# Kaz-RoBERTa (base-sized model) 

## Model description



## Usage

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='kz-transformers/kaz-roberta-conversational')
>>> unmasker("Hello I'm a <mask> model.")



Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("kz-transformers/kaz-roberta-conversational")
model = AutoModelForMaskedLM.from_pretrained("kz-transformers/kaz-roberta-conversational")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)
```

### BibTeX entry and citation info
{"language": ["kk"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["kz-transformers/multidomain-kazakh-dataset"], "pipeline_tag": "fill-mask"}
transformers pytorch safetensors fill-mask kk dataset:kz-transformers/multidomain-kazakh-dataset license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #fill-mask #kk #dataset-kz-transformers/multidomain-kazakh-dataset #license-apache-2.0 #endpoints_compatible #has_space #region-us 

# Kaz-RoBERTa (base-sized model) 

## Model description



## Usage

You can use this model directly with a pipeline for masked language modeling:

python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("kz-transformers/kaz-roberta-conversational")
model = AutoModelForMaskedLM.from_pretrained("kz-transformers/kaz-roberta-conversational")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(encoded_input)
'''

### BibTeX entry and citation info
# Kaz-RoBERTa (base-sized model) ## Model description ## Usage

You can use this model directly with a pipeline for masked language modeling:

python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("kz-transformers/kaz-roberta-conversational")
model = AutoModelForMaskedLM.from_pretrained("kz-transformers/kaz-roberta-conversational") # prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt') # forward pass
output = model(encoded_input)
''' ### BibTeX entry and citation info
TAGS
#transformers #pytorch #safetensors #fill-mask #kk #dataset-kz-transformers/multidomain-kazakh-dataset #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # Kaz-RoBERTa (base-sized model) ## Model description ## Usage

You can use this model directly with a pipeline for masked language modeling:

python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("kz-transformers/kaz-roberta-conversational")
model = AutoModelForMaskedLM.from_pretrained("kz-transformers/kaz-roberta-conversational") # prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt') # forward pass
output = model(encoded_input)
''' ### BibTeX entry and citation info
TAGS
#transformers #pytorch #safetensors #fill-mask #kk #dataset-kz-transformers/multidomain-kazakh-dataset #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# Kaz-RoBERTa (base-sized model)## Model description## Usage

You can use this model directly with a pipeline for masked language modeling:

python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("kz-transformers/kaz-roberta-conversational")
model = AutoModelForMaskedLM.from_pretrained("kz-transformers/kaz-roberta-conversational")# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')# forward pass
output = model(encoded_input)
'''### BibTeX entry and citation info