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# RoBERTa-tg

This model is a fine-tuned version of [Tajik-Corpus](https://huggingface.co/datasets/muhtasham/tajik-corpus) dataset which is based on Leipzig Corpora.


## Model description

You can use model for masked text generation or fine-tune it to a downstream task.

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

{"language": ["tg"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u041f\u043e\u0439\u0442\u0430\u0445\u0442\u0438 <mask> \u0414\u0443\u0448\u0430\u043d\u0431\u0435"}, {"text": "<mask> \u0431\u0430 \u0438\u043d \u0441\u0430\u0439\u0442\u0438 \u0448\u0443\u043c\u043e \u043c\u0435\u0434\u0430\u0440\u043e\u044f\u043c."}, {"text": "\u041d\u043e\u043c\u0438 \u043c\u0430\u043d \u0410\u043a\u0440\u0430\u043c <mask>"}], "model-index": [{"name": "RoBERTa-tg", "results": []}]}
transformers pytorch roberta fill-mask generated_from_trainer tg autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #tg #autotrain_compatible #endpoints_compatible #region-us 




# RoBERTa-tg

This model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora.


## Model description

You can use model for masked text generation or fine-tune it to a downstream task.

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

# RoBERTa-tg

This model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora. ## Model description

You can use model for masked text generation or fine-tune it to a downstream task. ## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1 ### Training results ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #tg #autotrain_compatible #endpoints_compatible #region-us 
 # RoBERTa-tg

This model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora. ## Model description

You can use model for masked text generation or fine-tune it to a downstream task. ## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1 ### Training results ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #tg #autotrain_compatible #endpoints_compatible #region-us 
# RoBERTa-tg

This model is a fine-tuned version of Tajik-Corpus dataset which is based on Leipzig Corpora.## Model description

You can use model for masked text generation or fine-tune it to a downstream task.## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1### Training results### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1