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# roberta-tiny-4l-10M

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3432
- Accuracy: 0.0513

## 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: 0.0007
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 7.4785        | 4.16  | 50   | 7.3834          | 0.0514   |
| 7.425         | 8.33  | 100  | 7.3559          | 0.0514   |
| 7.4187        | 12.49 | 150  | 7.3517          | 0.0512   |
| 7.4204        | 16.66 | 200  | 7.3440          | 0.0514   |
| 7.4099        | 20.82 | 250  | 7.3454          | 0.0515   |
| 7.2916        | 24.99 | 300  | 7.3442          | 0.0515   |
| 7.4117        | 29.16 | 350  | 7.3440          | 0.0513   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-tiny-4l-10M", "results": []}]}
transformers pytorch tensorboard roberta fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

roberta-tiny-4l-10M
===================


This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 7.3432
* Accuracy: 0.0513


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: 0.0007
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0007
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0007
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0007
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1