metadata
language:
- ne
- en
tags:
- translation
- fairseq
- fsmt
license: apache-2.0
Nepali-Tamang-English Machine Translation Model
This model was converted from a Fairseq checkpoint to Hugging Face Transformers format. It performs machine translation between Nepali, Tamang, and English.
Model Details
- Architecture: FSMT (Fairseq Machine Translation)
- Original Framework: Fairseq
- Languages: Nepali (ne), Tamang, English (en)
Usage
You can use this model directly with the Hugging Face transformers library.
Installation
pip install transformers
Python Example
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "rishi70612/nepali-tamang-english-mt"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input_text = "Hugging Face is a technology company based in New York and Paris." # Replace with your source text
input_ids = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded)
Note on Languages
Since this is a custom model, ensure you handle input text preprocessing (like language tags) if required by your specific training setup. The tokenizer is configured with BPE.