metadata
license: afl-3.0
datasets:
- WillHeld/hinglish_top
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: fill-mask
HingMaskedLM
This is a BERT model 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
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/