# random-roberta-tiny

We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.

It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.

A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.

The code to obtain such random model:

```python

from transformers import RobertaConfig, RobertaModel

    
def get_custom_blank_roberta(h=768, l=12):

    # Initializing a RoBERTa configuration
    configuration = RobertaConfig(num_attention_heads=h, num_hidden_layers=l)

    # Initializing a model from the configuration
    model = RobertaModel(configuration)

    return model

rank="tiny"
h=128
l=2
model_type = "roberta"
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model_name ="random-"+model_type+"-"+rank
model = get_custom_blank_roberta(h, l)
```

{}
transformers pytorch tf roberta feature-extraction endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us 

# random-roberta-tiny

We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.

It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.

A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.

The code to obtain such random model:



# random-roberta-tiny

We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.

It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.

A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.

The code to obtain such random model:
TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us 
 # random-roberta-tiny

We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.

It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.

A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.

The code to obtain such random model:
passage: TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us 
# random-roberta-tiny

We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.

It's important to note that tokenizer of random-roberta-tiny is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.

A debatable advantage of pulling random-roberta-tiny from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.

The code to obtain such random model: