MaskedLM is a pre-training technique used in Natural Language Processing (NLP) for deep-learning models like Transformers. It is a variant of language modeling where a portion of the input text is masked, and the model is trained to predict the masked tokens based on the context provided by the unmasked tokens. This model is trained for Masked Language Modeling for Hinglish Data.

Dataset

Hinglish-Top Dataset columns

en_query
cs_query
en_parse
cs_parse
domain

Training

Epoch	Loss
1	0.0465
2	0.0262
3	0.0116
4	0.00385
5	0.0103
6	0.00738
7	0.00892
8	0.00379
9	0.00126
10	0.000684

Inference

python

from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM")

model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM")

fill = pipeline('fill-mask', model=model, tokenizer=tokenizer)

fill(f'please {fill.tokenizer.mask_token} ko cancel kardo')

Citation

Author: @SRDdev

Name: Shreyas Dixit
framework: Pytorch
Year: Jan 2023
Pipeline: fill-mask
Github: https://github.com/SRDdev
LinkedIn: https://www.linkedin.com/in/srddev/