# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("SRDdev/MaskedLM")
model = AutoModelForMaskedLM.from_pretrained("SRDdev/MaskedLM")Quick Links
SRDberta
This is a BERT model trained for Masked Language Modeling for English Data.
Dataset
Hinglish-Top Dataset columns
- en_query
- cs_query
- en_parse
- cs_parse
- domain
Training
| Epoch | Loss |
|---|---|
| 1 | 0.0485 |
| 2 | 0.00837 |
| 3 | 0.00812 |
| 4 | 0.0029 |
| 5 | 0.014 |
| 6 | 0.00748 |
| 7 | 0.0041 |
| 8 | 0.00543 |
| 9 | 0.00304 |
| 10 | 0.000574 |
Inference
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("SRDdev/SRDBerta")
model = AutoModelForMaskedLM.from_pretrained("SRDdev/SRDBerta")
fill = pipeline('fill-mask', model='SRDberta', tokenizer='SRDberta')
fill_mask = fill.tokenizer.mask_token
fill(f'Aap {fill_mask} ho?')
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/
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/MaskedLM")