metadata
base_model:
- google/mt5-small
datasets:
- syubraj/roman2nepali-transliteration
language:
- ne
- en
library_name: transformers
license: apache-2.0
pipeline_tag: translation
tags:
- nepali
- roman english
- translation
- transliteration
Model Card for Model ID
Model Trained for 8500 steps on <110k dataset.
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Model type: (Translation)
- Language(s) (NLP): Nepali, English
- License: [Apache license 2.0]
- Finetuned from model : [google/mt5-small]
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, MT5ForConditionalGeneration
checkpoint = "syubraj/RomanEng2Nep-v2"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = MT5ForConditionalGeneration.from_pretrained(checkpoint)
# Set max sequence length
max_seq_len = 20
def translate(text):
# Tokenize the input text with a max length of 20
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_seq_len)
# Generate translation
translated = model.generate(**inputs)
# Decode the translated tokens back to text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
# Example usage
source_text = "muskuraudai" # Example Romanized Nepali text
translated_text = translate(source_text)
print(f"Translated Text: {translated_text}")
Training Data
syubraj/roman2nepali-transliteration
Training Hyperparameters
- Training regime:
training_args = Seq2SeqTrainingArguments(
output_dir="/content/drive/MyDrive/romaneng2nep_v2/",
eval_strategy="steps",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=8,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=2,
predict_with_generate=True,
)
Training and Validation Metrics
| Step | Training Loss | Validation Loss | Gen Len |
|---|---|---|---|
| 500 | 21.636200 | 9.776628 | 2.001900 |
| 1000 | 10.103400 | 6.105016 | 2.077900 |
| 1500 | 6.830800 | 5.081259 | 3.811600 |
| 2000 | 6.003100 | 4.702793 | 4.237300 |
| 2500 | 5.690200 | 4.469123 | 4.700000 |
| 3000 | 5.443100 | 4.274406 | 4.808300 |
| 3500 | 5.265300 | 4.121417 | 4.749400 |
| 4000 | 5.128500 | 3.989708 | 4.782300 |
| 4500 | 5.007200 | 3.885391 | 4.805100 |
| 5000 | 4.909600 | 3.787640 | 4.874800 |
| 5500 | 4.836000 | 3.715750 | 4.855500 |
| 6000 | 4.733000 | 3.640963 | 4.962000 |
| 6500 | 4.673500 | 3.587330 | 5.011600 |
| 7000 | 4.623800 | 3.531883 | 5.068300 |
| 7500 | 4.567400 | 3.481622 | 5.108500 |
| 8000 | 4.523200 | 3.445404 | 5.092700 |
| 8500 | 4.464000 | 3.413630 | 5.132700 |