khasibert

57
1 language
license:cc-by-nc-4.0
by
MWirelabs
Language Model
OTHER
New
57 downloads
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Quick Summary

KhasiBERT is a foundational language model for the Khasi language, trained on 3.

Code Examples

Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)
Usagepythontransformers
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, pipeline

# Load model and tokenizer
model = RobertaForMaskedLM.from_pretrained('MWirelabs/khasibert')
tokenizer = RobertaTokenizerFast.from_pretrained('MWirelabs/khasibert')

# Create fill-mask pipeline
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

# Example usage
text = 'Ka Meghalaya ka <mask> ha ka jingpyrkhat jong ki Khasi.'
results = fill_mask(text)
print(results)

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