0006
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 1024
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 250000


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.1.1.dev0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0006
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 1024
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 250000 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.1.1.dev0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #pixel #masked-auto-encoding #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0006
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 1024
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 250000 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0
* Datasets 2.1.1.dev0
* Tokenizers 0.12.1