Instructions to use musfiqdehan/bengali-english-word-aligner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musfiqdehan/bengali-english-word-aligner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="musfiqdehan/bengali-english-word-aligner")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("musfiqdehan/bengali-english-word-aligner") model = AutoModelForMaskedLM.from_pretrained("musfiqdehan/bengali-english-word-aligner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Bengali to English Word Aligner
Finetuned Model for Bengali to English Word which was build on bert-base-multilingual-cased
Quick Start
Initialize to use it in your project
tokenizer = AutoTokenizer.from_pretrained("musfiqdehan/bengali-english-word-aligner")
model = AutoModel.from_pretrained("musfiqdehan/bengali-english-word-aligner")
Bengali-English Word Alignment
Install Dependencies
!pip install -U data-preprocessors
!pip install -U bangla-postagger
Import Necessary Libraries
from pprint import pprint
from data_preprocessors import text_preprocessor as tp
from bangla_postagger import (en_postaggers as ep,
bn_en_mapper as bem,
translators as trans)
Testing Word Mapping and Alignment
src = "আমি ভাত খাই না, রুটি খাই।"
tgt = "I do not eat rice, I eat bread."
# Give one space before and after punctuation
# for easy tokenization
src = tp.space_punc(src)
tgt = tp.space_punc(tgt)
print("Word Mapping:")
mapping = bem.get_word_mapping(
source=src, target=tgt, model_path="musfiqdehan/bengali-english-word-aligner")
pprint(mapping)
Output
Word Mapping:
['bn:(আমি) -> en:(I)',
'bn:(ভাত) -> en:(rice)',
'bn:(খাই) -> en:(do)',
'bn:(খাই) -> en:(eat)',
'bn:(না) -> en:(not)',
'bn:(,) -> en:(,)',
'bn:(রুটি) -> en:(bread)',
'bn:(খাই) -> en:(eat)',
'bn:(।) -> en:(.)']
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