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# legal-lithuanian-roberta-base

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5928

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 200000

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.013         | 23.01 | 50000  | 0.7246          |
| 0.8993        | 47.01 | 100000 | 0.6356          |
| 0.8031        | 71.01 | 150000 | 0.5993          |
| 0.7947        | 95.01 | 200000 | 0.5928          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.12.0

{"tags": ["generated_from_trainer"], "model-index": [{"name": "legal-lithuanian-roberta-base", "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 

legal-lithuanian-roberta-base
=============================


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


* Loss: 0.5928


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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 200000


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.12.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 200000 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.12.0
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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 200000 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.12.0
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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 200000### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu102
* Datasets 2.9.0
* Tokenizers 0.12.0