chuuhtetnaing / myanmar-ner-model

Myanmar ner Tagging Model

Fine-tuned myanmar-pos-model for Myanmar NER tagging.

Training Results

Test Set Evaluation

Evaluated on myanmar-ner-dataset test split using seqeval metrics:

Training Details

Usage

from transformers import pipeline

ner = pipeline("token-classification", model="chuuhtetnaing/myanmar-ner-model", grouped_entities=True)
result = ner("ကိုမောင်သည်ရန်ကုန်မြို့သို့သွားသည်။")  # Ko Maung went to Yangon city
print(result)

Evaluation Code

!pip install seqeval

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
from datasets import load_dataset
from tqdm import tqdm
from seqeval.metrics import classification_report

# Load model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("chuuhtetnaing/myanmar-ner-model")
tokenizer = AutoTokenizer.from_pretrained("chuuhtetnaing/myanmar-ner-model")

def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
    labels = []
    for i, label in enumerate(examples["ner_tags"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)
    tokenized_inputs["labels"] = labels
    return tokenized_inputs

# Load and tokenize dataset
ner = pipeline("token-classification", model="chuuhtetnaing/myanmar-ner-model", aggregation_strategy=None)
ds = load_dataset("chuuhtetnaing/myanmar-ner-dataset")
tokenized_ds = ds.map(tokenize_and_align_labels, batched=True)
test_ds = tokenized_ds["test"]

# Get label mapping
label_list = model.config.id2label

y_true = []
y_pred = []

for example in tqdm(test_ds):
    tokens = tokenizer.convert_ids_to_tokens(example["input_ids"])
    true_labels = [label_list[l] if l != -100 else "O" for l in example["labels"]]
    
    text = tokenizer.decode(example["input_ids"], skip_special_tokens=True)
    preds = ner(text)
    
    pred_labels = ["O"] * len(true_labels)
    for pred in preds:
        idx = pred["index"]
        if idx < len(pred_labels):
            pred_labels[idx] = pred["entity"]
    
    y_true.append([label_list[l] for l in example["labels"] if l != -100])
    y_pred.append([p for p, l in zip(pred_labels, example["labels"]) if l != -100])

print(classification_report(y_true, y_pred))

NER Labels