Vietnamese is a beautiful language, but the absence of accent marks can make it challenging to read and understand. Today, we’ll explore how to use a Transformer model specifically designed to insert these vital diacritics into Vietnamese texts.

Understanding the Model

The task of inserting accent marks in Vietnamese can be thought of as a game of transformation. Imagine you are an artist with a canvas, where each token of the text is a brushstroke. The model’s job is to enhance these strokes with the perfect color – in our case, the correct accents!

Model training has been approached as a token classification problem. For each piece of input, the model assigns a tag that transforms plain text into beautifully accented words. This model is fine-tuned from the XLM-Roberta Large model, known for its robustness.

How to Use This Model

Implementing this model is straightforward, consisting of three key steps:

• Step 1: Load the model as a token classification model.
• Step 2: Run your input through the model to obtain the tag index for each input token.
• Step 3: Utilize the tag index to retrieve actual tags and convert each token into its accented version.

Step 1: Load the Model

Before diving into the code, ensure you have the required packages installed: transformers, torch, and numpy.

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
import numpy as np

def load_trained_transformer_model():
    model_path = 'peterhung/vietnamese-accent-marker-xlm-roberta'
    tokenizer = AutoTokenizer.from_pretrained(model_path, add_prefix_space=True)
    model = AutoModelForTokenClassification.from_pretrained(model_path)
    return model, tokenizer

model, tokenizer = load_trained_transformer_model()

Step 2: Run Input Text Through the Model

Next, we’ll feed in our text. This step can be likened to putting our color palette to work on the canvas.

# Use GPU if available
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)

# Set to evaluation mode
model.eval()

def insert_accents(text, model, tokenizer):
    our_tokens = text.strip().split()
    inputs = tokenizer(our_tokens, is_split_into_words=True, 
                       truncation=True, padding=True, return_tensors='pt')
    input_ids = inputs['input_ids']
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    tokens = tokens[1:-1]  
    with torch.no_grad():
        inputs.to(device)
        outputs = model(**inputs)
    predictions = outputs['logits'].cpu().numpy()
    predictions = np.argmax(predictions, axis=2)
    predictions = predictions[0][1:-1]  
    assert len(tokens) == len(predictions)
    return tokens, predictions

text = "Nhin nhung mua thu di, em nghe sau len trong nang."
tokens, predictions = insert_accents(text, model, tokenizer)

Step 3: Obtain the Accented Words

We will now download the tags set file and process the obtained tokens and predictions.

def _load_tags_set(fpath):
    labels = []
    with open(fpath, 'r') as f:
        for line in f:
            line = line.strip()
            if line:
                labels.append(line)
    return labels

label_list = _load_tags_set('selected_tags_names.txt')
assert len(label_list) == 528, f"Expect len(label_list) tags"

print(tokens)
print(list(label_list[pred] for pred in predictions))

# Merge and get accented words
TOKENIZER_WORD_PREFIX = '▁'

def merge_tokens_and_preds(tokens, predictions):
    merged_tokens_preds = []
    i = 0
    while i < len(tokens):
        tok = tokens[i]
        label_indexes = set([predictions[i]])
        if tok.startswith(TOKENIZER_WORD_PREFIX): 
            tok_no_prefix = tok[len(TOKENIZER_WORD_PREFIX):]
            cur_word_toks = [tok_no_prefix]
            j = i + 1
            while j < len(tokens):
                if not tokens[j].startswith(TOKENIZER_WORD_PREFIX):
                    cur_word_toks.append(tokens[j])
                    label_indexes.add(predictions[j])
                    j += 1
                else:
                    break
            cur_word = ''.join(cur_word_toks)
            merged_tokens_preds.append((cur_word, label_indexes))
            i = j
        else:
            merged_tokens_preds.append((tok, label_indexes))
            i += 1
    return merged_tokens_preds

merged_tokens_preds = merge_tokens_and_preds(tokens, predictions)
accedented_words = get_accented_words(merged_tokens_preds, label_list)
print(accented_words)

Troubleshooting Tips

If you encounter any issues while implementing this model, here are some tips to help you out:

• Ensure all necessary libraries are installed and updated to their latest versions.
• Confirm that your input text is correctly formatted; sometimes unseen characters can cause hiccups.
• If the model does not run as expected, check your GPU/CPU settings.
• For any persistent issues, consider exploring community forums or reaching out for help.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these steps, you’ll be able to insert Vietnamese accent marks efficiently using a powerful Transformer model. This development is crucial for clearer communication in Vietnamese text processing.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.