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# roberta-tiny-8l-10M

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3389
- Accuracy: 0.0516

## 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: 0.0004
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 7.8102        | 1.04  | 50   | 7.3747          | 0.0514   |
| 7.805         | 2.08  | 100  | 7.3699          | 0.0517   |
| 7.7907        | 3.12  | 150  | 7.3595          | 0.0517   |
| 7.7838        | 4.16  | 200  | 7.3617          | 0.0514   |
| 7.7706        | 5.21  | 250  | 7.3586          | 0.0514   |
| 7.2933        | 6.25  | 300  | 7.3566          | 0.0513   |
| 7.2932        | 7.29  | 350  | 7.3527          | 0.0516   |
| 7.2986        | 8.33  | 400  | 7.3561          | 0.0516   |
| 7.289         | 9.37  | 450  | 7.3495          | 0.0515   |
| 7.2879        | 10.41 | 500  | 7.3455          | 0.0514   |
| 7.276         | 11.45 | 550  | 7.3477          | 0.0513   |
| 7.3072        | 12.49 | 600  | 7.3446          | 0.0516   |
| 7.2978        | 13.53 | 650  | 7.3463          | 0.0514   |
| 7.2857        | 14.58 | 700  | 7.3426          | 0.0515   |
| 7.2868        | 15.62 | 750  | 7.3438          | 0.0515   |
| 7.2973        | 16.66 | 800  | 7.3442          | 0.0517   |
| 7.2988        | 17.7  | 850  | 7.3437          | 0.0512   |


### 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-8l-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-8l-10M
===================


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


* Loss: 7.3389
* Accuracy: 0.0516


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: 0.0004
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 512
* 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: 0.0004
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 512
* 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: 0.0004
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 512
* 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: 0.0004
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 512
* 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