You can deploy this model and start earning money today!

sachin18449/
hindi-english-unigram-nmt

Name: hindi-english-unigram-nmt
Availability: InStock

sachin18449/hindi-english-unigram-nmt

1706.03762

apache-2.0

endpoints_compatible

Views

No views yet

Model Card for English-to-Hindi Unigram NMT

This model is a Transformer-based Neural Machine Translation (NMT) model trained to translate English sentences into Hindi using Unigram tokenization.
It was trained from scratch using Hugging Face Transformers.

Model Details

Model Description

This sequence-to-sequence model uses a standard Encoder–Decoder Transformer architecture for English→Hindi translation.
It is trained with Unigram tokenization instead of BPE to improve rare word handling and morphological coverage.

Developed by: Sachin Kumar
Funded by: Self / Personal Research
Shared by: Sachin Kumar
Model type: Transformer Encoder–Decoder (seq2seq)
Language(s) (NLP): English → Hindi
License: Apache-2.0
Finetuned from model: Trained from scratch

Model Sources

Repository: Hugging Face Model Page
Paper (architecture): Attention Is All You Need
Dataset: sachin18449/hindi-english-unigram-nmt

Uses

Direct Use

Translating English text into Hindi.
Integrating into multilingual apps, chatbots, and translation services.
Generating Hindi content from English sources.

Downstream Use

Fine-tuning for domain-specific translation (medical, legal, technical).
Multi-task setups (e.g., summarization + translation pipelines).

Out-of-Scope Use

Not designed for Hindi→English translation.
Not suitable for audio inputs (speech-to-text).
Should not be used for high-stakes decisions without human review.

Bias, Risks, and Limitations

May reflect gender or cultural biases in training data.
Rare words, idioms, or domain-specific jargon may be mistranslated.
Limited ability to handle code-mixed or informal text.

Recommendations

Post-edit translations for professional use.
Fine-tune on domain-specific data for critical applications.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "sachin18449/hindi-english-unigram-nmt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = "How are you?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))